[go: up one dir, main page]

CN119721905B - Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm - Google Patents

Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm Download PDF

Info

Publication number
CN119721905B
CN119721905B CN202510205716.3A CN202510205716A CN119721905B CN 119721905 B CN119721905 B CN 119721905B CN 202510205716 A CN202510205716 A CN 202510205716A CN 119721905 B CN119721905 B CN 119721905B
Authority
CN
China
Prior art keywords
spider
bee
population
individual
temperature
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202510205716.3A
Other languages
Chinese (zh)
Other versions
CN119721905A (en
Inventor
青敏
倪志伟
刘文涛
刘金龙
冯雅茹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN202510205716.3A priority Critical patent/CN119721905B/en
Publication of CN119721905A publication Critical patent/CN119721905A/en
Application granted granted Critical
Publication of CN119721905B publication Critical patent/CN119721905B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Landscapes

  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm, which is applied to the technical field of multi-temperature co-distribution in the field of logistics and supply chain management and comprises the steps of obtaining vehicle resources and client demand and position information of a distribution center, establishing a refrigeration cost calculation function based on the change of ambient temperature, establishing a multi-temperature co-distribution path planning model based on flexible compartments and ambient temperature and taking the minimum distribution total cost as a target, establishing constraint conditions of the multi-temperature co-distribution path planning model based on the vehicle resources and client information, and carrying out target solution of the model by utilizing the enhanced multi-strategy spider bee algorithm combined with chaotic mapping, self-adaptive elite strategy and reciprocating population reduction strategy to obtain optimal departure time and distribution paths. The invention effectively improves the energy efficiency utilization rate in the cold chain transportation process, and improves the full load rate of vehicles while minimizing the number of vehicles used.

Description

Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm
Technical Field
The invention relates to the technical field of multi-temperature co-distribution in the field of logistics and supply chain management, in particular to a multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm.
Background
Along with the acceleration of urban process and the acceleration of life rhythm, the demand of consumers for convenient food promotes the rapid development of the prefabricated vegetable industry. The cold chain stream serves as a key support and while guaranteeing food quality, there is a need to optimize the path and reduce costs in multi-temperature zone distribution. However, the conventional path optimization method has limitations in terms of processing time window, order splitting and temperature control complexity, and is difficult to meet the high-efficiency requirement of multi-temperature co-distribution.
The flexible compartment technology provides greater flexibility for multi-temperature co-dispensing, and the loading rate and the dispensing efficiency are improved by dynamically adjusting the size of the compartment. However, existing research focuses on specific cargo types, and does not adequately account for the varying temperature requirements and environmental changes in the distribution of prepared dishes. Optimizing the departure time of a vehicle is also an important strategy for improving the distribution efficiency, but most researches assume that the departure time is fixed, and under the consideration of the environmental temperature, the influence of long-time high-temperature exposure on refrigeration energy consumption is ignored, so that the optimization effect is limited.
Therefore, how to provide a multi-temperature co-distribution path optimization model capable of comprehensively considering the environmental temperature, the flexible compartment, the time window and the load factor and combining the flexible compartment and the departure time optimization, and design an improved multi-strategy spider bee optimization algorithm, and through chaotic mapping, self-adaptive variation and population reduction strategies, the quality and convergence speed of solutions are improved, so that the energy efficiency utilization rate in the cold chain transportation process is improved, and the multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm for improving the full load rate of vehicles while minimizing the number of vehicles is a problem to be solved by the person skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a multi-temperature co-allocation path planning method based on an enhanced multi-strategy spider bee algorithm. Firstly, acquiring vehicle resources and customer demand and position information of a distribution center, secondly, calculating the ambient temperature at the time t, establishing a refrigeration cost calculation function based on the change of the ambient temperature, finally, under the limitation of the vehicle resources of the distribution center, constructing a multi-temperature co-distribution optimization model for minimizing the total distribution cost, and carrying out target solution of a multi-temperature co-distribution path planning model by utilizing an enhanced multi-strategy spider bee algorithm combined with chaotic mapping, a self-adaptive elite strategy and a reciprocating population reduction strategy to obtain the optimal departure time and the optimal distribution path. According to the invention, by combining dynamic changes of flexible compartments and ambient temperature, the departure time of the vehicle is optimized in a self-adaptive manner, the energy efficiency utilization rate in the cold chain transportation process is improved, the number of vehicles used is minimized, the full load rate of the vehicles is improved, and a more efficient energy-saving consumption-reducing and quality-guaranteeing scheme is provided for enterprises.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm comprises the following steps:
Step1, acquiring vehicle resources, customer demands and position information of a distribution center, and establishing a refrigeration cost calculation function based on the change of the environmental temperature;
Step 2, constructing a multi-temperature co-distribution path planning model based on flexible compartments and environmental temperature and taking the minimum distribution total cost as a target based on a refrigeration cost calculation function, and constructing constraint conditions of the multi-temperature co-distribution path planning model based on vehicle resources and customer information;
and 3, carrying out target solution of the multi-temperature co-distribution path planning model by utilizing an enhanced multi-strategy spider bee algorithm combined with the chaotic mapping, the self-adaptive elite strategy and the reciprocating population reduction strategy to obtain the optimal departure time and distribution path.
Optionally, in step 1, a refrigeration cost calculation function is established based on the change of the ambient temperature, specifically:
dividing the environment temperature according to the hours, taking each hour as a time zone, and calculating the environment temperature of each time zone as follows:
Wherein, M represents the number of time to which the time zone belongs; represents the maximum temperature in a day; Representing the lowest temperature during the day; Representing hourly simulations A respective air temperature value; Is a fixed parameter;
Assuming that the temperature per hour is constant, the vehicle is considered to pass through one or more time zones, and the vehicle k is assumed to start from point i, with the starting time expressed as The next node accessed is denoted as j, the time of travel from point i to point jThe service time at point j is expressed asThe heat transfer areas of the refrigerated and frozen compartments are as follows:
Wherein, The method comprises the steps of representing the distance from a node i to a node j, v representing the speed of a vehicle k, N representing a node set consisting of a client point set and a distribution center, i, j epsilon N, i=0, representing the distribution center, p representing p-type carriages of the distribution vehicle, p=1 representing a refrigeration type, and p=2 representing a refrigeration type; Representing the refrigerating cargo demand of the j node; z represents the total heat conduction area of the carriage; representing the frozen goods demand of the j node;
Time of day The belonging time zone m is expressed as, wherein,、Respectively the departure timeA start time point and an end time point corresponding to the belonging time zone m,Also indicates the starting time point of the time zone m+1, and since the ending time point of the time zone m coincides with the starting time point of the time zone m+1, the ending time point corresponding to the belonging time zone m is indicated by the starting time point of the time zone m+1The travel and service time is expressed asJudgingWhether or not to establish;
If true, then from From moment to point j, the vehicles are all at temperatureRunning down, neglecting the effect of opening and closing the door during service, the refrigeration cost during this period is expressed as:
Wherein, A temperature representing time zone m; representing the thermal conductivity of the cabin; Indicating the temperature of the refrigerator compartment; indicating the temperature of the refrigerated compartment; k represents a collection of delivery vehicles; representing refrigeration cost; representing the heat generated by the refrigerator during the delivery of vehicle k from node i to node j; E represents the heat generated by the unit gasoline;
If not, then in the time period The vehicle is at temperatureDriving downwards for a period of timeExpressed as:
Updating ,Re-judgingWhether or not to be established to obtain refrigeration cost.
Optionally, in step 2, a multi-temperature co-distribution path planning model based on the flexible compartment and the ambient temperature is constructed with the objective of minimizing the total distribution cost based on the refrigeration cost calculation function, as follows:
Wherein, K represents a collection of delivery vehicles; a fixed resource consumption indicating the start of delivery vehicle k; Indicating whether the vehicle is starting from the distribution center; N represents a node set formed by the client point set and the distribution center, and when i, j epsilon N and i=0, the distribution center is represented; indicating whether the delivery vehicle k travels from the i-node to the next node j; Representing the capacity of the delivery vehicle; representing the number of initial loads of the delivery vehicle k when it starts from the delivery center; Indicating the fuel consumption rate of the delivery vehicle when fully loaded; indicating the fuel consumption rate of the delivery vehicle when the delivery vehicle is empty; Representing the distance from node i to node j; representing a unit fuel supply level; p represents p-class compartments of the delivery vehicle, p=1 represents refrigeration class, and p=2 represents refrigeration class; the cargo loss rate of the p-type cargo is represented, L represents unit number cargo unit price; representing the time required to travel from node i to node j; representing the service time of node j; representing the p-type cargo demand of the j node; representing the cabin of a vehicle k Whether the p-class commodity requirement of the customer i is met; representing a time window penalty cost; a time window representing a client point i, wherein, Indicating the earliest arrival time at which node i requires the delivery vehicle to arrive,Representing the latest arrival time at which node i requires the delivery vehicle to arrive; Representing the arrival time of delivery vehicle k to node i; 、 penalty coefficients representing early and late arrival of the vehicle, respectively; Indicating refrigeration costs.
Optionally, in step 2, constraint conditions of the multi-temperature co-configured path planning model are constructed based on vehicle resources and customer information, as follows:
wherein, N represents a node set composed of a client point set and a distribution center, and i, j epsilon N, i=0 represents the distribution center; Indicating whether to arrive at the j node from the distribution center; indicating whether to return to the distribution center from a certain i node; K represents a distribution vehicle set; indicating whether the delivery vehicle k travels from the i-node to the next node j; P-class cars representing vehicles k satisfy p-class commodity demands of customers j, p representing p-class cars of delivery vehicles, p=1 representing refrigeration classes, and p=2 representing refrigeration classes; Indicating whether the vehicle k is from node j to node i; representing the p-type cargo demand of the i node; Representing the capacity of the delivery vehicle; indicating the time when delivery vehicle k starts from point j; Indicating the time when the delivery vehicle k starts from point i; representing the time required to travel from node i to node j; S represents a set of path vertices; The departure time of the delivery vehicle k from the delivery center is shown; A delivery time window representing a delivery center is provided, Indicating the earliest time that the delivery center requires delivery of vehicle k; m represents the number of clients; indicating whether the delivery vehicle k travels from the i-node to the next node j; Indicating whether the p-class cars of delivery vehicle k meet the p-class merchandise demand of customer i; representing the time from the last customer point of the path; indicating the latest return to the distribution center time.
Optionally, in step 3, the objective solution of the multi-temperature co-distribution path planning model is performed by using an enhanced multi-strategy spider bee algorithm combined with a chaotic mapping, a self-adaptive elite strategy and a reciprocal population reduction strategy, which specifically includes:
initializing parameters including population size G and minimum population size Upper bound ub, lower bound nb, number of iterationsThe method comprises the steps of weighing rate TR, crossing rate Cr, current optimal individual variation frequency p_m and global optimal individual variation frequency g_m, defining and initializing the current iteration frequency to be T=0;
Generating an initial spider bee population by adopting tent chaotic mapping initialization strategy, coding and decoding the generated population, and calculating an fitness value set of the initial population by utilizing a multi-temperature co-distribution path planning model From a set of fitness values of the initial populationSelecting the minimum fitness valueThe corresponding spider bee individuals are marked as the initial population optimal spider bee individualsAnd globally optimal spider bee individualsThe corresponding fitness value is a global optimal fitness value;
Entering into main circulation to generate random numberAs an index of the search agent,If (if)Executing hunting behavior update spider bee population, otherwise executing mating behavior update spider bee population, when executing hunting behavior update spider bee population, makingWhen (when)At this time, a random number p is generated,When (when)When using search behavior to update individuals in a spider bee population, whenWhen the following and escaping actions are adopted to update the individuals in the spider bee population, whenWhen the method is used, individuals in the spider bee population are updated by nesting behaviors, and random numbers are generated、,When (when)When using the current optimal solutionUpdating individuals in a spider bee population whenRandomly selecting a spider bee in the population and updating individuals in the spider bee population using additional steps when performing mating behavior updating the spider bee population, randomly selecting individual spider bees、、Generating parameters、By means of parameters、Generating new spider bee individuals, generating random numbers rand for each newly generated spider bee individual in the population, updating the individual in the population to be the new spider bee individual when rand < Cr, otherwise, not updating;
Coding and decoding the new spider bee individuals obtained currently, and calculating the fitness value set of all individuals in the spider bee population according to the multi-temperature co-distribution path planning model Respectively compare in the current setThe adaptation degree value of the corresponding individual in the updated population is equal to the adaptation degree value of the corresponding individual in the prior spider bee population, if the current adaptation degree value is better, the corresponding individual in the updated population is the current spider bee individual, otherwise, the original individual is reserved, and the spider bee population is updated by comparing in turnCoding and decoding the new spider bee individuals obtained currently, calculating the fitness value set of all individuals in the updated spider bee population, and optimizing the fitness valueAnd global optimum fitness valueComparing, if the current fitness value is better, updating the optimal spider bee individualsAnd an optimal fitness valueThe method comprises the steps of obtaining a current spider bee body and a corresponding fitness value;
let the current iteration times Performing elite mutation p_m times on each individual in the current spider bee population by using an adaptive elite mutation strategy, checking whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space by boundary, and correcting the new spider bee position to the boundary if the new spider bee position exceeds the upper and lower bounds ub and nb of the search spacePerforming elite mutation g_m times on the optimal spider bee individuals in the population, and if the individual fitness value after each mutation is better, updating the corresponding spider bee individuals in the populationFitness valueObtaining a new mutated spider bee population for the current individual spider bees and the fitness value thereof;
Comparison ofAnd (3) withIf the size of (a)More preferably, updating the optimal spider bee individualsAnd an optimal fitness valueFor the current spider bee individuals and corresponding fitness values, updating the population quantity G by utilizing a reciprocating population reduction strategy until the number of reciprocating cycles is reached, and judging whether the current iteration number T is larger than the number of reciprocating cyclesIf yes, outputting the corresponding optimal fitness valueAnd optimal spider bee individualsOtherwise, returning to the main loop to continue repeating the steps, and updating the solution.
Optionally, generating an initial spider bee population by adopting tent chaotic mapping initialization strategy, and encoding and decoding the generated population, specifically:
generating an initial matrix of 2M rows and G columns at random, and updating the initial matrix by utilizing tent chaotic mapping to generate an initial spider bee population, wherein the initial spider bee population is as follows:
Wherein, The m-th numerical value of the g-th spider bee body and chaotic parameters;An mth value representing a g-th parameter in the initial matrix; Representing population size; Representing chaos variables, rand representing random numbers, ub and nb representing upper and lower bounds, respectively, and generating an initial spider bee population as follows:
wherein each row represents a spider bee individual ,Each individual spider bee represents a set of solutions, simplifying the spider bee population into;
For any individual spider bees,The time of departure is indicated as such,Representing client nodes, decoding distribution paths of the latter M nodes by introducing a position-sequence coding method, and decoding the distribution paths of the latter M nodes by introducing a position-sequence coding methodSorting from small to large, recording number, converting it into natural number code, using said partial code to represent customer point number, distributing the route according to vehicle capacity constraint, if the sum of customer point requirements is met, adding said customer point into the route, otherwise, generating new route until all nodes are distributed, and for vehicle departure timeThe number of the generated paths is distributed from front to back in sequence.
Optionally, when performing the prey behavior update the spider bee population, the search behavior is used to update the individuals in the spider bee population, in particular:
Generating random numbers 、,When (1)When generating parameters,Wherein rn1 is a random number, and two spider bee individuals are randomly selected from the population、Updating parameters in each individual spider bee using the following formula:
When (when) When generating parameters,Wherein,,Rand represents a random number, and the parameters in each individual spider bee are updated using the following formula:
The following and escaping actions are adopted to update individuals in the spider bee population, specifically:
When (when) At this time, individual spider bees are randomly selected from the populationThe generation of the parameter C,
Updating parameters in each individual spider bee using the following formula:
When (when) When generating the parameter matrix,WhereinUpdating parameters in each individual spider bee using the following formula:
updating individuals in the spider bee population by using nesting behaviors, specifically:
When (when) When in use, globally optimal spider bee individuals are utilizedUpdating the spider bee individuals as follows:
When (when) When in use, the individual spider bees are randomly selected、、Generating a new spider bee entity using the formula:
Wherein, Parameters representing levy flight generation.
Alternatively, when performing mating behavior to update the spider bee population, individual spider bees are randomly selected、、Generating parameters、The method specifically comprises the following steps:
Computing a matrix Randomly selecting a spider bee individual,;
Calculation ofRandomly selecting a spider bee individual、,;
By means of parameters、Generating new spider bee individuals, generating random numbers rand for each newly generated spider bee individual in the population, and updating the individual in the population to be the new spider bee individual by using the following formula when rand < Cr, or not updating;
Boundary checking, namely checking whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space, if so, correcting the new spider bee position to the boundary to obtain an updated T-th generation spider bee population ,。
Optionally, each individual in the current spider bee population is elite mutated using an adaptive elite mutation strategy as follows:
Wherein, 、Representing a random selection of spider bee individuals from the population.
Optionally, the population number G is updated using a reciprocal population reduction strategy as follows:
wherein,% is the remainder operator, Indicating the number of reciprocation cycles.
Compared with the prior art, the invention provides a multi-temperature co-allocation path planning method based on an enhanced multi-strategy spider bee algorithm. The invention enriches the research content of the flexible compartment in the multi-temperature co-distribution path optimization, and provides a dynamic adjustment method based on the flexible compartment, so that the distribution path planning is more flexible and efficient. The flexible compartment can adjust space allocation according to the temperature control demands of different cargoes, so that the loading efficiency and the resource utilization rate under the multi-temperature-zone co-distribution environment are improved, and a more intelligent and high-adaptability solution is provided for cold chain distribution. By optimizing the vehicle departure time strategy, the invention effectively reduces the energy consumption in the transportation process, and particularly when facing a long-distance multi-temperature co-matched cold chain transportation scene, the optimized departure time can effectively avoid a high-temperature period, and the residence time of the vehicle in a high-temperature environment is reduced, so that the refrigeration burden is reduced. The optimized departure time not only improves the transportation efficiency, but also greatly reduces the energy consumption and enhances the environmental protection and economy of cold chain transportation. The invention provides a comprehensive path optimization and energy efficiency management scheme for the distribution of cold chain products such as prefabricated vegetables, combines flexible compartments with departure time optimization, improves the transportation efficiency, and reduces the refrigeration energy consumption waste by precisely controlling the temperature control environment. Through implementing this scheme, the enterprise can be under the prerequisite of guarantee cold chain delivery quality, improves transportation efficiency and resource utilization, promotes the industry to the development to more high-efficient, more environmental protection.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic flow chart of the enhanced multi-strategy spider bee algorithm of the present invention.
FIG. 3 is a schematic diagram of the path optimization result according to the present invention.
FIG. 4 is a diagram showing the comparison of the dispatch number of the flexible compartment with and without consideration of the present invention.
FIG. 5 is a graphical illustration of vehicle full load ratio comparisons with and without flexible compartments in accordance with the present invention.
FIG. 6 is a schematic diagram of the overall cost of the present invention with and without optimized departure times.
Fig. 7 is a schematic diagram of an iterative comparison of the algorithm of the present invention with other algorithms.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
the embodiment 1 of the invention discloses a multi-temperature co-formulation path planning method based on an enhanced multi-strategy spider bee algorithm, which is shown in fig. 1 and comprises the following steps:
each delivery vehicle is defined to start at delivery center point 0 and eventually return to delivery center point 0.
And step 1, acquiring vehicle resources, customer demands and position information of a distribution center, and establishing a refrigeration cost calculation function based on the change of the ambient temperature.
The vehicle resource and customer demand and position information of the distribution center are specifically the maximum loading capacity Q of the distribution vehicle, the heat conducting area of the carriage, the position distribution of the distribution center point and the customer point, the demand of each customer point and the time window requirement.
The refrigeration cost calculation function is established based on the change of the ambient temperature, and specifically comprises the following steps:
The ambient temperature change can be approximately seen as a sine curve, the ambient temperature is divided by hours, each hour is taken as a time zone, and the ambient temperature of each time zone is calculated as follows:
Wherein, M represents the number of time to which the time zone belongs; represents the maximum temperature in a day; Representing the lowest temperature during the day; Representing hourly simulations A respective air temperature value; taking 12 as a fixed parameter;
Assuming that the temperature per hour is constant, the vehicle is considered to pass through one or more time zones, and the vehicle k is assumed to start from point i, with the starting time expressed as The next node accessed is denoted as j, the time of travel from point i to point jThe service time at point j is expressed asThe heat transfer areas of the refrigerated and frozen compartments are as follows:
Wherein, The method comprises the steps of representing the distance from a node i to a node j, v representing the speed of a vehicle k, N representing a node set consisting of a client point set and a distribution center, i, j epsilon N, i=0, representing the distribution center, p representing p-type carriages of the distribution vehicle, p=1 representing a refrigeration type, and p=2 representing a refrigeration type; Representing the refrigerating cargo demand of the j node; z represents the total heat conduction area of the carriage; representing the frozen goods demand of the j node;
Time of day The belonging time zone m is expressed as, wherein,、Respectively the departure timeA start time point and an end time point corresponding to the belonging time zone m,Also indicates the starting time point of the time zone m+1, and since the ending time point of the time zone m coincides with the starting time point of the time zone m+1, the ending time point corresponding to the belonging time zone m is indicated by the starting time point of the time zone m+1The travel and service time is expressed asJudgingWhether or not to establish;
If true, then from From moment to point j, the vehicles are all at temperatureRunning down, neglecting the effect of opening and closing the door during service, the refrigeration cost during this period is expressed as:
Wherein, A temperature representing time zone m; representing the thermal conductivity of the cabin; Indicating the temperature of the refrigerator compartment; indicating the temperature of the refrigerated compartment; k represents a collection of delivery vehicles; representing refrigeration cost; representing the heat generated by the refrigerator during the delivery of vehicle k from node i to node j; E represents the heat generated by the unit gasoline;
If not, then in the time period The vehicle is at temperatureDriving downwards for a period of timeExpressed as:
Updating ,Re-judgingWhether or not to be established to obtain the refrigeration cost.
And 2, constructing a multi-temperature co-distribution path planning model based on the flexible compartment and the environment temperature and aiming at minimizing the total distribution cost based on the refrigeration cost calculation function, and constructing constraint conditions of the multi-temperature co-distribution path planning model based on vehicle resources and customer information.
To be used for、As a decision variable, the decision variable may be, among other things,Indicating whether the delivery vehicle k travels from node i to the next node j; Indicating whether the class p car of vehicle k meets the class p commodity demand of customer j.
Based on the refrigeration cost calculation function, a multi-temperature co-distribution path planning model based on flexible compartments and ambient temperature, which aims at minimizing the total distribution cost, is constructed as follows:
Wherein, K represents a collection of delivery vehicles; a fixed resource consumption indicating the start of delivery vehicle k; Indicating whether the vehicle is starting from the distribution center; N represents a node set formed by the client point set and the distribution center, and when i, j epsilon N and i=0, the distribution center is represented; indicating whether the delivery vehicle k travels from the i-node to the next node j; Representing the capacity of the delivery vehicle; representing the number of initial loads of the delivery vehicle k when it starts from the delivery center; Indicating the fuel consumption rate of the delivery vehicle when fully loaded; indicating the fuel consumption rate of the delivery vehicle when the delivery vehicle is empty; Representing the distance from node i to node j; representing a unit fuel supply level; p represents p-class compartments of the delivery vehicle, p=1 represents refrigeration class, and p=2 represents refrigeration class; the cargo loss rate of the p-type cargo is represented, L represents unit number cargo unit price; representing the time required to travel from node i to node j; representing the service time of node j; representing the p-type cargo demand of the j node; representing the cabin of a vehicle k Whether the p-class commodity requirement of the customer i is met; representing a time window penalty cost; a time window representing a client point i, wherein, Indicating the earliest arrival time at which node i requires the delivery vehicle to arrive,Representing the latest arrival time at which node i requires the delivery vehicle to arrive; Representing the arrival time of delivery vehicle k to node i; 、 penalty coefficients representing early and late arrival of the vehicle, respectively; Indicating refrigeration costs.
Constraint conditions of the multi-temperature co-distribution path planning model are constructed based on vehicle resources and customer information, and the constraint conditions are as follows:
indicating that the vehicle starts from the distribution center, and finally returns to the distribution center after the customer is served;
indicating that customer j receives a delivery service when there is a vehicle passing through arc (i, j);
indicating a balance of vehicle traffic to access the customer;
indicating that the delivery demand of each customer commodity is not greater than the corresponding delivery vehicle compartment capacity;
indicating that each customer commodity can only be dispensed by one vehicle;
representing a time relationship between a time precursor to arrival of the vehicle at customer j and a successor node;
indicating that the customer demand is not negative;
Indicating that the customer needs are not detachable;
representing an cancellation sub-loop;
Indicating that the departure time of the vehicle must be within the dispatch center time window;
indicating that all vehicles must be returned to the distribution center within a prescribed time;
a decision variable is 1 if the vehicle k travels from customer i to customer j, and 0 if not;
As decision variables, representing the cabin of the vehicle k The p-type commodity requirement of the customer i is met, namely 1, otherwise, 0;
wherein, N represents a node set composed of a client point set and a distribution center, and i, j epsilon N, i=0 represents the distribution center; Indicating whether to arrive at the j node from the distribution center; indicating whether to return to the distribution center from a certain i node; K represents a distribution vehicle set; indicating whether the delivery vehicle k travels from the i-node to the next node j; P-class cars representing vehicles k satisfy p-class commodity demands of customers j, p representing p-class cars of delivery vehicles, p=1 representing refrigeration classes, and p=2 representing refrigeration classes; Indicating whether the vehicle k is from node j to node i; representing the p-type cargo demand of the i node; Representing the capacity of the delivery vehicle; indicating the time when delivery vehicle k starts from point j; Indicating the time when the delivery vehicle k starts from point i; representing the time required to travel from node i to node j; S represents a set of path vertices; The departure time of the delivery vehicle k from the delivery center is shown; A delivery time window representing a delivery center is provided, Indicating the earliest time that the delivery center requires delivery of vehicle k; m represents the number of clients; indicating whether the delivery vehicle k travels from the i-node to the next node j; Indicating whether the p-class cars of delivery vehicle k meet the p-class merchandise demand of customer i; representing the time from the last customer point of the path; indicating the latest return to the distribution center time.
And 3, carrying out target solution of the multi-temperature co-distribution path planning model by utilizing an enhanced multi-strategy spider bee algorithm combining the chaotic mapping, the self-adaptive elite strategy and the reciprocating population reduction strategy as shown in fig. 2, and obtaining the optimal departure time and distribution path.
The target solution of the multi-temperature co-distribution path planning model is carried out by utilizing an enhanced multi-strategy spider bee algorithm combined with chaotic mapping, a self-adaptive elite strategy and a reciprocating population reduction strategy, and the method specifically comprises the following steps:
initializing parameters including population size G and minimum population size Upper bound ub, lower bound nb, number of iterationsThe method comprises the steps of weighing the rate TR, the crossing rate Cr, the current optimal individual variation frequency p_m and the global optimal individual variation frequency g_m, and defining and initializing the current iteration frequency to be T=0.
Generating an initial spider bee population by adopting tent chaotic mapping initialization strategy, coding and decoding the generated population, and calculating an adaptability value set of the initial population by utilizing the multi-temperature co-allocation path planning modelFrom a set of fitness values of the initial populationSelecting the minimum fitness valueThe corresponding spider bee individuals are marked as the initial population optimal spider bee individualsAnd globally optimal spider bee individualsThe corresponding fitness value is a global optimal fitness value。
Generating an initial spider bee population by adopting tent chaotic mapping initialization strategy, and encoding and decoding the generated population, wherein the method specifically comprises the following steps:
randomly generating an initial matrix of 2M rows and G columns, and updating the initial matrix by utilizing tent chaotic mapping to generate the initial spider bee population, wherein the initial spider bee population is as follows:
Wherein, The m-th numerical value of the g-th spider bee body and chaotic parameters;An mth value representing a g-th parameter in the initial matrix; Representing population size; Representing chaotic variable, rand representing random number, ub and nb representing upper and lower bounds respectively, and generating the initial spider bee population as follows:
wherein each row represents a spider bee individual ,Each individual spider bee represents a set of solutions, simplifying the spider bee population into;
For any individual spider bees,The time of departure is indicated as such,Representing client nodes, decoding distribution paths of the latter M nodes by introducing a position-sequence coding method, and decoding the distribution paths of the latter M nodes by introducing a position-sequence coding methodSorting from small to large, recording number, converting it into natural number code, using said partial code to represent customer point number, distributing the route according to vehicle capacity constraint, if the sum of customer point requirements is met, adding said customer point into the route, otherwise, generating new route until all nodes are distributed, and for vehicle departure timeThe number of the generated paths is distributed from front to back in sequence.
Entering into main circulation to generate random numberAs an index of the search agent,If (if)Executing hunting behavior update spider bee population, otherwise executing mating behavior update spider bee population, when executing hunting behavior update spider bee population, makingWhen (when)At this time, a random number p is generated,When (when)When using search behavior to update individuals in a spider bee population, whenWhen the following and escaping actions are adopted to update the individuals in the spider bee population, whenWhen the method is used, individuals in the spider bee population are updated by nesting behaviors, and random numbers are generated、,When (when)When using the current optimal solutionUpdating individuals in a spider bee population whenRandomly selecting a spider bee in the population and updating individuals in the spider bee population using additional steps when performing mating behavior updating the spider bee population, randomly selecting individual spider bees、、Generating parameters、By means of parameters、Generating new spider bee individuals, generating random numbers rand for each newly generated spider bee individual in the population, updating the individual in the population to be the new spider bee individual when rand < Cr, otherwise, not updating.
When performing hunting behavior to update a spider bee population, the search behavior is used to update individuals in the spider bee population, specifically:
Generating random numbers 、,When (1)When generating parameters,Wherein rn1 is a random number, and two spider bee individuals are randomly selected from the population、Updating parameters in each individual spider bee using the following formula:
When (when) When generating parameters,Wherein,,Rand represents a random number, and the parameters in each individual spider bee are updated using the following formula:
The following and escaping actions are adopted to update individuals in the spider bee population, specifically:
When (when) At this time, individual spider bees are randomly selected from the populationThe generation of the parameter C,
Updating parameters in each individual spider bee using the following formula:
When (when) When generating the parameter matrix,WhereinUpdating parameters in each individual spider bee using the following formula:
updating individuals in the spider bee population by using nesting behaviors, specifically:
When (when) When in use, globally optimal spider bee individuals are utilizedUpdating the spider bee individuals as follows:
When (when) When in use, the individual spider bees are randomly selected、、Generating a new spider bee entity using the formula:
Wherein, Parameters representing levy flight generation;
Randomly selecting individual spider bees when performing mating behavior to update the spider bee population 、、Generating parameters、The method specifically comprises the following steps:
Computing a matrix Randomly selecting a spider bee individual,;
Calculation ofRandomly selecting a spider bee individual、,;
By means of parameters、Generating new spider bee individuals, generating random numbers rand for each newly generated spider bee individual in the population, and updating the individual in the population to be the new spider bee individual by using the following formula when rand < Cr, or not updating;
Boundary checking, namely checking whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space, if so, correcting the new spider bee position to the boundary to obtain an updated T-th generation spider bee population ,。
Coding and decoding the new spider bee individuals obtained currently, and calculating the fitness value set of all individuals in the spider bee population according to the multi-temperature co-distribution path planning modelRespectively compare in the current setThe adaptation degree value of the corresponding individual in the updated population is equal to the adaptation degree value of the corresponding individual in the prior spider bee population, if the current adaptation degree value is better, the corresponding individual in the updated population is the current spider bee individual, otherwise, the original individual is reserved, and the spider bee population is updated by comparing in turnCoding and decoding the new spider bee individuals obtained currently, calculating the fitness value set of all individuals in the updated spider bee population, and optimizing the fitness valueAnd global optimum fitness valueComparing, if the current fitness value is better, updating the optimal spider bee individualsAnd an optimal fitness valueIs the current spider bee individual and the corresponding fitness value.
Let the current iteration timesPerforming elite mutation p_m times on each individual in the current spider bee population by using an adaptive elite mutation strategy, checking whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space by boundary, and correcting the new spider bee position to the boundary if the new spider bee position exceeds the upper and lower bounds ub and nb of the search spacePerforming elite mutation g_m times on the optimal spider bee individuals in the population, and if the individual fitness value after each mutation is better, updating the corresponding spider bee individuals in the populationFitness valueObtaining a new mutated spider bee population for the current individual spider bees and the fitness value thereof。
Elite variation is performed on each individual in the current spider bee population using an adaptive elite variation strategy as follows:
Wherein, 、Representing a random selection of spider bee individuals from the population.
Comparison ofAnd (3) withIf the size of (a)More preferably, updating the optimal spider bee individualsAnd an optimal fitness valueFor the current spider bee individuals and corresponding fitness values, updating the population quantity G by utilizing a reciprocating population reduction strategy until the number of reciprocating cycles is reached, and judging whether the current iteration number T is larger than the number of reciprocating cyclesIf yes, outputting the corresponding optimal fitness valueAnd optimal spider bee individualsOtherwise, returning to the main loop to continue repeating the steps, and updating the solution.
Updating the population number G using a reciprocal population reduction strategy as follows:
wherein,% is the remainder operator, Indicating the number of reciprocation cycles.
Example 2:
The embodiment 2 of the invention discloses a specific application of a multi-temperature co-formulation path planning method based on an enhanced multi-strategy spider bee algorithm, which comprises the following steps:
Since MCVRPTW has no internationally recognized example, to verify the effectiveness of the algorithm of the present invention, the VRPTW algorithm of Solomon (1987) was modified to a useable MCVRP algorithm. The cooling requirement and the freezing requirement in the experiment are obtained by splitting the client requirement according to the proportion of 2:1, and the capacity of the freezing compartment and the refrigerating compartment of the flexible compartment is not considered in the experiment and is divided according to the proportion of 3:1 of the original compartment capacity. In order to finely simulate the corresponding environmental temperature change of time, in view of the consistency of the time windows of the distribution centers of the selected calculation examples, the time window of each distribution center is uniformly divided into 12 time periods, each time period represents 1 hour and corresponds to one temperature data, and on the basis, all the customer time window data in the original calculation examples are correspondingly modified to conform to a new time division scheme, and the vehicle speed is correspondingly modified to a value corresponding to one time period. In the experiment, the first 4 of the C1 class data sets of 25,50 and 100 scales are respectively selected for carrying out reconstruction experiments, and each scale of the data sets is respectively called S1-S4, R1-R4 and L1-L4. The algorithm programming adopts MATLAB R2023b, and all experiments are carried out on a server cluster with 256GB memory and CentOS Linux release 7.6.1810 operating system in Intel (R) Xeon (R) Gold 6258R CPU @ 2.70 GHz. Examples related parameters are shown in table 1.
Table 1 example related parameters
In order to verify the effect of the enhanced multi-strategy spider bee optimization algorithm in solving the path optimization problem, experimental tests are carried out, and the result is shown in figure 3. These calculations further demonstrate the superiority of the proposed algorithm. Fig. 3 shows intuitively the optimized path, service order. It can be clearly seen that all the client points have successfully completed the service, which illustrates that the optimization algorithm effectively reduces the overall distribution cost while improving the service quality. The results show that the enhanced multi-strategy spider bee optimization algorithm not only realizes remarkable cost optimization on the premise of ensuring the service quality, but also can stably obtain high-quality solutions in a plurality of experimental rounds, and fully verifies the robustness and effectiveness of the algorithm.
To demonstrate the advantages of considering the flexible compartment over the traditional fixed compartment, the algorithm of the present invention was run 10 times in 12 examples to find the optimal result. Consider the dispatch number versus not considering the flexible compartment, as shown in fig. 4. As can be seen from fig. 4, the flexible compartment model reduces the number of vehicles by 2 on average, and can reduce the number of vehicles by 4 at most, compared to the fixed compartment model, which directly reduces the fixed cost of the vehicles. Second, in terms of full rate, consider as compared to vehicle full rate without considering flexible compartments, as shown in fig. 5. As can be seen from fig. 5, the use of flexible compartments also brings about a significant improvement. The full load rate is greatly improved from 57.5%, 71.67% and 69.69% to 76.76%, 86% and 89.85% respectively, for small-scale, medium-scale and large-scale examples. This lifting means that each vehicle can carry more cargo in a single delivery, thereby reducing vehicle shuttle times and empty rates. Meanwhile, the stability and the reliability of the distribution system are enhanced by improving the full load rate.
To demonstrate the advantage of optimizing departure time, the algorithm was run 10 times in each example to get the optimal solution, and the total cost of optimizing versus not optimizing departure time is compared as shown in FIG. 6. As can be seen from the results of fig. 6, the total cost is positively influenced by optimizing the departure time. In the different scale calculation examples, the optimized total cost is reduced by 4.34 percent (large scale), 4.08 percent (medium scale) and 0.67 percent (small scale), which is mainly beneficial to better matching the external temperature change encountered by the vehicle in the transportation process by precisely adjusting the departure time, thereby reducing the refrigeration requirement, optimizing the refrigeration efficiency and avoiding the energy waste of overcooling.
In order to verify the superiority and performance of the strong multi-strategy spider bee algorithm in model solving, the strong multi-strategy spider bee algorithm provided by the invention is compared with a novel heuristic algorithm self-Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA), a tuna optimization algorithm (TSO) and a moth group optimization algorithm (MSA) which are widely applied and excellent in recent years. Meanwhile, a Particle Swarm Optimization (PSO) is selected as a classical benchmark for comparison to show the lifting space and advantages of the algorithm compared with the classical algorithm, and each algorithm is operated under the condition of a calculation example L4, and the result is shown in fig. 7. It can be seen that although PSO and MSA converge faster at the early stage, it is difficult to optimize further at the later stage due to the weak initial solution generation capability and the tendency to fall into local optima. In contrast, EMSWO, ASFSSA, and TSO are more advantageous at an early stage, enabling rapid approach to the optimal solution region. Wherein EMSWO shows a faster decreasing trend, and shows remarkable convergence advantage in the initial stage of iteration. The lower final convergence value of EMSWO compared to ASFSSA and TSO indicates a stronger optimizing capability. In addition, EMSWO tends to be stable in the later stage of iteration, no obvious fluctuation or rebound occurs, and excellent solution stability and convergence reliability are shown.
The embodiment of the invention discloses a multi-temperature co-allocation path planning method based on an enhanced multi-strategy spider bee algorithm. The invention enriches the research content of the flexible compartment in the multi-temperature co-distribution path optimization, and provides a dynamic adjustment method based on the flexible compartment, so that the distribution path planning is more flexible and efficient. The flexible compartment can adjust space allocation according to the temperature control demands of different cargoes, so that the loading efficiency and the resource utilization rate under the multi-temperature-zone co-distribution environment are improved, and a more intelligent and high-adaptability solution is provided for cold chain distribution. By optimizing the vehicle departure time strategy, the invention effectively reduces the energy consumption in the transportation process, and particularly when facing a long-distance multi-temperature co-matched cold chain transportation scene, the optimized departure time can effectively avoid a high-temperature period, and the residence time of the vehicle in a high-temperature environment is reduced, so that the refrigeration burden is reduced. The optimized departure time not only improves the transportation efficiency, but also greatly reduces the energy consumption and enhances the environmental protection and economy of cold chain transportation. The invention provides a comprehensive path optimization and energy efficiency management scheme for the distribution of cold chain products such as prefabricated vegetables, combines flexible compartments with departure time optimization, improves the transportation efficiency, and reduces the refrigeration energy consumption waste by precisely controlling the temperature control environment. Through implementing this scheme, the enterprise can be under the prerequisite of guarantee cold chain delivery quality, improves transportation efficiency and resource utilization, promotes the industry to the development to more high-efficient, more environmental protection.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A multi-temperature co-distribution path planning method based on an enhanced multi-strategy spider bee algorithm is characterized by comprising the following steps:
Step1, acquiring vehicle resources, customer demands and position information of a distribution center, and establishing a refrigeration cost calculation function based on the change of the environmental temperature;
step 2, constructing a multi-temperature co-distribution path planning model based on flexible compartments and environmental temperature and aiming at minimizing distribution total cost based on the refrigeration cost calculation function, and constructing constraint conditions of the multi-temperature co-distribution path planning model based on vehicle resources and customer information;
step 3, utilizing an enhanced multi-strategy spider bee algorithm combined with chaotic mapping, a self-adaptive elite strategy and a reciprocating population reduction strategy to carry out target solution of the multi-temperature co-distribution path planning model so as to obtain optimal departure time and distribution paths;
In the enhanced multi-strategy spider bee algorithm combining the chaotic mapping, the self-adaptive elite strategy and the reciprocating population reduction strategy, an initial spider bee population is generated by adopting a tent chaotic mapping initialization strategy, and the generated population is encoded and decoded, specifically:
randomly generating an initial matrix of 2M rows and G columns, and updating the initial matrix by utilizing tent chaotic mapping to generate the initial spider bee population, wherein the initial spider bee population is as follows:
xg,m=rand*(ub-nb)+nb;
Wherein o g,m represents the mth value of the G-th spider bee entity, the chaos parameter delta epsilon (0, 1]; x g,m represents the mth value of the G-th parameter in the initial matrix, g=1, 2, 3..G represents the population size, m=1, 2..M represents the chaos variable, rand represents the random number, ub and nb represent the upper and lower bounds respectively, the initial spider bee population generated is as follows:
wherein each row represents a spider bee individual
Each individual spider bee represents a set of solutions, simplifying the spider bee population into
The method comprises the steps of (a) representing departure time by any spider bee entity a g,og,1,og,m,…,og,M, wherein o g,M+1,…,og,M represents a client node, decoding distribution paths of the M nodes after the spider bee entity a g,og,1,og,m,…,og,M, sorting o g,M+1,…,og,M according to a sequence from small to large by using a position-sequence coding method, recording numbers, then converting the numbers into natural number codes, representing client point numbers, distributing the paths according to vehicle capacity constraint, adding the client point into the paths if the sum of client point demands is met, otherwise, generating a new path until all the nodes are distributed, and distributing the paths in sequence from front to back directly according to the number of the generated paths for the vehicle departure time o g,1,og,m,…,og,M;
Elite variation is performed on each individual in the current spider bee population using an adaptive elite variation strategy as follows:
Wherein, Representing randomly selecting a spider bee individual from the population;
Updating the population number G using a reciprocal population reduction strategy as follows:
Where,% is the remainder operator and ε represents the number of reciprocal cycles.
2. The multi-temperature co-formulation path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1, wherein in step 1, a refrigeration cost calculation function is established based on the change of the ambient temperature, specifically:
dividing the environment temperature according to the hours, taking each hour as a time zone, and calculating the environment temperature of each time zone as follows:
Wherein T m represents the ambient temperature in m time zone, m represents the number of times the time zone belongs to, T max represents the highest temperature in a day, T min represents the lowest temperature in a day, temp represents simulated Temp air temperature values per hour, and delta is a fixed parameter;
Assuming that the temperature per hour is constant, the vehicle is considered to pass through one or more time zones, let vehicle k start from point i, start time be t ik, next node visited be j, time of travel from point i to point j The service time at point j is denoted as w j, the heat transfer area of the refrigerated and frozen compartment is as follows:
Wherein c ij represents the distance from node i to node j, v represents the speed of vehicle k, N represents the node set composed of the client point set and the distribution center, i, j epsilon N, i=0, p represents the p-type carriage of the distribution vehicle, p=1 represents the refrigeration type, and p=2 represents the refrigeration type; Representing the refrigerating cargo demand of the j node; z represents the total heat conduction area of the carriage; representing the frozen goods demand of the j node;
The time zone m to which the time t ik belongs is denoted as Q m＝[tm,tm+1 ], wherein t m、tm+1 is a start time point and an end time point corresponding to the time zone m to which the departure time t ik belongs, t m+1 is also denoted as a start time point of the time zone m+1, and the end time point corresponding to the time zone m is denoted as a start time point of the time zone m+1 because the end time point of the time zone m coincides with the start time point of the time zone m+1, and the time for traveling and service in the time zone Q m is denoted as t m+1-tik, so that whether f ij+wj≤tm+1-tik is satisfied is determined;
if so, from the time T ik to the departure point j, the vehicle runs at the temperature T m, and the influence of opening and closing the door during service is ignored, and the refrigeration cost in the period is expressed as:
Hijk＝R1(S1(Tm-T1)+S2(Tm-T2))(fij+wj);
Wherein T m represents the temperature in time zone m, R 1 represents the car heat transfer coefficient, T 1 represents the refrigerator car temperature, T 2 represents the refrigerator car temperature, C f represents the unit fuel supply level, K represents the distribution vehicle set, C Refrigerating system represents the refrigeration cost, H ijk represents the heat generated by the refrigerator during the distribution of vehicle K from node i to node j, E represents the heat generated by the unit gasoline;
If not, the vehicle is driven at temperature T m for a period of time [ T ik,tm+1 ] denoted as H ijk:
Hijk＝R1(S1(Tm-T1)+S2(Tm-T2))(tm+1-tik);
Updating f ij＝fij-(tm+1-tik),tik＝tm+1, and re-judging whether f ij+wj≤tm+1-tik is true or not so as to obtain the refrigeration cost.
3. The multi-temperature co-formulation path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1, wherein in step 2, a multi-temperature co-formulation path planning model based on flexible compartments and ambient temperature targeting at minimizing the total cost of delivery is constructed based on the refrigeration cost calculation function as follows:
minf=C1+C2+C3+C4+C Refrigerating system ;
Wherein C 1 represents a fixed cost, K represents a distribution vehicle set, C k represents fixed resource consumption for starting the distribution vehicle K, x 0jk represents whether the vehicle starts from a distribution center, C 2 represents a transportation cost, N represents a node set consisting of a client point set and the distribution center, i, j E N, i=0, represents the distribution center, x kij represents whether the distribution vehicle K travels from the i node to the next node j, Q represents the capacity of the distribution vehicle, and Q k represents the initial load quantity of the distribution vehicle K when the distribution vehicle starts from the distribution center; Indicating the fuel consumption rate of the delivery vehicle when fully loaded; The fuel consumption rate of the delivery vehicle when no load is applied, C ij is the distance from the node i to the node j, C f is the unit fuel supply level, C 3 is the loss cost, p is the p-class compartment of the delivery vehicle, p=1 is the refrigeration class, p=2 is the refrigeration class, lambda p is the goods loss rate of the p-class goods, L is the unit price of the quantity of goods, f ij is the time required for running from the node i to the node j, and w j is the service time of the node j; The method comprises the steps of representing the p-type goods demand of a j node, representing y ikp whether a carriage u k,p of a vehicle k meets the p-type goods demand of a customer i, representing a time window penalty cost by C 4, representing a time window of a customer point i by [ ET i,LTi ], wherein ET i represents the earliest arrival time of the delivery vehicle required by the node i, LT i represents the latest arrival time of the delivery vehicle required by the node i, e ik represents the arrival time of the delivery vehicle k to the node i, alpha and beta represent penalty coefficients of early arrival and late arrival of the vehicle respectively, and C Refrigerating system represents refrigeration cost.
4. The multi-temperature co-distribution path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1, wherein in step 2, constraint conditions of the multi-temperature co-distribution path planning model are constructed based on vehicle resources and customer information, and the constraint conditions are as follows:
Wherein N represents a node set consisting of a client point set and a distribution center, i, j is epsilon N, i=0, represents the distribution center, x 0jk represents whether the client point set starts from the distribution center to reach j nodes, x i0k represents whether the client point set returns to the distribution center from a certain i node, K represents a distribution vehicle set, x ijk represents whether the distribution vehicle K runs from the i node to the next node j, y jkp represents whether p-class carriages of the vehicle K meet the p-class commodity requirement of a client j, p represents p-class carriages of the distribution vehicle, p=1 represents a refrigeration class, p=2 represents a freezing class, and x jik represents whether the vehicle K runs from the node j to the node i; The method comprises the steps of (1) representing the p-class goods demand of an i node, (Q) representing the capacity of a delivery vehicle, (t jk) representing the time when the delivery vehicle k starts from a j point, (t ik) representing the time when the delivery vehicle k starts from the i point, (f ij) representing the time required for traveling from the i point to the j point, (w j) representing the service time of the j point, (S) representing the path vertex set, (t 0k) representing the starting time of the delivery vehicle k from the delivery center, (ET 0,LT0) representing the delivery time window of the delivery center, (ET 0) representing the earliest time when the delivery center requests the delivery vehicle k, LT 0 representing the latest time, (m) representing the number of clients, (x kij) representing whether the delivery vehicle k travels from the i point to the next node j, (y ikp) representing whether the p-class carriages of the delivery vehicle k meet the p-class goods demand of a client i, t mk represents the time when the last client point of the path starts, and (t 0) representing the latest time to return to the delivery center.
5. The multi-temperature co-formulation path planning method based on the enhanced multi-strategy spider bee algorithm according to claim 1, wherein in the step 3, the enhanced multi-strategy spider bee algorithm combined with chaotic mapping, self-adaptive elite strategy and reciprocal population reduction strategy is utilized to carry out target solution of the multi-temperature co-formulation path planning model, specifically:
initializing parameters including population size G, minimum population size G min, upper bound ub, lower bound nb, iteration number T max, weighing rate TR, crossing rate Cr, current optimal individual variation frequency p_m and global optimal individual variation frequency g_m;
Generating an initial spider bee population by adopting tent chaotic mapping initialization strategy, coding and decoding the generated population, and calculating an adaptability value set of the initial population by utilizing the multi-temperature co-allocation path planning model Selecting a minimum fitness value from a fitness value set F * of the initial populationThe corresponding spider bee individuals are marked as the initial population optimal spider bee individualsAnd a global optimal spider bee individual C best, the corresponding fitness value is a global optimal fitness value S best;
Entering a main loop, generating a random number r 6 as a search agent index, r 6 E (0, 1), if r 6 < TR, executing a hunting behavior update spider bee population, otherwise executing a mating behavior update spider bee population, and when executing the hunting behavior update spider bee population, letting Generating a random number p, p epsilon (0, 1) when G < G.times.K, updating individuals in the spider bee population using search behavior when p < (1-T/T max), updating individuals in the spider bee population using follow-up and escape behavior when p > (1-T/T max), updating individuals in the spider bee population using nesting behavior when G > G.times.K, generating a random number r 3、r4,r3、r4 epsilon (0, 1), and using a current optimal solution when r 3＜r4 Updating individuals in the spider bee population, randomly selecting spider bees within the population and updating the individuals in the spider bee population with additional steps when r 3＞r4, randomly selecting the individual spider bees when performing mating to update the spider bee populationGenerating a parameter V 1、V2, generating a new spider bee individual by using the parameter V 1、V2, generating a random number rand for each newly generated spider bee individual in the population, updating the individual in the population to be the new spider bee individual when rand < Cr, otherwise, not updating;
Coding and decoding the new spider bee individuals obtained currently, and calculating the fitness value set of all individuals in the spider bee population according to the multi-temperature co-distribution path planning model Respectively comparing the current setsThe adaptation degree value of the new spider bee population is compared with the adaptation degree value of the corresponding individual in the spider bee population before updating, if the adaptation degree value is better, the corresponding individual in the updating population is the current spider bee individual, otherwise, the original individual is kept, the spider bee population A (T) is updated, the new spider bee individual obtained at present is encoded and decoded, the adaptation degree value set of all the individuals in the updated spider bee population is calculated, and the optimal adaptation degree value is calculatedComparing with the global optimal fitness value S best, if the current fitness value is better, updating the optimal spider bee individual C best and the optimal fitness value S best into the current spider bee individual and the corresponding fitness value;
Performing elite mutation p_m times on each individual in the current spider bee population by using an adaptive elite mutation strategy, checking whether the new spider bee position exceeds the upper and lower bounds ub and nb of the search space by boundary, if so, correcting the new spider bee position to the boundary, performing elite mutation g_m times on the optimal spider bee individual in the new population A (T) by using the adaptive elite mutation strategy, and if the individual fitness value after each mutation is better, updating the corresponding spider bee individual in the population Fitness valueObtaining a mutated new spider bee population A (T) for the current spider bee individuals and fitness values thereof;
Comparison of And S best, ifAnd if so, updating the optimal spider bee individuals C best and the optimal fitness value S best to be the current spider bee individuals and the corresponding fitness values, updating the population quantity G by utilizing a reciprocating population reduction strategy until the number of times of reciprocating cycles is reached, judging whether the current iteration number T is greater than T max, if so, outputting the corresponding optimal fitness value S best and the optimal spider bee individuals C best, otherwise, returning to the main cycle, continuing to repeat the steps, and updating the solution.
6. The method for multi-temperature co-formulation path planning based on enhanced multi-strategy spider bee algorithm according to claim 5, wherein when performing hunting behavior to update the spider bee population, the individual in the spider bee population is updated using the searching behavior, in particular:
Generating random number r 1、r2,r1、r2 E (0, 1), generating parameter m 1,m1＝|rn1|*r1 when r 1＞r2, wherein rn1 is random number, randomly selecting two spider bee individuals from population Updating parameters in each individual spider bee using the formula:
when r 1＜r2, the generation parameter m 2,m2 =b×cos (2 l pi), where L= (α 2-1)*rand+1,a2＝-1-1*(T/Tmax), rand represents a random number, and the parameters in each individual spider bee are updated using the following formula:
The following and escaping actions are adopted to update individuals in the spider bee population, specifically:
when r 1＞r2, the individual spider bees are randomly selected from the population Generating a parameter C, a=2-2 x (T/T max), updating the parameters in each individual spider bee using the following formula:
When r 1＜r2, a parameter matrix V= { V, V 2,vm,…,vM},vm ε (-k, k) is generated, where Updating parameters in each individual spider bee using the formula:
updating individuals in the spider bee population by using nesting behaviors, specifically:
When r 1＞r2, the individual spider bees are updated with the globally optimal individual spider bees C best as follows:
Randomly selecting individual spiders and bees when r 1＜r2 is the same Generating a new spider bee entity using the formula:
Where χ represents the parameters of levy flight generation.
7. The method for multi-temperature co-formulation path planning based on enhanced multi-strategy spider bee algorithm according to claim 5, wherein individual spider bees are selected randomly when the mating behavior is performed to update the spider bee populationThe generation parameter V 1、V2 is specifically:
Calculating matrix V 1＝{v1,…,vm,…,vM, randomly selecting a spider bee individual
Calculating V 2＝{z1,…,zm,…,zM, randomly selecting a spider bee individual
Generating new spider bee individuals by using a parameter V 1、V2, generating a random number rand for each newly generated spider bee individual in the population, and updating the individual in the population to be the new spider bee individual by using the following formula when rand < Cr, or not updating;
boundary checking, checking whether the new spider bee position exceeds the upper and lower bounds ub, nb of the search space, if so, correcting the new spider bee position to the boundary to obtain an updated T-th generation spider bee population A (T),
CN202510205716.3A 2025-02-25 2025-02-25 Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm Active CN119721905B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202510205716.3A CN119721905B (en) 2025-02-25 2025-02-25 Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202510205716.3A CN119721905B (en) 2025-02-25 2025-02-25 Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm

Publications (2)

Publication Number Publication Date
CN119721905A CN119721905A (en) 2025-03-28
CN119721905B true CN119721905B (en) 2025-06-20

Family

ID=95081510

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202510205716.3A Active CN119721905B (en) 2025-02-25 2025-02-25 Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm

Country Status (1)

Country Link
CN (1) CN119721905B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118036931A (en) * 2024-01-12 2024-05-14 淮阴工学院 Multi-AGV task cooperative scheduling method and device applied to flexible manufacturing workshops
CN118642478A (en) * 2024-04-29 2024-09-13 淮阴工学院 Delivery robot path-finding method, device and medium based on improved spider bee algorithm

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
IT1015759B (en) * 1974-07-09 1977-05-20 Istel Spa SYSTEM WITH SEQUENTIAL CLOSING CONDITIONED BY FAILURES DUE TO THE CONTROL OF ENERGY DISTRIBUTION LINES
JP4730440B2 (en) * 2009-01-01 2011-07-20 ソニー株式会社 Trajectory planning apparatus, trajectory planning method, and computer program
CN117495016A (en) * 2023-11-15 2024-02-02 上海恒驰软件有限公司 Urban rail traffic scheduling method and system based on big data and SWO
CN118886673A (en) * 2024-07-30 2024-11-01 昆明理工大学 A method for scheduling stacker cranes and AGV clusters in aviation material warehouses based on improved RRT algorithm
CN118999614A (en) * 2024-09-13 2024-11-22 桂林电子科技大学 Vehicle path planning method based on inflection point multi-objective evolutionary algorithm

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118036931A (en) * 2024-01-12 2024-05-14 淮阴工学院 Multi-AGV task cooperative scheduling method and device applied to flexible manufacturing workshops
CN118642478A (en) * 2024-04-29 2024-09-13 淮阴工学院 Delivery robot path-finding method, device and medium based on improved spider bee algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
时变温度下考虑装载策略的多温共配优化研究;尹廷玉等;《物流与供应链经济》;20211130;正文第1-5页 *

Also Published As

Publication number Publication date
CN119721905A (en) 2025-03-28

Similar Documents

Publication Publication Date Title
CN113011644B (en) Smart city dynamic cold-chain logistics scheduling method based on ant colony optimization algorithm
Qi et al. Optimization of vehicle routing problem for emergency cold chain logistics based on minimum loss
Zhang et al. An optimization model for the vehicle routing problem in multi-product frozen food delivery
CN109978471B (en) Cold-chain logistics path optimization method with time window
CN112686458A (en) Optimized scheduling method for multi-vehicle fleet cargo delivery process
CN107341628B (en) A hub-and-spoke logistics network hub site selection and allocation method based on probabilistic taboo algorithm
CN113780961B (en) Low-carbon vaccine cold chain optimization distribution method of multi-target firework algorithm
CN117993586A (en) Cold chain distribution optimization method and system based on improved genetic algorithm
Agrawal et al. FLIHSBC: fuzzy logic and improved harmony search based clustering algorithm for wireless sensor networks to prolong the network lifetime
Li et al. Multi-objective optimization for location-routing-inventory problem in cold chain logistics network with soft time window constraint
CN115222327A (en) Urban cold chain logistics vehicle route optimization method, system and storage medium
CN119721905B (en) Multi-temperature co-distribution path planning method based on enhanced multi-strategy spider bee algorithm
Hou et al. An Improved Particle Swarm Optimization Algorithm for the Distribution of Fresh Products.
CN120013192A (en) An energy optimization scheduling method and system for industrial parks with electricity and carbon synergy
CN110752611A (en) Method for optimizing operation and energy storage capacity of town energy Internet
Yin et al. Routing optimization in distribution of cold chain logistics
CN116502989B (en) Cold-chain logistics vehicle path optimization method based on mixed balance optimization algorithm
Fu et al. Research on Optimization of cold chain logistics distribution path of fresh products based on Hybrid Genetic Algorithm
Yang et al. Research on vehicle routing problem based on improved dragonfly algorithm
Hu et al. Diploid hybrid particle swarm optimization with differential evolution for open vehicle routing problem
Hu et al. Low-Carbon Vehicle Routing Models and Optimization Algorithms with Hybrid Time Window.
Xiao et al. Research on Logistics Distribution Problem Based on Improved Ant Colony Algorithm
Juan Optimization of multi-temperature joint distribution path for cold chain logistics under carbon emission
Guo Research on logistics distribution route optimization based on ant colony algorithm
Shao et al. Design of logistics operation management algorithm based on information technology on internet

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant