Supply chain scheduling optimization method based on transportation outsourcing and time window constraint

Abstract

The application provides a supply chain scheduling optimization method based on transportation outsourcing and time window constraints, and relates to the technical field of supply chain scheduling. Based on the framework of strict time window constraints, and under the condition that self-transport capacity cannot completely meet the order transportation demand, the order production and transportation process are cooperatively scheduled, the benefits of order autonomous transportation and outsourcing transportation are balanced, the production scheduling scheme and the transportation scheduling scheme under the condition of partial transportation task outsourcing are comprehensively considered, and an approximate optimal solution of the problem is dynamically and accurately obtained, so that the resources of the supply chain production and transportation stages can be cooperatively optimized and configured, thereby the overall cost of the supply chain is maximally reduced, and the resource cooperation efficiency and management ability of the enterprise supply chain are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain scheduling, and particularly relates to a supply chain scheduling optimization method based on transportation outsourcing and time window constraints. BACKGROUND

[0002] In modern supply chain management, enterprises increasingly adopt the business model of single-order production, and there is little or no finished product inventory in actual operation, so that production and transportation plans are closely connected to eliminate the increase in cost and the extension of product circulation time caused by high inventory; enterprises also need to ensure on-time delivery of products within strict time windows to reduce the loss of enterprise income and reputation caused by delayed delivery. In order to meet the time window constraints, enterprises often rely on transportation outsourcing to provide flexible transportation support. Transportation outsourcing has become an important strategy for enterprises to improve efficiency and reduce costs. By outsourcing part of the transportation tasks to professional logistics service providers, enterprises can optimize resource allocation and reduce the risk of delayed delivery. However, the introduction of transportation outsourcing makes the trade-off between cost, benefit and other factors in the collaborative scheduling plan of the supply chain particularly complex, and it is necessary to find the optimal balance point between autonomous transportation and transportation outsourcing.

[0003] However, existing research has rarely considered the production and transportation collaborative scheduling problem of the supply chain based on the transportation outsourcing scenario. From the perspective of actual supply chain management, the current research on production and transportation collaborative scheduling is difficult to help enterprises cope with the challenges of strict time window constraints and transportation outsourcing benefit trade-offs to achieve the desired on-time delivery performance with the minimum total cost, and ensure the efficiency and reliability of overall operation. Therefore, it is of great significance to comprehensively consider the production and transportation collaborative problem under the condition of partial transportation outsourcing in the framework of strict time window constraints, and to realize the efficient collaboration of the supply chain. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a supply chain scheduling optimization method based on transportation outsourcing and time window constraints, which solves the problem of production and transportation collaborative scheduling of the supply chain under the conditions of product transportation outsourcing and strict time window constraints.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme:

[0006] A supply chain scheduling optimization method based on transportation outsourcing and time window constraints, the scheduling optimization method comprises the following steps:

[0007] S1, setting the input parameters of the simulated annealing algorithm based on neighborhood search according to the customer order, production process and transportation process related data;

[0008] S2, obtaining a global optimal solution according to the input parameters and the simulated annealing algorithm based on neighborhood search, and obtaining a minimum total cost of the supply chain, an optimal production process single machine scheduling scheme and an optimal order transportation scheme according to the global optimal solution.

[0009] Preferably, in the S1, the input parameters of the simulated annealing algorithm based on neighborhood search specifically include:

[0010] order set ; customer number, order number, distribution point number , wherein the customer number, the distribution point number and the order number are consistent, and the customer number corresponds to the order number , when the production center; order production sequence number on the production line ; transportation vehicle number , when the transportation is carried out by the outsourcing vehicle; transportation batch when the vehicle is on tour ;

[0011] the number of products produced by each production line per unit time ;

[0012] the production cost per unit time of each production line ;

[0013] order required product quantity ;

[0014] order arrival deadline ;

[0015] transportation capacity of a single autonomous transportation vehicle ;

[0016] transportation speed of autonomous transportation and outsourcing transportation vehicles ;

[0017] distance between the factory and the customer , and the distance between the order and the order ; ;

[0018] cost coefficient of autonomous transportation vehicles per unit driving time ;

[0019] unit distance cost coefficient of outsourcing transportation per unit product ;

[0020] penalty factor per unit time of delay ;​

[0021] Factory and Orders Transportation time between Correspondingly, orders With orders Transportation time between ;

[0022] Complete Collection of Single-Machine Scheduling Schemes for Production Process A feasible scheduling scheme , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ;

[0023] Order Production start time ;

[0024] Order Production end time ;

[0025] Complete Collection of Multi-Vehicle Dispatch Solutions for Transportation Processes A feasible transportation solution , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced. For the corresponding order The transport batch number in the transport vehicle;

[0026] Order Transportation departure time ;

[0027] Order End time of transportation ;

[0028] vehicle No. The set of transport orders corresponding to this transport ;

[0029] vehicle No. The time of departure from the factory for this shipment ;

[0030] vehicle No. Second transport return to factory time ;

[0031] order production cost ;

[0032] order autonomous transportation route transportation cost ;

[0033] order arrival delay cost ;

[0034] order outsourcing transportation cost ;

[0035] total supply chain cost .

[0036] Preferably, the S2 specifically comprises:

[0037] S2.1, setting running parameters and initial solution of the simulated annealing algorithm , the running parameters including: initial temperature , termination temperature , temperature decay function coefficient ;

[0038] S2.2, obtaining and recording new solution by perturbation ;

[0039] S2.3, calculating energy increment according to the initial solution and the new solution

[0040] S2.4, if the energy increment is less than 0, the new solution is taken as the current solution, and the current solution is recorded as ; if the energy increment is not less than 0, the acceptance probability is calculated, and the new solution is accepted according to the acceptance probability; if the new solution is accepted, the initial temperature is updated based on the given temperature decay function coefficient; if the new solution is not accepted, return to S2.2

[0041] S2.5, selecting the neighborhood structure based on the roulette method, and generating the neighborhood solution of the current solution according to the selected neighborhood structure ; ;

[0042] S2.6, using the selected neighborhood structure to perform local search on the neighborhood solution , obtain the local optimal solution , and compare the local optimal solution with the current solution obtained by the simulated annealing algorithm , if is better than , let​ And record the neighborhood structure. The number of times a local optimum is successfully found; if Not better Then record the neighborhood structure. The number of failures to find a local optimum is recorded, and the neighborhood structure is updated accordingly. The probability of;

[0043] S2.7 Determine if the current temperature is lower than the final temperature. If it is, output the globally optimal solution. If it is not lower than, use the current temperature as the initial temperature, and the current solution... As the initial solution, return S2.2.

[0044] Preferably, S2.1 specifically includes:

[0045] Based on a hybrid algorithm combining simulated annealing and variable neighborhood search, the relevant data of customer orders, production processes, and transportation processes in the supply chain production and transportation collaborative scheduling process are encoded to initialize the production and transportation model. Any feasible solution that can ensure the normal completion of production and transportation for all orders is randomly selected as the initial solution. The initial solution consists of two sequences: SC and PC-SX. SC represents the order of order scheduling on a single machine, and PC-SX represents the transportation scheme for autonomous transportation orders across multiple vehicles. The initial solution is decoded at the initial temperature to obtain... .

[0046] Preferably, in step S2.1, the encoding process includes:

[0047] S2.1.1 Based on the characteristics of the two key stages of the supply chain production and transportation processes, the solution... Solution from the production stage and the solution of the transportation phase The solutions for the production stage are represented by one-dimensional integer arrays, while the solutions for the transportation stage are represented by two one-dimensional integer arrays. The included order production sequence SC has a sequence length of . In SC, the numbers represent order numbers, and the number of times a number appears indicates the quantity of products required for that order; Solution It consists of two parts: the order shipping sequence (PC) and the list of vehicle numbers corresponding to the order (SX). The length of the sequence or list is [length missing]. PC and SX consist of autonomous transportation orders and vehicle numbers, respectively. Each column in the array represents the order with the corresponding number being transported by the vehicle with the corresponding number.

[0048] The decoding process includes:

[0049] S2.1.2. The set of order processing sequences to be allocated to the production line for processing is generated from solution X1, i.e., a certain feasible scheduling scheme. , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ,in ;Order Production end time is For example, the following is an illustration: Let That is, order 4 is the second item to be produced on the production line, meaning the production completion time for order 4 is... And orders production costs ;

[0050] S2.1.3. The solution X2 generates a set of orders allocated to different vehicles, representing a feasible transportation plan. , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced; thus, the vehicle can be obtained. The collection of orders transported ,when Time order collection This is a collection of outsourced transportation orders; due to the capacity limitations of the vehicles themselves, the order collection... Delivery will be conducted in multiple batches; the rule to be met is: order collection. Internal orders are sorted according to their known production completion times. After sorting, vehicle transportation batches are determined based on capacity constraints. The division, that is, the vehicle No. The set of transport orders corresponding to this transport and set The total number of products required for internal orders must not exceed the vehicle's capacity limit.

[0051] S2.1.4 When the order is shipped independently, the order... The start time of the transport is ;Order The end time of the transport is When the order In order collection When the first one is transported, that is At that time, the end time of transportation When orders In order collection the end time of the transportation when the batch is not the first to be transported the orders in the batch the order the previous transportation location of the order, i.e. , and satisfies the condition ; when the order is outsourced transportation, the start time of the transportation of the order ; the end time of the transportation of the order ;

[0052] S2.1.5, the transportation cost of the order is divided into the total cost of autonomous transportation or the total cost of outsourcing transportation; when , the total transportation cost of the order is the sum of the autonomous transportation route transportation cost and the arrival delay cost, wherein the autonomous transportation route transportation cost , and the arrival delay cost ; when , the outsourcing transportation total cost of the order is the sum of the outsourcing transportation cost and the arrival delay cost, wherein the outsourcing transportation cost of the order , and the arrival delay cost ;

[0053] S2.1.6, for a single batch transportation vehicle, the vehicle departure time is ; the vehicle returns to the factory time , wherein ;

[0054] S2.1.7, therefore, the total supply chain cost .

[0055] Preferably, the S2.3 specifically comprises:

[0056] S2.3.1, according to the initial solution and the new solution , the supply chain total costs of the initial solution and the new solution are obtained by decoding, and the values are respectively recorded as and ;

[0057] S2.3.2, the difference between and , i.e. , is calculated and taken as the energy increment.

[0058] Preferably, the S2.4 specifically comprises:

[0059] When the energy increment is not less than 0, the new solution acceptance probability is calculated, and the acceptance probability is represented as​​​ ; take random number , , when , update the new solution as the current solution; otherwise, do not update, the new solution is not accepted, and return to S2.2.

[0060] Preferably, the S2.5 specifically comprises:

[0061] operate on the current solution in the selected neighborhood structure , so that the initial solution of this stage can jump to another solution in the feasible region, thereby avoiding repeated iterations on the same solution and making the algorithm fall into a local optimum.

[0062] Preferably, the S2.6 specifically comprises:

[0063] S2.6.1, neighborhood structure of the variable neighborhood search algorithm , in the algorithm parameter setting stage, set the initial selection probability of each neighborhood structure is the same, where ;

[0064] S2.6.2, record the number of times each neighborhood structure successfully searches for a local optimal solution and the number of failures in the local search process of the variable neighborhood search algorithm , Update the selection probability of each neighborhood according to the following formula: , where represents the local search success rate of the neighborhood structure , and the calculation formula is , is a small positive number;

[0065] S2.6.3, define a variable , generate a random number in the interval , and assign the random number to the variable ; determine the interval to which the variable belongs, if , select the neighborhood structure as the type of neighborhood search.

[0066] Preferably, the supply chain scheduling optimization system comprises an input parameter acquisition module and a solution module.

[0067] The input parameter acquisition module is used to set the input parameters of the simulated annealing algorithm based on neighborhood search according to customer orders, production processes, order data in transportation processes, production line data, transportation vehicle data, and transportation outsourcing cost data.

[0068] The solving module is used for obtaining a global optimal solution according to an input parameter and a simulated annealing algorithm based on neighborhood search, and obtaining a minimum total cost of the supply chain, an optimal production process single machine scheduling scheme and an optimal order transportation scheme according to the global optimal solution.

[0069] The application provides a supply chain scheduling optimization method based on transportation outsourcing and time window constraints.

[0070] In the application, a strict time window constraint framework is used, and the order production and transportation processes are cooperatively scheduled under the condition that self-transportation capacity cannot completely meet the order transportation demand, the benefits of order self-transportation and outsourcing transportation are balanced, the production scheduling scheme and the transportation scheduling scheme under the condition of partial transportation task outsourcing are comprehensively considered, and an approximate optimal solution of the problem is dynamically and accurately obtained, so that the resources of the supply chain production and transportation stages can be cooperatively optimized and configured, the overall cost of the supply chain is minimized, and the resource cooperation efficiency and management ability of the enterprise supply chain are improved. BRIEF DESCRIPTION OF DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0072] Figure 1 A flowchart of a supply chain scheduling optimization method based on transportation outsourcing and time window constraints provided by the embodiments of the present application is shown in the figure.

[0073] Figure 2 A flowchart of a simulated annealing algorithm based on neighborhood search provided by the embodiments of the present application is shown in the figure.

[0074] Figure 3 A decoding schematic diagram provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0076] This application provides a supply chain scheduling optimization method based on transportation outsourcing and time window constraints, which solves the problem of supply chain production and transportation coordination scheduling under product transportation outsourcing and strict time window constraints.

[0077] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0078] Example:

[0079] like Figure 1 As shown, this invention provides a supply chain scheduling optimization method based on transportation outsourcing and time window constraints. The scheduling optimization method includes the following steps:

[0080] S1. Based on customer orders, production process, and transportation process data, set the input parameters for the simulated annealing algorithm based on neighborhood search;

[0081] S2. Based on the input parameters and the simulated annealing algorithm based on neighborhood search, obtain the global optimal solution, and based on the global optimal solution, obtain the minimum total cost of the supply chain, the optimal single-machine scheduling scheme for the production process, and the optimal order transportation scheme.

[0082] like Figure 2 As shown, in step S1, based on customer orders, production process, and transportation process data, the input parameters of the simulated annealing algorithm based on neighborhood search are set to construct supply chain total cost models under conditions of partial product transportation outsourcing and strict time window constraints. The specific implementation process is as follows:

[0083] Data related to customer orders, production processes, and transportation processes are obtained through manual data entry and other methods. Input parameters for a simulated annealing algorithm based on neighborhood search are then set.

[0084] The input parameters for the simulated annealing algorithm based on neighborhood search specifically include:

[0085] Order Sets Customer ID, Order ID, Delivery Point ID Because each customer corresponds to a single order, the customer number, delivery point number, and order number must be consistent. , At that time, it was the production center; the production sequence number of orders on the production line. Transport vehicle number , Transportation was carried out by outsourced vehicles; the transportation batches during vehicle round-trip deliveries ;

[0086] The number of products produced by each production line per unit time ;

[0087] Production cost per unit time for each production line ;

[0088] Order Required product quantity ;

[0089] Order Delivery deadline ;

[0090] The transport capacity of a single autonomous transport vehicle ;

[0091] Transportation speed of self-operated and outsourced transportation vehicles ;

[0092] Factory and Customer Distance between Correspondingly, orders With orders Distance between ;

[0093] Cost coefficient per unit travel time of autonomous transport vehicles ;

[0094] Unit distance cost coefficient for outsourced transportation per unit of product ;

[0095] Penalty factor per unit of lateness ;

[0096] Factory and Orders Transportation time between Correspondingly, orders With orders Transportation time between ;

[0097] Complete Collection of Single-Machine Scheduling Schemes for Production Process A feasible scheduling scheme , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ;

[0098] Order Production start time ;

[0099] Order Production end time ;

[0100] Complete Collection of Multi-Vehicle Dispatch Solutions for Transportation Processes A feasible transportation solution , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced. For the corresponding order The transport batch number in the transport vehicle;

[0101] Order Transportation departure time ;

[0102] Order End time of transportation ;

[0103] vehicle No. The set of transport orders corresponding to this transport ;

[0104] vehicle No. The time of departure from the factory for this shipment ;

[0105] vehicle No. Second transport return to factory time ;

[0106] Order production costs ;

[0107] Order Self-transportation route transportation costs ;

[0108] Order Delivery delay costs ;

[0109] Order Outsourcing transportation costs ;

[0110] Total supply chain cost ;

[0111] In step S2, a globally optimal solution is obtained based on the input parameters and a simulated annealing algorithm based on neighborhood search. Then, based on this globally optimal solution, the minimum total cost of the supply chain, the optimal single-machine scheduling scheme for the production process, and the optimal order transportation scheme are obtained. The specific implementation process is as follows:

[0112] S2.1, setting the running parameters of the simulated annealing algorithm and the initial solution The running parameters include: setting the running parameters, including the initial temperature , the final temperature , the temperature decay function coefficient ; specifically including:

[0113] Based on the simulated annealing and variable neighborhood search hybrid algorithm, the customer order, production process and transportation process related data in the supply chain production and transportation collaborative scheduling process are encoded, the production and transportation model is initialized, and any feasible scheme that can meet all order normal production and transportation is randomly selected as the initial solution. The initial solution is composed of SC and PC-SX two sequences, wherein SC code represents the order of order production on single machine, PC-SX code represents the transportation scheme of autonomous transportation order on multiple vehicles, and the initial solution under the initial temperature is decoded to obtain . The specific implementation process is as follows:

[0114] As shown in Figure 3 , the encoding process is as follows:

[0115] S2.1.1, based on the characteristics of the two key stages of supply chain production process and transportation process, the solution is composed of the solution of the production stage and the solution of the transportation stage , the solution of the production stage is represented by one-dimensional integer array, and the solution of the transportation stage is represented by two one-dimensional integer arrays. The solution contains order production order sequence SC, and the sequence length is . The number in SC represents the order number, and the number of times the number appears represents the number of products required by the order. The solution contains two parts, order transportation order sequence PC and order corresponding vehicle number list SX, and the length of the sequence or list is . PC and SX are composed of autonomous transportation orders and vehicle numbers, and each column in the array represents that the corresponding order is transported by the corresponding vehicle number. In particular, the order in SC and PC-SX is determined according to the following rules:

[0116] The order arrangement sequence in PC is arranged according to the order production sequence in SC; the autonomous transportation vehicles and outsourcing numbers in SX represent the transportation order of the order in the numbered vehicle from front to back.

[0117] It's important to understand that the vehicles available for the transportation phase are primarily to ensure smooth operations. However, the actual demand for transporting completed orders often exceeds the vehicle's capacity. Therefore, after completing one round of delivery, the vehicle must immediately be deployed for the next. In the case of outsourced transportation, factors such as vehicle batch and capacity limitations are disregarded; the order's production completion time is the start time for outsourced transportation. Furthermore, in cases of delivery delays, the total order transportation cost needs to be calculated, including the cost of in-house or outsourced transportation, as well as the delay cost incurred due to the delayed arrival.

[0118] The decoding process involves calculating the total supply chain cost based on the obtained feasible solution and the actual scenario. This mainly includes the following operations:

[0119] S2.1.2. The set of order processing sequences to be allocated to the production line for processing is generated from solution X1, i.e., a certain feasible scheduling scheme. , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ,in .Order Production end time is For example, the following is an illustration: Let That is, order 4 is the second item to be produced on the production line, meaning the production completion time for order 4 is... And orders production costs ;

[0120] S2.1.3. The solution X2 generates a set of orders allocated to different vehicles, representing a feasible transportation plan. , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced. Vehicles will then be available. The collection of orders transported ,when Time order collection This is a collection of outsourced transportation orders. Due to the capacity limitations of the vehicles themselves, the order collection... Delivery will be conducted in multiple batches. The required rule is: order aggregation. Internal orders are sorted according to their known production completion times. After sorting, vehicle transportation batches are determined based on capacity constraints. The division, that is, the vehicle No. a set of transportation orders corresponding to the sub-transportation , and the sum of the required product quantities of the orders in the set is not greater than the capacity limit of the vehicle itself;

[0121] S2.1.4, when the order is self-transported, the transportation start time of the order is . The transportation end time of the order is , when the order is the first to be transported in the order set , that is, , the transportation end time ; when the order is not the first to be transported in the order set , at this time the transportation end time , the order in the transportation batch is the previous transportation location of the order , that is, , and the condition is satisfied. When the order is outsourced transportation, the transportation start time of the order is . The transportation end time of the order is ;

[0122] S2.1.5, at this time the transportation cost of the order is divided into self-transportation total cost or outsourced transportation total cost. When , the order transportation total cost is the sum of self-transportation route transportation cost and delivery delay cost, wherein the self-transportation route transportation cost , the delivery delay cost ; when , the order outsourced transportation total cost is the sum of outsourced transportation cost and delivery delay cost, wherein the order outsourced transportation cost , the delivery delay cost ;

[0123] S2.1.6, for a single batch transportation vehicle, the vehicle departure time is ; the vehicle returns to the factory time , wherein ;

[0124] S2.1.7, therefore the supply chain total cost ;

[0125] S2.2, by perturbation, a new solution is obtained and recorded The specific implementation process is as follows:

[0126] S2.2.1, Perturbation operation, which is to perturb the initial solution. Make random modifications within a range to explore the solution space, starting from the initial solution. Randomly select a new candidate solution within the neighborhood. Random modification methods include:

[0127] 1. Exchange method: In the production sequence SC, two orders are randomly selected, such as order SC. and orders Then, the corresponding production positions are exchanged; in the order vehicle numbering scheme PC-SX, two orders are randomly selected from PC, such as order... and orders Then, exchange the vehicle numbers in the corresponding SX;

[0128] 2. Reversal Method: In the production sequence SC, randomly select two orders, such as order... and orders , order Orders This group of orders (including orders) Orders To reposition orders, the last order in the production sequence is placed at the beginning of the production sequence, and vice versa. In the order vehicle numbering scheme PC-SX, two orders are randomly selected from PC, such as order... and orders , order Orders This group of orders (including orders) Orders ), and swap the vehicle number in the corresponding SX;

[0129] 3. Insertion method: In the production sequence SC, randomly select two orders, such as order... and orders Change order The production location will be the order. Production location is placed in the order The next one; in the order vehicle numbering scheme PC-SX, randomly select two orders in PC, such as order and orders Change order The vehicle number in the corresponding SX is the order number. The corresponding vehicle number, and the order in the PC. The vehicle number in SX corresponding to the last produced order is changed to the vehicle number in SX corresponding to the order before the last produced order.

[0130] It is worth noting that in order to effectively optimize the global solution in the solution space, the disturbance can be large in the high temperature stage to promote extensive exploration of the solution space; as the temperature decreases, the disturbance should gradually decrease to enhance the convergence of the algorithm.

[0131] S2.3, according to the initial solution and the new solution, calculate the energy increment;

[0132] S2.3.1, according to the initial solution and the new solution , the total cost of the supply chain is obtained by decoding the initial solution and the new solution, and the values are respectively recorded as and , the decoding process is seen in S2.1;

[0133] S2.3.2, calculate and difference, that is , and take it as the energy increment;

[0134] S2.4, if the energy increment is less than 0, that is , the new solution is taken as the current solution, and the current solution is recorded as ; if the energy increment is not less than 0, calculate the acceptance probability, and accept the new solution according to the acceptance probability. If the new solution is accepted, update the initial temperature based on the given temperature decay function coefficient; if the new solution is not accepted, return to S2.2. The specific implementation process is as follows:

[0135] When the energy increment is not less than 0, calculate the acceptance probability of the new solution, and the acceptance probability is expressed as . Take a random number , , when , update the new solution as the current solution; otherwise, do not update, the new solution is not accepted, and return to S2.2.

[0136] S2.5, select the neighborhood structure based on the roulette method, and generate the neighborhood solution of the current solution according to the selected neighborhood structure . The specific implementation process is as follows:

[0137] select the neighborhood structure of the current solution The operation within the region allows the initial solution of this stage to jump to another solution within the feasible region, thus avoiding repeated iterations under the same solution and preventing the algorithm from getting trapped in local optima. Considering the complexity of supply chain production and transportation coordination scheduling and the requirements of constraints, to ensure that the solution generated by each operation change is a feasible solution, the following four neighborhood structures are specifically designed:

[0138] Neighborhood Structure 1: Defining Variables Select any two orders from SC. and Assign the order location to a variable. Compare the corresponding order delivery deadlines. and ,like and Position in sequence SC The encoding is placed in position The first bit of the encoding; define the variable. Randomly select two orders on the PC, such as order number 1. and orders Assign the order location to a variable. From the interval Two numbers are randomly selected and assigned to positions in sequence SX. The encoding and placement of the position The encoding;

[0139] Neighborhood Structure 2: Defining Variables Select any two orders from SC. and Assign the order location to a variable. Compare the corresponding order delivery deadlines. and ,like and Position in sequence SC and location Interchange the encodings; define variables. Randomly select two orders on the PC, such as order number 1. and orders Assign the order location to a variable. , position in sequence SX and location The encodings are interchanged;

[0140] Neighborhood Structure 3: Defining Variables Select any two orders from SC. and Assign the order location to a variable. Compare the required product quantities for the corresponding orders. and , if and , interchange the codes of the orders at positions and in sequence SC; define a variable , randomly select two orders in PC, such as order and order , assign the order positions to variable , place the code of the order at position in sequence SX in front of the code of the order at position ;

[0141] Neighborhood structure 4: define a variable , randomly select two orders in SC, such as order and order , assign the order positions to variable , compare the distances between the corresponding factories and customers and , if and , interchange the codes of the orders at positions and in sequence SC; define a variable , randomly select two orders in PC, such as order and order , assign the order positions to variable , interchange the codes of the orders at positions and in sequence SX;

[0142] S2.6, use the selected neighborhood structure to perform a local search on the neighborhood solution , obtain a local optimal solution , and compare the local optimal solution with the current solution obtained by the simulated annealing algorithm , if is better than , let , and record the number of times that the neighborhood structure successfully searches for a local optimal solution; if is not better than , record the number of times that the neighborhood structure fails to search for a local optimal solution, and update the probability of selecting the neighborhood structure . The specific implementation process is as follows:

[0143] S2.6.1, neighborhood structure of the variable neighborhood search algorithm ​In the setting algorithm parameter stage, initial selection probability of each neighborhood structure is set The same, wherein ;

[0144] S2.6.2, in the local search process of variable neighborhood search algorithm, record the number of times each neighborhood structure successfully searches for local optimal solution and the number of failures , update the selection probability of each neighborhood according to the following formula: , wherein denotes the local search success rate of neighborhood structure , the calculation formula is , is a small positive number;

[0145] S2.6.3, define a variable , generate a random number in the interval , assign the random number to the variable , judge the interval to which the variable belongs, if , select neighborhood structure as the type of neighborhood search.

[0146] S2.7, judge whether the current temperature is lower than the final temperature, if lower, output the global optimal solution ; if not, take the current temperature as the initial temperature, and take the current solution as the initial solution, return to S2.2. Specifically, it includes:

[0147] According to the global optimal solution output by the algorithm , the SC sequence and PC-SX sequence in the solution are analyzed according to the actual situation of the enterprise supply chain to obtain the order processing order and order transportation scheme, and the minimum total cost of the supply chain is obtained.

[0148] The embodiment of the application also provides a supply chain scheduling optimization system based on transportation outsourcing and time window constraint, which comprises an input parameter acquisition module and a solving module.

[0149] The input parameter acquisition module is used for setting input parameters of the simulated annealing algorithm based on neighborhood search according to customer orders, production processes, order data in the transportation process, production line data, transportation vehicle data and transportation outsourcing cost data;

[0150] The solving module is used for acquiring a global optimal solution according to the input parameters and the simulated annealing algorithm based on neighborhood search, and acquiring a minimum total cost of a supply chain, an optimal production process single machine scheduling scheme and an optimal order transportation scheme according to the global optimal solution.

[0151] Compared with the prior art, the present application has the following beneficial effects:

[0152] 1、In the embodiment of the present application, based on the framework of strict time window constraint, and in the case that self-transport capacity cannot completely meet the order transportation demand, the order production and transportation process is cooperatively scheduled, the benefits of order autonomous transportation and outsourcing transportation are weighed, the production scheduling scheme and the transportation scheduling scheme under the condition of partial transportation task outsourcing are comprehensively considered, and the approximate optimal solution of the problem is dynamically and accurately obtained, which can effectively cooperatively optimize and configure the resources of the supply chain production and transportation two stages, thereby minimizing the overall cost of the supply chain and improving the resource cooperation efficiency and management ability of the enterprise supply chain.

[0153] 2、In the embodiment of the present application, a simulated annealing algorithm based on neighborhood search is designed, which combines the global optimization ability of the simulated annealing algorithm and the local search ability of the variable neighborhood search algorithm; at the same time, corresponding adaptive selection mechanisms are designed in the simulated annealing algorithm and the variable neighborhood search algorithm, and special crossover operators and local search neighborhood structures are designed, which effectively improve the solution efficiency and solution quality of the algorithm.

[0154] It should be noted that, in this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Furthermore, no element discussed herein is required unless otherwise specifically recited in a claim.

[0155] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A supply chain scheduling optimization method based on transportation outsourcing and time window constraints, characterized in that, The scheduling optimization method includes the following steps: S1. Based on customer orders, production process, and transportation process data, set the input parameters for the simulated annealing algorithm based on neighborhood search; S2. Based on the input parameters and the simulated annealing algorithm based on neighborhood search, obtain the global optimal solution, and based on the global optimal solution, obtain the minimum total cost of the supply chain, the optimal single-machine scheduling scheme for the production process, and the optimal order transportation scheme. In step S1, the input parameters of the simulated annealing algorithm based on neighborhood search specifically include: Order Sets Customer ID, Order ID, Delivery Point ID ,in , At that time, it was the production center; the production sequence number of orders on the production line. Transport vehicle number , Transportation was carried out by outsourced vehicles; the transportation batches during vehicle round-trip deliveries ; The number of products produced by each production line per unit time ; Production cost per unit time for each production line ; Order Required product quantity ; Order Delivery deadline ; The transport capacity of a single autonomous transport vehicle ; Transportation speed of self-operated and outsourced transportation vehicles ; Factory and Customer Distance between Correspondingly, orders With orders Distance between ; Cost coefficient per unit travel time of autonomous transport vehicles ; Unit distance cost coefficient for outsourced transportation per unit of product ; Penalty factor per unit of lateness ; Factory and Orders Transportation time between Correspondingly, orders With orders Transportation time between ; Complete Collection of Single-Machine Scheduling Schemes for Production Process A feasible scheduling scheme , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ; Order Production start time ; Order Production end time ; Complete Collection of Multi-Vehicle Dispatch Solutions for Transportation Processes A feasible transportation solution , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced. For the corresponding order The transport batch number in the transport vehicle; Order Transportation departure time ; Order End time of transportation ; vehicle No. The set of transport orders corresponding to this transport ; vehicle No. The time of departure from the factory for this shipment ; vehicle No. Second transport return to factory time ; Order production costs ; Order Self-transportation route transportation costs ; Order Delivery delay costs ; Order Outsourced transportation costs ; Total supply chain cost ; S2 specifically includes: S2.1 Setting the running parameters and initial solution of the simulated annealing algorithm. Operating parameters include: initial temperature Termination temperature Temperature decay function coefficient ; S2.2 Obtain and record the new solution through perturbation. ; S2.3 Calculate the energy increment based on the initial solution and the new solution; S2.4 If the energy increment is less than 0, the new solution is taken as the current solution, and the current solution is denoted as... If the energy increment is not less than 0, calculate the acceptance probability and accept the new solution according to the acceptance probability; if the new solution is accepted, update the initial temperature based on the given temperature decay function coefficient; if the new solution is not accepted, return to S2.

2. S2.

5. Select the neighborhood structure based on the roulette wheel method, and then select the neighborhood structure accordingly. Generate the current solution neighborhood solution ; S2.6, Use the selected neighborhood structure Neighborhood solution Perform a local search to obtain a local optimum. and the local optimal solution The current solution obtained by the simulated annealing algorithm If a comparison is made, Superior Then let And record the neighborhood structure. The number of times a local optimum is successfully found; if Not better Then record the neighborhood structure. The number of failures to find a local optimum is recorded, and the neighborhood structure is updated accordingly. The probability of; S2.7 Determine if the current temperature is lower than the final temperature. If it is, output the globally optimal solution. If it is not lower than, use the current temperature as the initial temperature, and the current solution... As the initial solution, return S2.2; S2.1 specifically includes: Based on a hybrid algorithm combining simulated annealing and variable neighborhood search, the relevant data of customer orders, production processes, and transportation processes in the supply chain production and transportation collaborative scheduling process are encoded to initialize the production and transportation model. Any feasible solution that can ensure the normal completion of production and transportation for all orders is randomly selected as the initial solution. The initial solution consists of two sequences: SC and PC-SX. SC represents the order of order scheduling on a single machine, and PC-SX represents the transportation scheme for autonomous transportation orders across multiple vehicles. The initial solution is decoded at the initial temperature to obtain... ; In S2.1, the encoding process includes: S2.1.1 Based on the characteristics of the two key stages of the supply chain production and transportation processes, the solution... Solution from the production stage and the solution of the transportation phase The solutions for the production stage are represented by one-dimensional integer arrays, while the solutions for the transportation stage are represented by two one-dimensional integer arrays. The included order production sequence SC has a sequence length of . In SC, the numbers represent order numbers, and the number of times a number appears indicates the quantity of products required for that order; Solution It consists of two parts: the order shipping sequence (PC) and the list of vehicle numbers corresponding to the order (SX). The length of the sequence or list is [length missing]. PC and SX consist of autonomous transportation orders and vehicle numbers, respectively. Each column in the array represents the order with the corresponding number being transported by the vehicle with the corresponding number. The decoding process includes: S2.1.

2. The set of order processing sequences to be allocated to the production line for processing is generated from solution X1, i.e., a certain feasible scheduling scheme. , represented as ,in Indicates order In the production order set The Middle One is to carry out production. ,in ;Order Production end time is And orders production costs ; S2.1.

3. The solution X2 generates a set of orders allocated to different vehicles, representing a feasible transportation plan. , represented as ,in For the corresponding order The transport vehicle number, In this case, the shipping for the order will be outsourced; thus, the vehicle can be obtained. The collection of orders transported ,when Time order collection This is a collection of outsourced transportation orders; due to the capacity limitations of the vehicles themselves, the order collection... Delivery will be conducted in multiple batches; the rule to be met is: order collection. Internal orders are sorted according to their known production completion times. After sorting, vehicle transportation batches are determined based on capacity constraints. The division, that is, the vehicle No. The set of transport orders corresponding to this transport and set The total number of products required for internal orders must not exceed the vehicle's capacity limit. S2.1.4 When the order is shipped independently, the order... The start time of the transport is ;Order The end time of the transport is When the order In order collection When the first one is transported, that is At that time, the end time of transportation When orders In order collection When the middle is not the first to be transported, the transport end time is... Orders in this shipment For orders The previous transportation point, namely And meet the conditions When the order is outsourced for shipping, the order... The start time of the transport is ;Order The end time of the transport is ; S2.1.5, Orders at this time Transportation costs are divided into total self-operated transportation costs or total outsourced transportation costs; when At that time, orders The total transportation cost is the sum of the self-operated transportation cost and the arrival delay cost, of which the self-operated transportation cost is the sum of the self-operated transportation cost and the arrival delay cost. Delivery delay costs ;when At that time, orders The total cost of outsourced shipping is the sum of outsourced shipping costs and delivery delay costs, of which the order... Outsourced transportation costs Delivery delay costs ; S2.1.6 For vehicles transporting a single batch, the vehicle departure time is... Vehicle return time to factory ,in ; S2.1.7 Therefore, the total supply chain cost .

2. The supply chain scheduling optimization method based on transportation outsourcing and time window constraints as described in claim 1, characterized in that, Specifically, S2.3 includes: S2.3.1, Based on the initial solution and new interpretations Decode the total supply chain cost of the initial solution and the new solution, and denote the values ​​as follows: and ; S2.3.2, Calculation and Difference, i.e. And as an energy increment.

3. The supply chain scheduling optimization method based on transportation outsourcing and time window constraints as described in claim 2, characterized in that, S2.4 specifically includes: When the energy increment is not less than 0, calculate the probability of receiving the new solution. The probability of receiving is expressed as follows: ; Generate random numbers , ,when If the current solution is not updated, then the new solution is updated to the current solution; otherwise, no update is made, the new solution is not accepted, and the process returns to S2.

2.

4. The supply chain scheduling optimization method based on transportation outsourcing and time window constraints as described in claim 1, characterized in that, Specifically, S2.5 includes: For the current solution In the selected neighborhood structure The operation is performed within the stage so that the initial solution of this stage can jump to another solution in the feasible region, thereby avoiding repeated iterations under the same solution and preventing the algorithm from getting trapped in a local optimum.

5. The supply chain scheduling optimization method based on transportation outsourcing and time window constraints as described in claim 1, characterized in that, Specifically, S2.6 includes: S2.6.1 Neighborhood Structure of Variable Neighborhood Search Algorithm In the algorithm parameter setting stage, the initial selection probability of each neighborhood structure is set. Same, among which ; S2.6.2 During the local search process of the variable neighborhood search algorithm, record the number of times each neighborhood structure successfully finds a local optimum. and the number of failures Update the selection probability of each neighborhood according to the following formula: ,in Representing neighborhood structure The local search success rate is calculated using the following formula: , It is a sufficiently small positive number; S2.6.3, Define a variable exist Generate a random number within the specified interval and assign that random number to the variable. Determine the variable If the interval belongs to, Then choose neighborhood structure As a type of neighborhood search.

6. A supply chain scheduling optimization system based on transportation outsourcing and time window constraints, characterized in that, The supply chain scheduling optimization system is applied to the supply chain scheduling optimization method based on transportation outsourcing and time window constraints as described in any one of claims 1-5. The supply chain scheduling optimization system includes: an input parameter acquisition module and a solution module. The input parameter acquisition module is used to set the input parameters of the simulated annealing algorithm based on neighborhood search, according to customer orders, production process, order data during transportation, production line data, transportation vehicle data, and transportation outsourcing cost data. The solution module is used to obtain the global optimal solution based on the input parameters and the simulated annealing algorithm based on neighborhood search, and based on the global optimal solution, obtain the minimum total cost of the supply chain, the optimal single-machine scheduling scheme for the production process, and the optimal order transportation scheme.

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