A method for optimizing workshop material distribution path

Abstract

The present invention relates to a method for optimizing workshop material distribution paths. Aiming at workshop material distribution, the present invention takes into account the urgency of material demand and carbon emissions at workstations while ensuring accuracy and timeliness, and establishes a mathematical model with the minimum number of vehicles used and the shortest vehicle driving distance as the optimization goals. On this basis, combined with the characteristics of material distribution, improvements are made on the basis of the traditional ant colony algorithm, and the ant colony state transfer rules are improved, so that the ant colony considers the urgency of material demand and carbon emissions at the workstation when selecting the next point, which is more in line with actual production conditions. In terms of updating pheromones, a new updating method is proposed to improve the algorithm's solution speed.

Description

Technical Field

[0001] The invention belongs to the field of production and manufacturing, and specifically relates to a method for optimizing a workshop material distribution path. Background Art

[0002] With the development of production technology and the deepening of economic globalization, the competition among manufacturing enterprises is becoming increasingly fierce. Enterprises have been close to saturation in their desire to earn profits by saving resources and improving labor productivity, so the cost of material distribution has gradually become the focus of attention of enterprises. Logistics has an important impact on manufacturing enterprises. Logistics costs largely determine the production costs of enterprises. The increase in logistics costs leads to a corresponding increase in production costs. Because material distribution costs account for a large part of the logistics costs of enterprises, how to effectively reduce the material distribution costs of enterprises and improve the production efficiency of enterprises is a problem currently faced by manufacturing enterprises.

[0003] The workshop material distribution problem refers to the activity of using appropriate distribution methods or tools to deliver raw materials, purchased parts, components, etc. from the material storage area to the assembly line demand point and conduct handover activities under the premise of considering accuracy and timeliness, through a series of sorting, material preparation, loading and unloading, and handling operations. Existing statistics show that the cost of production logistics has reached 20%-50% of the total production cost, accounting for most of the cost of the production process. Therefore, how to reduce the production logistics cost is of great significance to the efficient production of enterprises.

[0004] Most of the current related research focuses on the shortest delivery time, the shortest delivery route, the least delivery vehicles, etc., and rarely considers the urgency of material demand at each workstation and the carbon emissions of small vehicles during transportation. However, in actual production, the production time of each workstation is often different, resulting in different demands for materials at each workstation. If the workstation with urgent material demand does not receive material delivery in time, it will also affect the overall production and manufacturing. With the continuous deepening of the low-carbon production concept in the manufacturing industry, green manufacturing should also be considered while considering production efficiency. Therefore, it is very meaningful to consider the carbon emissions of small vehicles during material distribution. Summary of the invention

[0005] The purpose of the present invention is to provide a workshop material distribution path optimization method for the current workshop material distribution, and the method is aimed at workshop material distribution.

[0006] To achieve the above-mentioned purpose, the technical solution of the present invention is: a method for optimizing the material distribution path in a workshop, which takes into account the urgency of material demand and carbon emissions at the workstation while ensuring accuracy and timeliness, and establishes a mathematical model with the minimum number of vehicles used and the shortest vehicle driving distance as the optimization goals. On this basis, combined with the characteristics of material distribution, improvements are made on the basis of the ant colony algorithm to improve the state transition probability, so that the ant colony considers the urgency of material demand and carbon emissions at the workstation when selecting the next point, which is more in line with actual production conditions; in terms of updating pheromones, a new updating method is proposed to improve the solution speed of the algorithm.

[0007] In one embodiment of the present invention, under the premise of ensuring accuracy and timeliness, the specific implementation process of establishing a mathematical model taking into account the urgency of material demand and carbon emissions at the workstation and taking the minimum number of vehicles used and the shortest vehicle travel distance as the optimization goal is as follows:

[0008] Assume that the following conditions are met in workshop material distribution:

[0009] (1) Vehicle capacity constraint, that is, the delivery capacity of each delivery route cannot be greater than the vehicle’s own capacity constraint;

[0010] (2) Each workstation is only allowed to be served by one delivery vehicle;

[0011] (3) Ignoring abnormal factors and external forces encountered by delivery vehicles during transportation and assembly at workstations;

[0012] (4) All vehicles depart from the same distribution center for delivery and must return to the distribution center after completing the delivery task;

[0013] Set the following parameters:

[0014] i, j: i, j∈V, V={0,1,...,n}, represents the set of demand points. When i or j is 0, it represents the distribution center;

[0015] k: vehicle number (k=1,2,...m);

[0016] d ij : represents the distance between workstation i and workstation j;

[0017] Q k : The maximum load of vehicle k;

[0018] q i : The demand for the i-th workstation;

[0019] T ei : The earliest time that the vehicle arrives at demand point i;

[0020] Tli : The latest time that the vehicle arrives at demand point i;

[0021] T ik : The actual time when the vehicle arrives at the demand point i;

[0022]

[0023]

[0024] Taking the minimum vehicle usage and the shortest total vehicle travel distance as the optimization goal, the mathematical model of workshop material distribution is established as follows:

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031] T ik ∈[T ei ,T li ] (7)

[0032] Formula (1) indicates that the minimum cost of the objective function = 1000 * the number of vehicles used + the total distance traveled by the vehicles; Formula (2) indicates that the delivery vehicle can only serve one work station once; Formula (3) indicates that the sum of the material quantities required by all demand points on each path cannot exceed the loading capacity of the delivery vehicle; Formula (4) indicates that the number of vehicles arriving at each demand point is the same as the number of vehicles leaving; Formula (5) indicates that each demand point is delivered by one vehicle; Formula (6) indicates that the delivery vehicle starts from the distribution center and must return to the distribution center after completing the delivery task; Formula (7) indicates that the delivery time of the vehicle must be within the specified time.

[0033] In one embodiment of the present invention, the state transition probability improvement process of the ant colony algorithm is as follows:

[0034] The state transition probability formula of the ant colony algorithm is:

[0035]

[0036] i, j represent the starting point and the end point respectively, η ij =1 / d ij represents the heuristic factor, which is the inverse of the distance between two points i and j, τ ij(t) represents the pheromone intensity from i to j at time t, allowed k is the set of nodes that have not been visited, α, β are the weighted values ​​of pheromone and heuristic factor;

[0037] Taking into account the impact of the urgency of material demand at the workstation and the carbon emissions of the vehicle, the improved state transition probability formula is:

[0038]

[0039] in It represents the inverse of the material demand time difference from point i to point j, λ ij (t) = 1 / u ij ,u ij represents the carbon emissions of the car from point i to point j, γ and μ are and λ ij The weighted value of (t);

[0040] The improved state transition probability formula delivery cart will give priority to workstations with urgent material needs, avoiding the phenomenon of workstation shutdown due to untimely material delivery, reducing carbon emissions during transportation, and achieving green production.

[0041] In one embodiment of the present invention, the pheromone update method of the ant colony algorithm is improved as follows:

[0042] The pheromone update formula of the ant colony algorithm is:

[0043] τ ij (t+1)=(1-ρ)·τ ij +ρ·Δτ ij (t) (10)

[0044] Where ρ is the pheromone volatilization coefficient. The larger ρ is, the faster the pheromone volatilizes.

[0045]

[0046] in It represents the pheromone left by ant k on the path from point i to point j in a cycle, and its expression is:

[0047]

[0048] Where Q is the pheromone strength, l k is the distance traveled by ant k in one cycle;

[0049] The improved pheromone update method is expressed as follows:

[0050]

[0051] Among them, best l is the optimal path currently found, l k Represents the path value found by the current ant k;

[0052] The improved pheromone update method can rapidly increase the amount of pheromone on excellent paths, while the amount of pheromone on non-excellent paths does not increase significantly. After multiple iterations, the amount of pheromone on excellent paths will be different from that on poor paths, which will speed up path optimization and help accelerate the convergence of the algorithm.

[0053] Compared with the prior art, the present invention has the following beneficial effects: for workshop material distribution, the present invention takes into account the urgency of material demand at the workstation and the carbon emissions of small vehicles under the premise of ensuring accuracy and timeliness, and establishes a mathematical model with the minimum number of vehicles used and the shortest vehicle driving distance as the optimization goal. On this basis, combined with the characteristics of material distribution, the traditional ant colony algorithm is improved, and the ant colony state transfer rule is improved, so that the ant colony considers the urgency of material demand at the workstation and the carbon emissions of small vehicles when selecting the next point, which is more in line with the actual production situation. In terms of updating pheromones, a new update method is proposed to improve the solution speed of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the workshop material distribution process.

[0055] Figure 2 This is the principle diagram of the ant colony algorithm.

[0056] Figure 3 Flowchart for solving the workshop distribution problem using the ant colony algorithm.

[0057] Figure 4 It is the workstation information.

[0058] Figure 5 It is a distribution solution for traditional ant colony algorithm.

[0059] Figure 6 It is the minimum cost graph of the traditional ant colony algorithm.

[0060] Figure 7 To improve the ant colony algorithm distribution solution.

[0061] Figure 8 To improve the minimum cost graph of ant colony algorithm. DETAILED DESCRIPTION

[0062] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0063] The present invention provides a workshop material distribution path optimization method. Under the premise of ensuring accuracy and timeliness, the urgency of workstation material demand and carbon emissions are considered, and a mathematical model is established with the minimum number of vehicles used and the shortest vehicle driving distance as the optimization goals. On this basis, combined with the characteristics of material distribution, improvements are made on the basis of the ant colony algorithm to improve the state transition probability, so that the ant colony considers the urgency of workstation material demand and carbon emissions when selecting the next point, which is more in line with actual production conditions. In terms of updating pheromones, a new updating method is proposed to improve the solution speed of the algorithm.

[0064] The following is the specific implementation process of the present invention.

[0065] Description and mathematical model of workshop delivery problem

[0066] 1. Problem description

[0067] The workshop material distribution problem studied refers to the reasonable planning of the driving route of the vehicle transporting materials from the distribution center to the demand point workstation, so that the vehicle arrives at the demand point in sequence according to the planned route and leaves until it finally returns to the distribution center. The purpose is to achieve certain distribution goals (including minimum distribution cost, minimum distribution time, shortest distribution distance, etc.). In this process, certain conditional constraints must be met (including demand point demand, material demand time window, and vehicle driving distance constraints, etc.).

[0068] Figure 1 This is a flow chart of workshop material distribution. After several carts receive the materials needed by each workstation from the distribution center, they are transported and distributed to each workstation to ensure the punctuality of material arrival, the accuracy of material quantity and the guarantee of distribution quality. On this basis, the present invention takes into account the different urgency of the needs of each workstation and the carbon emissions of the carts, ensuring that the workstations with urgent material needs can be distributed first and carbon emissions are taken into account to reduce the overall transportation cost. The present invention has the following assumptions in the study of workshop material distribution:

[0069] (1) Vehicle capacity (load, volume, etc.) constraints, that is, the delivery capacity of each delivery route cannot be greater than the vehicle’s own capacity (load, volume, etc.) constraints.

[0070] (2) Only one delivery vehicle is allowed to serve each workstation.

[0071] (3) Ignore abnormal factors and external forces such as workshop congestion encountered by delivery vehicles during transportation and assembly at workstations.

[0072] (4) All vehicles depart from the same distribution center for delivery and must return to the distribution center after completing the delivery mission.

[0073] 2. Mathematical model

[0074] 1) Parameters:

[0075] i, j: i, j∈V, V={0,1,...,n}, represents the set of demand points. When i or j is 0, it represents the distribution center;

[0076] k: vehicle number (k=1,2,...m);

[0077] d ij : represents the distance between workstation i and workstation j;

[0078] Q k : The maximum load of vehicle k;

[0079] q i : The demand for the i-th workstation;

[0080] T ei : The earliest time that the vehicle arrives at demand point i;

[0081] T li : The latest time that the vehicle arrives at demand point i;

[0082] T ik : The actual time when the vehicle arrives at the demand point i;

[0083]

[0084]

[0085] 2) Model:

[0086] Taking the minimum vehicle usage and the shortest total vehicle travel distance as the optimization goal, the mathematical model of workshop material distribution is established as follows:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] T ik ∈[T ei ,T li ] (7)

[0094] Formula (1) indicates that the minimum cost of the objective function = 1000 * the number of vehicles used + the total distance traveled by the vehicles; Formula (2) indicates that the delivery vehicle can only serve one work station once; Formula (3) indicates that the sum of the material quantities required by all demand points on each path cannot exceed the loading capacity of the delivery vehicle; Formula (4) indicates that the number of vehicles arriving at each demand point is the same as the number of vehicles leaving; Formula (5) indicates that each demand point is delivered by one vehicle; Formula (6) indicates that the delivery vehicle starts from the distribution center and must return to the distribution center after completing the delivery task; Formula (7) indicates that the delivery time of the vehicle must be within the specified time.

[0095] 2. Ant Colony Algorithm

[0096] The Ant Colony Algorithm was first proposed by Italian scholar M. Dorigo and others in 1992. It is a bionic optimization algorithm that simulates the foraging behavior of real ant colonies in nature. It is essentially a meta-heuristic global optimization algorithm in evolutionary algorithms, with the characteristics of distributed computing, information positive feedback and heuristic search.

[0097] Ant colony algorithm principle diagram Figure 2 As shown in the figure, the optimization goal is to find the shortest distance from point A to point H. During the solution process, each ant independently searches for feasible solutions in the solution space and leaves pheromones. The better the solution, the more pheromones are left. As the algorithm progresses, the pheromones on the better solution path increase, and the number of ants choosing it also increases. The pheromones on other paths will begin to evaporate over time, and finally the optimal solution is obtained, that is, the path ABDEGH.

[0098] Figure 3 This is the flow chart of the ant colony algorithm solving the workshop distribution problem. It is mainly divided into two steps. The first step is to build the ant colony algorithm search space for solving the vehicle routing problem with time window material distribution. The second step is for ants to plan the path according to the search mechanism rules. After each ant traverses all demand nodes, it updates the optimal solution set according to the constraints and updates the current search environment pheromones.

[0099] 3. Improved ant colony algorithm

[0100] 3.1. Improving state transition probability

[0101] The state transition probability of the traditional ant colony algorithm only considers the influence of factors such as visibility and pheromone intensity, and does not consider the influence of actual parameters when applied to workshop material distribution. In order to better consider the urgency of material demand at each workstation in the workshop and the carbon emissions of small vehicles during the distribution process, these two parameters are added to the state transition probability formula of the ant colony algorithm, making the algorithm more considerate of the actual situation.

[0102] The traditional ant colony algorithm state transition probability formula is:

[0103]

[0104] i, j represent the starting point and the end point respectively, η ij =1 / d ij represents the heuristic factor, which is the inverse of the distance between two points i and j, τ ij (t) represents the pheromone intensity from i to j at time t, allowed k is the set of nodes that have not been visited, α, β are the weighted values ​​of pheromone and heuristic factor;

[0105] In order to take into account the urgency of the workstation's material demand and the impact of the car's carbon emissions, the improved state transition probability formula is:

[0106]

[0107] in It represents the inverse of the material demand time difference from point i to point j, λ ij (t) = 1 / u ij ,u ij represents the carbon emissions of the car from point i to point j, γ and μ are and λ ij The weighted value of (t);

[0108] The improved state transition probability formula delivery cart will give priority to workstations with urgent material needs, avoiding the phenomenon of workstation shutdown due to untimely material delivery, reducing carbon emissions during transportation, and achieving green production.

[0109] 3.2. Improved pheromone update method

[0110] In order to avoid excessive pheromone residues that obscure the heuristic information, the residual information must be updated after each ant passes through a cycle.

[0111] The traditional ant colony algorithm pheromone update formula is:

[0112] τ ij (t+1)=(1-ρ)·τ ij +ρ·Δτ ij (t) (10)

[0113] Where ρ is the pheromone volatilization coefficient. The larger ρ is, the faster the pheromone volatilizes.

[0114]

[0115] in It represents the pheromone left by ant k on the path from point i to point j in a cycle, and its expression is:

[0116]

[0117] Where Q is the pheromone strength, l k is the distance traveled by ant k in one cycle;

[0118] The difference in pheromone quantity on the path of the traditional pheromone updating method is not very obvious, which makes the convergence speed of the algorithm slow. Therefore, an improved pheromone updating method is proposed, and its expression is:

[0119]

[0120] Among them, best l is the optimal path currently found, l k Represents the path value found by the current ant k;

[0121] The improved pheromone update method can rapidly increase the amount of pheromone on excellent paths, while the amount of pheromone on non-excellent paths does not increase significantly. After multiple iterations, the amount of pheromone on excellent paths will be different from that on poor paths, which will speed up path optimization and help accelerate the convergence of the algorithm.

[0122] Example

[0123] A production line has 25 workstations, 1 distribution center, and a trolley load of 200. The data of each workstation is as follows: Figure 4 shown.

[0124] Ant colony algorithm and improved ant colony algorithm were used to carry out simulation experiments on workshop material distribution, and the initialization parameters were set as 50 ants, α as 1, β as 3, γ as 2, μ as 3, ρ as 0.8, Q as 5, and the number of iterations as 30.

[0125] The results of the traditional ant colony algorithm are as follows Figure 5 , Figure 6 As shown in Figure 2, the results of the improved ant colony algorithm are as follows: Figure 7 , Figure 8As shown. By comparing the traditional ant colony algorithm and the improved ant colony algorithm, it can be concluded that the improved ant colony algorithm reduces the overall workshop material distribution cost. Before the improvement, four carts were needed for distribution, and the distribution plans were 0-23-18-17-0, 0-16-24-25-20-21-19-6-4-1-0, 0-5-3-7-8-2-0, and 0-9-13-14-15-11-12-10-22-0. The improved distribution plan only requires three carts for distribution, and the distribution plans are 0-16-24-25-8-20-21-19-6-4-2-1-0, 0-5-3-7-15-11-12-10-22-18-17-0, and 0-9-13-14-23-0. The reduction in the number of distribution carts greatly reduces the total cost price. Figure 6 and Figure 8 It can be seen that the total cost before the improvement is about 5300, and the cost after the improvement is about 3200. From the comparison, it can be seen that the final cost of the distribution plan obtained using the improved ant colony algorithm will be lower than that before the improvement.

[0126] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A workshop material distribution path optimization method, characterized in that: Under the premise of ensuring accuracy and timeliness, the urgency of material demand and carbon emissions of the workstation are considered, and a mathematical model is established with the minimum number of vehicles used and the shortest vehicle driving distance as the optimization goal. On this basis, combined with the characteristics of material distribution, improvements are made on the basis of the ant colony algorithm to improve the state transition probability, so that the ant colony considers the urgency of material demand and carbon emissions of the workstation when selecting the next point, which is more in line with the actual production situation; in terms of updating pheromones, a new updating method is proposed to improve the solution speed of the algorithm; Under the premise of ensuring accuracy and timeliness, the specific implementation process of establishing a mathematical model taking into account the urgency of material demand and carbon emissions at the workstation and taking the minimum number of vehicles used and the shortest vehicle travel distance as the optimization goal is as follows: Assume that the following conditions are met in workshop material distribution: (1) Vehicle capacity constraint, that is, the delivery capacity of each delivery route cannot be greater than the vehicle’s own capacity constraint; (2) Each workstation is only allowed to be served by one delivery vehicle; (3) Ignoring abnormal factors and external forces encountered by delivery vehicles during transportation and assembly at workstations; (4) All vehicles depart from the same distribution center for delivery and must return to the distribution center after completing the delivery task; Set the following parameters: i, j: i, j∈V, V={0,1,...,n}, represents the set of demand points. When i or j is 0, it represents the distribution center; k: vehicle number (k=1,2,...m); d ij : represents the distance between workstation i and workstation j; Q k : The maximum load of vehicle k; q i : The demand for the i-th workstation; T ei : The earliest time that the vehicle arrives at demand point i; T li : The latest time that the vehicle arrives at demand point i; T ik : The actual time when the vehicle arrives at the demand point i; Taking the minimum vehicle usage and the shortest total vehicle travel distance as the optimization goal, the mathematical model of workshop material distribution is established as follows: T ik ∈[T ei ,T li ](7) Formula (1) indicates that the minimum cost of the objective function = 1000 * the number of vehicles used + the total distance traveled by the vehicles; Formula (2) indicates that the delivery vehicle can only serve one workstation once; Formula (3) indicates that the sum of the material quantities required by all demand points on each path cannot exceed the loading capacity of the delivery vehicle; Formula (4) indicates that the number of vehicles arriving at each demand point is the same as the number of vehicles leaving; Formula (5) indicates that each demand point is delivered by one vehicle; Formula (6) indicates that the delivery vehicle starts from the distribution center and must return to the distribution center after completing the delivery task; Formula (7) indicates that the delivery time of the vehicle must be within the specified time; The state transition probability improvement process of the ant colony algorithm is as follows: The state transition probability formula of the ant colony algorithm is: i, j represent the starting point and the end point respectively, η ij =1 / d ij represents the heuristic factor, which is the inverse of the distance between two points i and j, τ ij (t) represents the pheromone intensity from i to j at time t, allowed k is the set of nodes that have not been visited, α, β are the weighted values ​​of pheromone and heuristic factor; Taking into account the impact of the urgency of material demand at the workstation and the carbon emissions of the vehicle, the improved state transition probability formula is: in It represents the inverse of the material demand time difference from point i to point j, λ ij (t) = 1 / u ij ,u ij represents the carbon emissions of the car from point i to point j, γ and μ are and λ ij The weighted value of (t); The improved state transition probability formula delivery cart will give priority to workstations with urgent material needs, avoiding the phenomenon of workstation shutdown due to untimely material delivery, reducing carbon emissions during transportation, and achieving green production.

2. A workshop material distribution path optimization method according to claim 1, characterized in that: The improvement process of the pheromone update method of the ant colony algorithm is as follows: The pheromone update formula of the ant colony algorithm is: t ij (t+1)=(1-ρ)·τ ij +ρ·Δτ ij (t) (10) Where ρ is the pheromone volatilization coefficient. The larger ρ is, the faster the pheromone volatilizes. in It represents the pheromone left by ant k on the path from point i to point j in a cycle, and its expression is: Where Q is the pheromone strength, l k is the distance traveled by ant k in one cycle; The improved pheromone update method is expressed as follows: Among them, best l is the optimal path currently found, l k Represents the path value found by the current ant k; The improved pheromone update method can rapidly increase the amount of pheromone on excellent paths, while the amount of pheromone on non-excellent paths does not increase significantly. After multiple iterations, the amount of pheromone on excellent paths will be different from that on poor paths, which will speed up path optimization and help accelerate the convergence of the algorithm.

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