Printed circuit board production workshop distribution route optimization method

Abstract

The invention provides a printed circuit board production workshop distribution route optimization method, which comprises the following steps: firstly, simulating workshop layout in detail through three-dimensional modeling, collecting real-time production data, docking with ERP (Enterprise Resource Planning) and MES (Manufacturing Execution System) data, analyzing a yield fluctuation rule, formulating a batch distribution plan and priority according to the characteristics of a printed circuit board, and optimizing the distribution route of the printed circuit board production workshop. A special container and a distribution scheme are designed to meet the storage and transportation requirements of different types of printed circuit boards, in a three-dimensional logistics network graph construction stage, specific paths, elevators and stair factors in a workshop are combined, an ant colony algorithm is combined with tabu search and a genetic algorithm, environment constraint conditions are innovatively brought into an optimization target, and a three-dimensional logistics network graph is constructed. An optimal distribution route meeting all constraints is found through a time window model and actual distribution conditions, an intelligent distribution module generates and updates an optimal route map in real time, the execution condition is monitored through the Internet of Things technology, and data is fed back in real time for dynamic adjustment and optimization.

Description

Technical Field

[0001] The present invention relates to the field of distribution route optimization, and particularly to an optimization method for the distribution route in a printed circuit board production workshop. Background Art

[0002] Currently, in modern manufacturing industries, especially in the field of electronic manufacturing, the production and distribution process management of printed circuit boards (PCBs) faces numerous challenges. With the development trend of intelligent manufacturing, the requirements for production efficiency, cost control, and material flow accuracy are increasing day by day. The traditional distribution methods of printed circuit boards often have problems such as unreasonable route planning, slow response speed, and difficulty in coping with production volume fluctuations, and these problems directly affect the overall efficiency of the production line and product quality.

[0003] The progress of the Internet of Things (IoT) technology makes it possible to obtain and analyze various data on the production site in real time, including but not limited to information such as inventory levels, production progress, and equipment status. These information can achieve full-process digital control from order to production through effective integration with the enterprise resource planning (ERP) system and the manufacturing execution system (MES).

[0004] At the same time, advanced optimization algorithms, such as Ant Colony Optimization, Tabu Search, Genetic Algorithm, etc., show strong advantages in solving complex logistics route planning problems. These algorithms can comprehensively consider various factors, such as distribution time windows, product attributes such as different types of printed circuit boards and their special storage and transportation requirements, environmental condition restrictions such as temperature and humidity control, anti-static measures, etc., so as to find the optimal or approximately optimal distribution strategy. Summary of the Invention

[0005] The present invention aims to provide an optimization method for the distribution route in a printed circuit board production workshop.

[0006] The problems to be solved by the present invention are as follows: Due to the lack of refined logistics distribution planning, it may lead to long and time-consuming material handling paths, unable to quickly respond to changes in production demand, affecting the production rhythm and overall production capacity; inaccurate prediction of the demand for each batch of printed circuit boards, making it difficult to achieve timely and appropriate material distribution, which may lead to production interruptions or inventory backlogs; ignoring environmental factors such as temperature and humidity control and anti-static in the distribution process may cause damage to printed circuit boards and affect product quality; in an environment with obvious changes between peak and off-peak production periods, there is a lack of an effective dynamic scheduling mechanism, unable to flexibly adjust the distribution plan to adapt to changes in the production rhythm. In summary, this method is committed to solving multiple core problems such as the efficiency optimization of logistics distribution in the printed circuit board production workshop, material demand prediction and matching, environmental factor control, reasonable resource allocation, and dynamic scheduling, in order to improve production efficiency, reduce costs, ensure product quality, and enhance the level of supply chain management.

[0007] An optimization method for the distribution route in a printed circuit board production workshop, the technical solution adopted is as follows: S1: Create a three-dimensional solid model of the production workshop, identify the location information of production equipment, material storage areas, turnover box storage areas, and finished product areas, establish a coordinate system, mark the installation positions of Internet of Things sensors, RFID tags, and card readers in the three-dimensional model, and collect real-time data on material consumption, output speed, and equipment status of the production line according to the production process of printed circuit boards. S2: Integrate the data of the ERP and MES systems, obtain the real-time production schedule and historical data, conduct time series analysis on the historical production data, statistically analyze the output fluctuations on a daily, weekly, and monthly basis, and obtain the peak and off-peak production periods. S3: Statistically analyze the batch demand for each type of printed circuit board, formulate a batch-by-batch distribution list, set priorities according to the urgency, define container standards for different distribution tasks, including pallets and boxes, and match appropriate handling equipment and load capacities. For printed circuit boards with special storage and transportation requirements, design customized distribution plans. For components sensitive to temperature and humidity, temperature control boxes and desiccants need to be configured when designing the distribution plan. S4: Measure and record the actual path length, turning radius, and passage restrictions inside the workshop, and construct a three-dimensional logistics network diagram including elements such as elevators and stairs. S5: Add environmental factor constraint conditions to the ant colony algorithm, including temperature and humidity control and anti-static requirements, apply a hybrid optimization strategy of the ant colony algorithm, tabu search, and genetic algorithm, and combine the time window model and actual distribution conditions to obtain the optimal distribution route that meets all constraint conditions. S6: Develop an intelligent distribution route optimization module, integrate the optimized ant colony algorithm, generate and update the optimal distribution route map in real time, organize staff training, implement Internet of Things technology in actual operations, monitor the distribution execution situation in real time, record data and feedback it to the intelligent scheduling system for dynamic adjustment and optimization; S7: Dynamically adjust the distribution plan according to the real-time monitoring data, including routes and frequencies, and update it to the intelligent scheduling system in real time. Regularly evaluate the accuracy of the prediction model, continuously iterate and optimize the model parameters and algorithm strategies, and integrate its path planning into the overall distribution system.

[0008] Furthermore, in S3, define container standards for different distribution tasks, including: Select a suitable container type according to the size, weight, shape, and quantity of the printed circuit board. Select a container with corresponding characteristics according to the particularity of the printed circuit board, including anti-static, moisture-proof, and shock-proof functions. Determine the material, structure, and load limit of the container according to the total weight of the printed circuit board and the load-bearing capacity of the handling equipment.

[0009] Furthermore, in S4, construct a three-dimensional logistics network diagram containing elevator and stair elements, including: Construct a three-dimensional logistics network diagram containing elevator and stair elements according to the three-dimensional solid model of the workshop created in S1, combined with the position, entrance and exit, passing direction, and passenger capacity information of the elevator and stair; Regard the roads, corridors, and passages inside the workshop as curve nodes in a two-dimensional network, and the elevators and stairs as vertical transition nodes in a three-dimensional network. Each node has attributes such as walking speed limit and turning radius; Link the plane path nodes with the elevator and stair nodes to form a three-dimensional logistics network. Each "edge" represents a potential transportation path, with corresponding attributes attached, including length, passing time, and whether two-way passing is allowed; Convert environmental factors such as temperature and humidity control and anti-static requirements into additional attributes of nodes and edges. For areas with temperature and humidity requirements that are not suitable for long-term stay, increase the passing time penalty. For areas with high anti-static requirements, select specific anti-static handling tools when passing through these nodes.

[0010] Furthermore, the optimal distribution route that meets all constraint conditions obtained in S5 includes: S51: According to the three-dimensional logistics network diagram constructed in S4, assign costs to each node and path according to the actual situation, including walking time, operation cost, and additional environmental factor constraint weights, including temperature sensitivity and anti-static requirement level; S52: The walking time cost is usually determined based on the actually measured length of the internal roads in the workshop, the passing time of elevators and stairs. For each connection node in the path, the time cost is calculated by computing the straight-line distance between the nodes and multiplying it by the walking time coefficient per unit distance. When there are elevators and stairs, the waiting and riding times are incorporated. Let d_ij be the physical distance from node i to node j, t_walk be the walking time coefficient per unit distance, t_wait_elevator and t_ride_elevator be the average waiting time and riding time of the elevator respectively. Then the time cost TC_ij from node i to node j is expressed as ; The operation cost covers the time and labor costs required for handling and loading / unloading operations, and is assigned according to the actual data; For environmental factors such as temperature and humidity control and anti-static requirements, corresponding constraint indicators are set up and converted into weights. Given an environmental factor E1, when node i is in a high-temperature sensitive anti-static area, an additional environmental constraint weight EC_weight_i is assigned to it. When this path is selected, a penalty of this weight value is added to the original path cost; S53: Initialize a group of ants. Each ant represents a possible delivery route. Each ant randomly selects a starting point and starts moving in the logistics network, and selects the next node based on heuristic information such as path length and environmental constraint factors; S54: The probability p of selecting node j , where τ_ij is the pheromone concentration from node i to node j, τ_ik is the pheromone concentration from node i to node k, η is the pheromone evaporation factor, Δ(t) is the pheromone update gain, α, β and γ are weight parameters, EC_weight_j is the environmental constraint weight of node j, and EC_weight_k is the environmental constraint weight of node k; During the transfer process of the ants, the pheromone concentration is used to determine the next move. When each ant makes a decision on the path, it checks whether the current selection meets the environmental factor constraints. If not, it reselects. When the ants search to a certain stage, a tabu search strategy is introduced. A tabu list of a certain size is set up to record the node combinations visited recently, to avoid repeating the selection of the same or similar path segments in a short time. After multiple iteration cycles, the excellent solutions in the group are integrated, and a genetic algorithm is used for population evolution. Finally, the optimal delivery route is obtained from all the iterations, which is the optimal solution that meets all the constraint conditions.

[0011] The beneficial effects of the present invention: By performing three-dimensional modeling on the production workshop and precisely arranging Internet of Things devices such as sensors and RFID, real-time collection and analysis of production data are carried out to achieve accurate control of material consumption, output speed and equipment status; By integrating the data of ERP and MES systems, analyzing the historical production output fluctuations, formulating a batch-by-batch distribution list adapted to demand changes according to the real-time production schedule, and combining with the characteristics of various printed circuit boards, designing a customized distribution plan to improve the overall distribution efficiency; Construct a three-dimensional logistics network diagram containing elements such as elevators and stairs. Based on this model, use a hybrid optimization strategy of ant colony algorithm, tabu search, and genetic algorithm to solve complex spatial path problems and achieve comprehensive optimization of multi-dimensional constraint conditions such as walking time, operation cost, and environmental factors; The intelligent distribution route optimization module can generate and update the optimal distribution route map in real time, and dynamically adjust the distribution plan according to the real-time monitoring data. This method is conducive to quickly responding to production changes, reducing ineffective labor and delays, and ensuring the safety and quality of special printed circuit boards during the distribution process; By incorporating environmental factors such as temperature and humidity control and anti-static requirements into the cost function, ensuring strict compliance with process environment requirements during the distribution process, and reducing the risk of product damage caused by environmental factors; Scientifically select the container type and handling equipment according to the characteristics of the printed circuit board, which not only meets the production and logistics requirements, but also saves costs and improves the rationality of resource allocation. Brief Description of the Drawings

[0012] Figure 1 It is a flowchart of an optimization method for the distribution route in a printed circuit board production workshop. Detailed Embodiments

[0013] The following further clearly and completely describes the present invention, but the protection scope of the present invention is not limited thereto.

[0014] An optimization method for the distribution route in a printed circuit board production workshop, and the technical solutions adopted are as follows: S1: Create a three-dimensional solid model of the production workshop, identify the location information of production equipment, material storage areas, turnover box storage areas, and finished product areas, establish a coordinate system, mark the installation positions of Internet of Things sensors, RFID tags, and card readers in the three-dimensional model, and collect the material consumption, output speed, and equipment status data of the production line in real time according to the production process of the printed circuit board; S2: Integrate the data of the ERP and MES systems, obtain the real-time production schedule and historical data, perform time series analysis on the historical production data, count the production output fluctuations on a daily, weekly, and monthly basis, and obtain the production peak and trough periods; S3: Statistically calculate the batch demand for each type of printed circuit board, formulate a batch-by-batch distribution list, set priorities based on the urgency level, define container standards for different distribution tasks, including pallets and boxes, and match appropriate handling equipment and load capacities. For printed circuit boards with special storage and transportation requirements, design customized distribution plans. For components sensitive to temperature and humidity, configure temperature control boxes and desiccants when designing the distribution plan. S4: Measure and record the actual path lengths, turning radii, and passage restrictions inside the workshop, and construct a three-dimensional logistics network diagram that includes elevator and staircase elements. S5: Incorporate environmental factor constraint conditions into the ant colony algorithm, including temperature and humidity control and anti-static requirements. Apply a hybrid optimization strategy of the ant colony algorithm, tabu search, and genetic algorithm. Combine the time window model and actual distribution conditions to obtain the optimal distribution route that meets all constraint conditions. S6: Develop an intelligent distribution route optimization module, integrate the optimized ant colony algorithm, generate and update the optimal distribution route diagram in real time, organize employee training, implement Internet of Things technology in actual operations, monitor the distribution execution situation in real time, record data, and feedback it to the intelligent scheduling system for dynamic adjustment and optimization. S7: Dynamically adjust the distribution plan based on real-time monitoring data, including routes and frequencies, and update it to the intelligent scheduling system in real time. Regularly evaluate the accuracy of the prediction model, continuously iterate and optimize the model parameters and algorithm strategies, and integrate its path planning into the overall distribution system.

[0015] Reference Figure 1 As shown, it is a flowchart of a method for optimizing the distribution route in a printed circuit board production workshop.

[0016] Furthermore, in S3, defining container standards for different distribution tasks includes: Select a suitable container type according to the size, weight, shape, and quantity of the printed circuit board. Select a container with corresponding characteristics according to the particularity of the printed circuit board, including anti-static, moisture-proof, and shock-proof functions. Determine the material, structure, and load limit of the container according to the total weight of the printed circuit board and the load capacity of the handling equipment.

[0017] Furthermore, in S4, constructing a three-dimensional logistics network diagram that includes elevator and staircase elements includes: Based on the three-dimensional solid model of the workshop created in S1, combine the position, entrance and exit, passing direction, and passenger capacity information of the elevator and staircase to construct a three-dimensional logistics network diagram that includes elevator and staircase elements. Regard the roads, corridors, and passages inside the workshop as curve nodes in a two-dimensional network, and the elevator and staircase as vertical transition nodes in a three-dimensional network. Each node has attributes such as walking speed limit and turning radius. Link the planar path nodes with elevator and staircase nodes to form a three-dimensional logistics network. Each "edge" represents a potential transportation path with corresponding attributes, including length, travel time, and whether two-way passage is allowed. Convert environmental factors such as temperature and humidity control and anti-static requirements into additional attributes of nodes and edges. For areas with temperature and humidity requirements where long-term stay is not suitable, increase the travel time penalty. For areas with high anti-static requirements, select specific anti-static handling tools when passing through these nodes.

[0018] Furthermore, S5 obtains the optimal distribution route that satisfies all constraint conditions, including: S51: According to the three-dimensional logistics network diagram constructed in S4, assign costs to each node and path according to the actual situation, including travel time, operation cost, and additional environmental factor constraint weights, including temperature sensitivity and anti-static requirement level. The travel time cost is usually determined based on the actual measured length of the internal roads in the workshop, the travel time of elevators and staircases. For each connection node in the path, calculate the time cost by calculating the straight-line distance between nodes and multiplying it by the travel time coefficient per unit distance. When there are elevators and staircases, combine the waiting and riding times. Let d_ij be the physical distance from node i to node j, t_walk be the travel time coefficient per unit distance, t_wait_elevator and t_ride_elevator be the average waiting time and riding time of the elevator respectively. Then the time cost TC_ij from node i to node j is expressed as ; The operation cost covers the time and labor costs required for handling and loading / unloading operations, and is assigned according to actual data. For environmental factors such as temperature and humidity control and anti-static requirements, set corresponding constraint indicators and convert them into weights. Let there be an environmental factor E1. When node i is in a high-temperature sensitive anti-static area, assign it an additional environmental constraint weight EC_weight_i. When choosing this path, a penalty of this weight value will be added to the original path cost. S53: Initialize a group of ants. Each ant represents a possible distribution route. Each ant randomly selects a starting point and starts moving in the logistics network, and selects the next node based on heuristic information such as path length and environmental constraint factors. S54: The probability p of selecting node j, , where τ_ij is the pheromone concentration from node i to node j, τ_ik is the pheromone concentration from node i to node k, η is the pheromone evaporation factor, Δ(t) is the pheromone update gain, α, β, and γ are weight parameters, EC_weight_j is the environmental constraint weight of node j, and EC_weight_k is the environmental constraint weight of node k. S55: During the transfer process, ants use the concentration of pheromone to determine the next move. When each ant makes a decision on the path, it checks whether the current choice meets the constraints of environmental factors. If not, it makes a new choice. After the ants search to a certain stage, a tabu search strategy is introduced. A tabu list of a certain size is set up to record the node combinations visited recently, avoiding repeated selection of the same or similar path segments in a short time. After multiple iteration cycles, the excellent solutions in the population are integrated, and the genetic algorithm is used for population evolution. Finally, the optimal distribution route is obtained from all iterations, which is the optimal solution under all constraint conditions.

[0019] The present invention provides an optimization method for the distribution route in a printed circuit board production workshop. First, the workshop layout is detailedly simulated through 3D modeling, real-time production data is collected, and it is docked with the data of the ERP and MES systems. The law of output fluctuation is analyzed. According to the characteristics of the printed circuit board, a batch distribution plan and priorities are formulated. Special containers and distribution plans are designed to meet the storage and transportation requirements of different types of printed circuit boards. In the stage of constructing the 3D logistics network diagram, combined with the specific paths, elevators, and stair factors inside the workshop, the ant colony algorithm is combined with tabu search and genetic algorithm. Innovatively, the environmental constraint conditions are incorporated into the optimization objective. Through the time window model and actual distribution conditions, the optimal distribution route that meets all constraints is found. The intelligent distribution module generates and updates the optimal route map in real time, and monitors the execution situation through Internet of Things technology. The real-time feedback data is used for dynamic adjustment and optimization.

Claims

1. A method for optimizing the distribution route of a printed circuit board production workshop, characterized in that: include: S1: Create a three-dimensional model of the production workshop, mark the location information of production equipment, material storage area, turnover box storage area, and finished product area, establish a coordinate system, mark the installation location of IoT sensors, RFID tags, and card readers in the three-dimensional model, and collect material consumption, output speed, and equipment status data of the production line in real time according to the production process of printed circuit boards; S2: Integrate ERP and MES system data to obtain real-time production schedules and historical data, conduct time series analysis on historical production data, count daily, weekly, and monthly production fluctuations, and derive production peak and trough periods; S3: Count the batch demand for each type of printed circuit boards, prepare a batch delivery list, set priorities based on urgency, define container standards for different delivery tasks, including pallets and boxes, and match appropriate handling equipment and load capacity. Design customized delivery plans for printed circuit boards with special storage and transportation requirements, including temperature- and humidity-sensitive components. Temperature-controlled boxes and desiccants are required when designing delivery plans. S4: Measure and record the actual path length, turning radius and channel restrictions inside the workshop, and construct a three-dimensional logistics network diagram including elevator and stair elements; S5: Add environmental constraints to the ant colony algorithm, including temperature and humidity control and anti-static requirements, apply the ant colony algorithm with taboo search and genetic algorithm hybrid optimization strategy, combine the time window model and actual delivery conditions, and obtain the optimal delivery route that meets all constraints; S6: Develop an intelligent delivery route optimization module, integrate the optimized ant colony algorithm, generate and update the optimal delivery route map in real time, organize employee training, implement IoT technology in actual operations, monitor delivery execution in real time, record data and feed it back to the intelligent scheduling system for dynamic adjustment and optimization; S7: Dynamically adjust the delivery plan, including routes and frequencies, based on real-time monitoring data, and update it to the intelligent scheduling system in real time. Regularly evaluate the accuracy of the prediction model, continuously iterate and optimize the model parameters and algorithm strategies, and integrate its path planning into the overall delivery system.

2. A printed circuit board production workshop distribution route optimization method as claimed in claim 1, characterized in that: The S3 defines container standards for different delivery tasks, including: Select the appropriate container type based on the size, weight, shape and quantity of the printed circuit boards. Select containers with corresponding characteristics based on the special characteristics of the printed circuit boards, including anti-static, moisture-proof and shock-resistant functions. Determine the material, structure and load limit of the container based on the total weight of the printed circuit boards and the carrying capacity of the handling equipment.

3. A method for optimizing the distribution route of a printed circuit board production workshop as claimed in claim 1, characterized in that: The three-dimensional logistics network diagram including elevator and stair elements is constructed in S4, including: According to the three-dimensional model of the workshop created in S1, a three-dimensional logistics network diagram including elevator and stair elements is constructed by combining the location, entrance and exit, traffic direction and passenger capacity information of elevators and stairs; The roads, corridors, and passages inside the workshop are regarded as curve nodes in the two-dimensional network, and the elevators and stairs are regarded as vertical transition nodes in the three-dimensional network. Each node has attributes such as walking speed limit and turning radius. Link the plane path nodes with the elevator and stair nodes to form a three-dimensional logistics network. Each "edge" represents a potential transportation path with corresponding attributes, including length, travel time, and whether two-way traffic is allowed. The environmental factors of temperature and humidity control and anti-static requirements are converted into additional attributes of nodes and edges. For areas with high temperature and humidity requirements that are not suitable for long-term stay, the travel time penalty is increased. For areas with high anti-static requirements, specific anti-static handling tools are selected when passing through these nodes.

4. A method for optimizing the distribution route of a printed circuit board production workshop as claimed in claim 1, characterized in that: S5 obtains the optimal delivery route that satisfies all constraints, including: S51: Based on the three-dimensional logistics network diagram constructed in S4, the cost of each node and path is assigned according to the actual situation, including travel time, operation cost and additional environmental factor constraint weights, including temperature sensitivity and anti-static requirement level; S52: The walking time cost is usually determined based on the actual measured length of the workshop internal roads, the travel time of the elevators and stairs. For each connection node in the path, the time cost is calculated by calculating the straight-line distance between the nodes and multiplying it by the walking time coefficient per unit distance. When there are elevators and stairs, the waiting and riding time are combined. Let d_ij be the physical distance from node i to node j, t_walk be the walking time coefficient per unit distance, t_wait_elevator and t_ride_elevator are the average waiting time and riding time of the elevator respectively. Then the time cost TC_ij from node i to node j is expressed as ; Operation costs include the time and labor costs required for handling, loading and unloading operations, and are assigned based on actual data; For the environmental factors of temperature and humidity control and anti-static requirements, corresponding constraint indicators are set up and converted into weights. There is an environmental factor E1. When node i is in a high-temperature sensitive anti-static area, it is given an additional environmental constraint weight EC_weight_i. When this path is selected, the penalty of this weight value will be added to the original path cost. S53: Initialize a group of ants, each ant represents a possible delivery route, each ant randomly selects a starting point and starts moving in the logistics network, selecting the next node based on heuristic information such as path length and environmental constraints; S54: probability p of selecting node j, , where τ_ij is the pheromone concentration from node i to node j, τ_ik is the pheromone concentration from node i to node k, η is the pheromone volatility factor, Δ(t) is the pheromone update gain, α, β and γ are weight parameters, EC_weight_j is the environmental constraint weight of node j, and EC_weight_k is the environmental constraint weight of node k; S55: During the migration process, ants use the concentration of pheromone to decide the next step. Each time the ants decide on a path, they check whether the current choice meets the constraints of environmental factors. If not, they will reselect. When the ants search to a certain stage, they introduce a taboo search strategy and set a taboo table of a certain size to record the node combinations visited recently to avoid repeated selection of the same or similar path segments in a short period of time. After multiple iteration cycles, they integrate the excellent solutions in the group and use genetic algorithms to evolve the population. Finally, they obtain the optimal delivery route from all iterations, which is the optimal solution that meets all constraints.

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