A conflict-free AGV scheduling method for just-in-time material delivery within a workshop

Abstract

The present invention relates to a conflict-free AGV scheduling method for just-in-time material delivery within a workshop, comprising the following steps: obtaining material delivery tasks and dividing the decision cycle into multiple stages; utilizing an independent scheduling algorithm to schedule AGVs in each stage, optimizing the tasks and departure times of each AGV for each trip, and planning a conflict-free optimal path to achieve an AGV scheduling solution that minimizes time window penalties and delivery costs. The scheduling algorithm includes task allocation and path planning. In task allocation, itineraries are divided based on departure time estimates, and AGVs are assigned to each trip based on heuristic rules. In path planning, an A* algorithm is used to perform initial path planning based on the task allocation results, and conflict detection is performed based on the path time window. Conflict resolution is achieved by combining waiting and local replanning. Compared with the prior art, the present invention achieves the coordination of task allocation and path planning under just-in-time delivery requirements, and can improve computing efficiency and resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV scheduling, and in particular to a conflict-free AGV scheduling method for just-in-time material distribution in a workshop. Background Art

[0002] In the modern economy, logistics is fundamental to every industry. From the timely delivery of fresh groceries in daily life to the precise distribution of emergency supplies after disasters, the importance of timely delivery is paramount. In the manufacturing industry, the adoption of just-in-time (JIT) principles reflects a paradigm shift in supply chain management, emphasizing the synchronization of production processes with customer demand to minimize inventory costs and improve responsiveness.

[0003] Material distribution on the shop floor plays a unique role in the supply chain. It requires not only consideration of material transportation and storage but also close integration with the production process. It has a crucial impact on production efficiency and costs, making it a key research topic in the realization of intelligent manufacturing. Timely material distribution is crucial to ensuring the smooth operation of the production line. Any delay or interruption in material flow can impact the entire production process, leading to idle machines and costly downtime. Furthermore, material distribution costs account for a significant proportion of total production costs, sometimes as high as 30%. With the advancement of manufacturing technology, small-batch, high-frequency delivery methods that can quickly respond to customer customization needs have become mainstream, placing higher demands on material distribution scheduling. Automated Guided Vehicles (AGVs) are intelligent logistics equipment that integrate multiple advanced technologies and can travel along planned routes. They offer automation, high flexibility, high efficiency, and high reliability, and are widely used in material distribution on manufacturing floors. Intelligent logistics systems composed of mobile robots such as AGVs can significantly reduce labor costs and improve material distribution efficiency.

[0004] The shop floor material delivery problem is similar to AGV scheduling, the vehicle routing problem (VRP), and the inventory routing problem (IRP), but it also exhibits distinct characteristics. Unlike the single-visit constraints of traditional VRPs, production lines often involve parallel operations, requiring carts to make multiple round trips between warehouses and sites, increasing the complexity of the problem. Furthermore, in the production of high-end equipment such as aircraft, material delivery tasks are unevenly distributed over time due to long production cycles, large material volumes, and significant variations in processing time between different processes. Scheduling deliveries for the entire production cycle at once can lead to performance degradation.

[0005] Just-in-time material delivery methods can be categorized as milk-run and point-to-point. Milk-run delivery typically follows a fixed, circular route, serving each workstation or warehouse in sequence for periodic replenishment. However, milk-run delivery suffers from route redundancy and limited flexibility. Point-to-point delivery, on the other hand, delivers materials directly from the warehouse to the target workstation, enabling rapid response to real-time or urgent needs and offering greater flexibility.

[0006] In point-to-point delivery, the system must first rationally allocate multiple delivery tasks to one or more vehicles. This involves the following decision-making issues: ① If the number of tasks exceeds the current vehicle's carrying capacity, multiple deliveries must be made in batches, and the tasks required for the vehicle to deliver on that trip must be selected. ② If a vehicle needs to deliver multiple tasks per trip, the order in which these tasks should be delivered must be determined. ③ The departure time for each vehicle trip must be determined. To achieve just-in-time delivery, task allocation focuses on minimizing penalties for early or late delivery of materials. Three general approaches are available. The first approach assigns vehicles to deliver continuously, optimizing the delivery sequence to minimize time window penalties. This approach is particularly suitable for scenarios with short decision cycles and tight task demands. The second approach reduces vehicle lead time by inserting idle time, determining departure times based solely on current task demand. The third approach works backwards from the material demand window and the determined travel time to determine departure times. This type of method has the greatest potential because it is more flexible, but it requires assuming that the cars travel along the shortest path and have a certain travel time. It does not consider the time consumption caused by conflicts between cars, and thus cannot arrange appropriate departure times.

[0007] Therefore, to improve the on-time delivery efficiency of the third method, after defining the delivery task for each vehicle, it is necessary to plan the optimal route between the starting and ending points. Traditional route planning typically searches for the shortest path between the starting and ending points in a static environment with known global map information. The problem of route planning for multiple vehicles with conflicts arising from limited access remains to be solved.

[0008] Another problem with task allocation is that all carts in the workshop are always activated when assigning carts. Common cart assignment rules include first arrive first serve (FAFS), minimum idle time (MIT), and shortest rectilinear distance (SRD). Existing research uses one or more priority rules to select among all carts, ensuring that all carts are almost always in operation. However, the more carts there are in motion, the more path conflicts that need to be handled, and this results in wasted resources when the number of tasks is small.

[0009] In summary, existing AGV scheduling methods have the following main problems: 1) Task allocation and path planning are difficult to coordinate under the requirement of just-in-time delivery; 2) All vehicles in the workshop are always activated during scheduling, resulting in resource waste; 3) When resolving conflicts, replanning the entire path may cause deadlock, reducing system stability. Summary of the Invention

[0010] The purpose of the present invention is to provide a conflict-free AGV scheduling method for just-in-time material distribution within the workshop. A phased scheduling scheme is proposed. In each phase, two sub-problems, task allocation and path planning, are solved. In terms of task allocation, a trip division method is designed to solve the departure time decision problem considering path conflicts under JIT distribution requirements. At the same time, an AGV assignment method based on heuristic rules is proposed, which can flexibly adjust the number of AGVs used. In order to solve the path conflict problem, a hybrid path planning algorithm combining the A* algorithm and the heuristic method is proposed. The entire optimization process is iterated through a genetic algorithm, which can achieve efficient and conflict-free material distribution.

[0011] The purpose of the present invention can be achieved by the following technical solutions:

[0012] A conflict-free AGV scheduling method for just-in-time material delivery within a workshop, the method comprising the following steps:

[0013] Get material delivery tasks,

[0014] Divide the decision cycle into stages;

[0015] In each stage, an independent scheduling algorithm is used to schedule AGVs, optimize the tasks and departure times of each AGV for each trip, and plan a conflict-free optimal path to achieve an AGV scheduling solution that minimizes time window penalties and delivery costs. The scheduling algorithm includes task allocation and path planning. In the task allocation, the itinerary is divided based on the departure time estimate, and the AGV is assigned to each itinerary based on heuristic rules. In the path planning, the A* algorithm is used for initial path planning based on the result of task allocation, and conflict detection is performed based on the path time window. Conflict resolution is performed by combining waiting and local replanning.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) The present invention establishes a JIT material distribution model with a time window to minimize the time window penalty and distribution cost. In terms of task allocation, a trip division method based on departure time estimation and an AGV assignment method based on heuristic rules are designed. In terms of path planning, a hybrid path planning algorithm combining the A* algorithm and the heuristic algorithm is proposed, and a genetic algorithm is used to jointly optimize task allocation and path planning, realizing the coordination of task allocation and path planning under the just-in-time delivery requirements, which can ensure timely delivery of materials without delays and avoid path conflicts, thus meeting actual needs.

[0018] (2) The present invention designs a trip partitioning method based on departure time estimation to solve the departure time decision problem considering path conflicts, which can reasonably reserve delivery lead time and increase feasible solutions.

[0019] (3) A cart assignment method based on heuristic rules is designed to enable a reasonable number of carts, achieve timely material delivery with fewer AGVs, and maintain a small number of line-side material storage, avoiding resource waste, improving computing efficiency and resource utilization, and achieving a balance between performance and cost.

[0020] (4) The present invention designs a local replanning method that retains the previous path without conflict and minimizes the changes of the new path compared to the original path. It can improve the quality and robustness of the solution and has good potential in terms of computational efficiency and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the layout of a single warehouse workshop of the present invention;

[0022] Figure 2 It is a schematic diagram of the phase division of the present invention;

[0023] Figure 3 This is a flow chart of the phased scheduling of the present invention;

[0024] Figure 4 It is the flow chart of genetic algorithm in the stages of the present invention;

[0025] Figure 5 Schematic diagram of two types of path conflicts in the present invention, where (5a) is a same-direction conflict and (5b) is an opposite-direction conflict;

[0026] Figure 6 This is the time window feature of the conflicting direction of the present invention;

[0027] Figure 7 is a specific example of a conflict of opposite directions;

[0028] Figure 88a is a comparison of the path time windows before and after the conflict is resolved in one embodiment, wherein (8a) is the path time window before the conflict 1 is resolved, (8b) is the path time window after the conflict 1 is resolved, (8c) is the path time window before the conflict 2 is resolved, and (8d) is the path time window after the conflict 2 is resolved;

[0029] Figure 9 It is the line-side material storage quantity of each station of the second assembly line in one embodiment. DETAILED DESCRIPTION

[0030] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0031] This embodiment provides a conflict-free AGV scheduling method for just-in-time material delivery within a workshop, the method comprising the following steps:

[0032] S1, obtain material delivery tasks,

[0033] S2 divides the decision cycle into multiple stages;

[0034] S3, in each stage, an independent scheduling algorithm is used to schedule AGVs, optimize the tasks and departure times of each AGV for each trip, and plan the optimal conflict-free path to achieve an AGV scheduling solution that minimizes time window penalties and delivery costs. The scheduling algorithm includes task allocation and path planning. In the task allocation, the itinerary is divided based on the departure time estimate, and the AGVs for each trip are assigned based on heuristic rules. In the path planning, the A* algorithm is used for initial path planning based on the results of task allocation, and conflict detection is performed based on the path time window. Conflict resolution is performed by combining waiting and local replanning.

[0035] The above method is specifically implemented through the following scheme.

[0036] (1) Problem description

[0037] (11) Assumptions related to workshop layout

[0038] a. There is V in the workshop L There are two identical assembly lines, each with V S Stations, each station has V A Warehouses, line-side storage areas, and intersections are all abstracted as nodes where AGVs can pass through or stay, such as Figure 1 shown.

[0039] b. A central warehouse is responsible for the materials of all assembly lines in the workshop, assuming that the warehouse has sufficient materials.

[0040] c. The size of a station is L S ×W S , L S is the length of a station, W S The logistics channels in the workshop are all bidirectional single lanes. When multiple AGVs occupy the same section between two adjacent nodes at the same time, path conflicts will occur.

[0041] (12) Assumptions related to delivery tasks

[0042] a. The assembly time of a product at a station is T A , the time to move between two stations is T M The workshop status is T M +T A It is a periodic cycle, which is set as the decision cycle, and the starting time of each cycle is called time 0.

[0043] b. There are J material delivery tasks in the decision cycle. The material demand time t of any material delivery task j∈{1,2,...,J} Dem,j ∈[T M ,T M +T A ), demand quantity q j and target node N j It is known that there is no temporary insertion of new tasks.

[0044] c. The materials required for assembly operations are mainly divided into three categories: ① Large structural parts with small quantities and irregular shapes, such as engines, are usually delivered directly using large logistics equipment such as cranes. ② Common standard parts with large demand and small size, such as screws and nuts, are usually stored in separate areas along the line and replenished regularly. ③ Assemblies with many types and medium size, such as instruments. Due to the correlation between assembly parts, the assembly parts required for the same process have been pre-packaged into one or more standard material boxes in a kitting manner. This article mainly considers the supply of assembly parts, and the material demand q j Corresponding to the number of standard material boxes.

[0045] (13) Assumptions related to the distribution process

[0046] a. There are K identical AGVs in the workshop. All are initially available. Each AGV has a constant speed v and can carry a maximum of Q standard containers.

[0047] b. In a single trip, an AGV departs from the warehouse, delivers the loaded tasks (which can be one or more) to their destination nodes, and then returns to the warehouse. Each AGV can reach any node on the map along the logistics corridor, but does not need to pass through all nodes in one trip.

[0048] c. Material requirement q for any task j j are all less than the maximum carrying capacity of the car, Q. A task cannot be divided, that is, each task can only be delivered once by an AGV, while an AGV can deliver multiple tasks in one trip.

[0049] d. A trip is performed by only one AGV, and the total amount of materials in a trip does not exceed the maximum carrying capacity Q of the trolley.

[0050] e. Factors such as loading and unloading time, AGV failure, AGV power, and lineside area capacity are not considered.

[0051] (2) Scheduling model

[0052] (21) Symbol Definition

[0053] A collection of task numbers.

[0054] AGV serial number collection.

[0055] A set of discrete time sequence numbers.

[0056] A set of path node sequence numbers.

[0057] The set of program numbers for car k, R k is the total number of trips of car k.

[0058] From the starting point To the end The path, where Indicates the node number. represents the road segments between adjacent nodes, Represents the Manhattan distance between nodes.

[0059] [t Dem,j -T W ,t Dem,j ]: Material demand time window of task j, where T W is the time window size.

[0060] t j : The delivery time of task j.

[0061] α,β: penalty factors for early or late delivery.

[0062] N j : The target node of task j.

[0063] q j : Material requirement for task j.

[0064] Q: What is the maximum load capacity of an AGV?

[0065] f k : The fixed cost of each start of the k-th AGV.

[0066] s k : The cost per unit distance traveled by the k-th AGV.

[0067] d kr : The total distance of the k-th AGV's r-th trip.

[0068] (22) Decision variables

[0069] If the delivery task j is delivered by the kth AGV on its rth trip, then x jkr =1, otherwise x jkr = 0. If there are multiple tasks to be delivered in one trip, then the material demand time t Dem Sort in ascending order and deliver in sequence.

[0070] If the kth AGV's rth trip starts at time m, then y krd =1, otherwise y krd =0.

[0071] If the kth AGV passes through path node n at time m, then z kmn =1, otherwise z kmn =0.

[0072] (23) Optimization goal

[0073] In order to achieve JIT delivery, a time window constraint is set to allow early arrival but not late arrival. Delivery time t j (See formula (1)) is not in the time window [t Dem,j -T W ,t Dem,j The penalty value within ] is shown in formula (2). The objective function f that minimizes the time window penalty is t As shown in formula (3), the distribution cost includes the fixed cost and travel distance cost of each trip of the car. Formula (4) extracts the path node of the kth AGV's rth trip and uses the function Calculate the total distance traveled. For example, when The objective function f that minimizes the total delivery cost d See formula (5).

[0074]

[0075] d kr =Dist({n|z kmn y krm' y k(r+1)m” =1,m'≤m≤m”}) (4)

[0076]

[0077] (24) Constraints

[0078]

[0079]

[0080] Among them, formula (6) indicates that a task can only be delivered by one AGV; formula (7) indicates that an AGV can only depart once in one trip; formula (8) indicates that the number of carts in use at any time does not exceed the total number of carts; formula (9) indicates that a cart can only pass through one path node at the same time; formula (10) indicates that a path node can only be occupied by one cart at the same time; formula (11) indicates that the total amount of materials carried by an AGV in one trip does not exceed its maximum carrying capacity.

[0081] (3) Scheduling model solution

[0082] (31) Phased scheduling process

[0083] Due to the long assembly time, large number of materials, and uneven distribution of tasks in time, the decision cycle is divided into multiple small stages. Specifically: All material distribution tasks are arranged according to the demand time t Dem Divided into H+1 task sets, respectively denoted as like Figure 2 As shown. The number of tasks in stage h is J h , the stage length is L; the task set Contains only t Dem =T M mission, Contains only t Dem ∈(T M ,T M +L] task, Contains only t Dem ∈(T M +T A -L,T M +T A ] task, where T AT is the assembly time of the product at a station, M is the time it takes to move between two stations.

[0084] Each stage has an independent scheduling algorithm. For stage h (h ≥ 1), the latest available time set of all AGVs after completing stage h-1 is input. and task collections Output AGV scheduling plan renew And input the next stage, t Avail,h-1,k represents the latest available time of AGV k in stage h-1, represents the set of AGV sequence numbers, K is the number of AGVs; tasks in stage h will not be advanced to stage h-1 or delayed to stage h+1 for processing.

[0085] The phased scheduling process is as follows Figure 3 Considering that the scheduling problem studied in this embodiment is an NP-hard problem, a heuristic algorithm combined with genetic evolution operators is designed within the stage.

[0086] (32) Intra-stage scheduling algorithm flow

[0087] The independent scheduling algorithm uses genetic algorithm. The specific steps of using genetic algorithm for AGV scheduling are as follows:

[0088] A chromosome is represented as is the task number, t Ld is the maximum lead time for material delivery, that is, the earliest time for task j to arrive at the target node is t Dem,j -t Ld , t Dem,j is the required time for task j; materials are allowed to arrive in advance, and the line-side storage capacity is used to ensure that the vehicle completes the delivery plan, and the lead time does not exceed t Ld , reducing the storage pressure on the line side. At the same time, it also reserves margin for subsequent conflict handling operations.

[0089] Since the number of tasks in different stages is different, the global search capability of the genetic algorithm is used to set the [t LL ,t LU ]Select the appropriate t Ld , repeat the following operations to generate the initial population: all tasks in the stage are assigned according to the required time t Dem Sort in ascending order. If the demand time is the same, sort in ascending order according to the distance from the target node to the warehouse; divide the sorted task sequence into several segments, and randomly sort the tasks in each segment to form the first J of the chromosome. h Position; finally in [t LL ,t LU] Randomly select an integer in the interval as the last digit t Ld ;

[0090] Decoding and fitness calculation: Each individual in the population is decoded and its fitness value is calculated. The decoding part includes task allocation and path planning. The task allocation algorithm divides the trips under the premise of satisfying the vehicle capacity constraints and delivery time constraints, assigns an AGV to each trip, and determines its departure time. To avoid conflicts, the vehicle starting point, destination, and departure time obtained by the task allocation algorithm are input into the path planning algorithm to obtain the optimal conflict-free path. When calculating individual fitness, the weighted addition of different objective values ​​is converted into a single-objective problem, as shown in Equation (12).

[0091] f=w1f t +w2f d (12)

[0092] Among them, w1 and w2 are weights, f t To minimize the objective function of the time window penalty, f d The objective function is to minimize the total delivery cost;

[0093] Evolution: Evolve a new generation of population through selection, crossover and mutation, and continuously iterate to approach the optimal solution. This part belongs to the general operation of genetic algorithm and will not be described in detail in this embodiment. The algorithm flow is as follows Figure 4 shown.

[0094] (33)Task allocation algorithm

[0095] Step 1) Estimated departure time:

[0096] Assume that the set of tasks to be transported in a trip According to the material demand time t of the task Dem , the shortest travel distance d, the vehicle speed v, and the maximum lead time t without considering path conflicts Ld Estimated time interval for the car to depart from the warehouse [t DL ,t DU ], specifically including the following steps:

[0097] Step 11) Calculate the number of tasks x delivered in order i Total distance traveled;

[0098] Step 12) Calculate the departure time interval

[0099] Step 13) Check Does it intersect with the departure time interval of the previous task? If not, then task x i If it is not suitable for this delivery, the algorithm terminates; otherwise, it continues to loop and outputs the final interval [tDL ,t DU ].

[0100] The pseudo code of the algorithm is shown in Table 1, where Represents a warehouse node.

[0101] Table 1

[0102]

[0103]

[0104] Step 2) Itinerary division:

[0105] Considering the capacity constraint of the car, the delivery task is divided into multiple trips and defined is the task set of the r-th trip, S T,r is the departure time interval of the rth trip. The trip division includes the following steps:

[0106] Step 21) Set task x i With current itinerary The tasks in the group are merged to form a temporary group

[0107] Step 22) Determine whether the vehicle capacity constraint is satisfied. If not, then assign task x to i Assign to the next trip and jump to step 21); if satisfied, execute the next step;

[0108] Step 23) Temporary group Press t to complete all tasks in Dem Sort in ascending order, and determine whether there is a feasible departure time interval according to the estimated departure time interval in step 1. If not, assign task x to i Assign to the next trip and jump to step 21); if yes, execute the next step;

[0109] Step 24) Set task x i Add to current trip.

[0110] The pseudo code of the algorithm is shown in Table 2.

[0111] Table 2

[0112]

[0113] Step 3) AGV assignment

[0114] Assume that chromosome Divided into R trips, which is the task set for the rth trip Assign a car and decide its departure time t D,r, prioritize the earliest available car among the cars that have been started in the stage. If there is no car that meets the time constraint, the earliest available car is selected from all cars. This method can reasonably arrange the number of cars used in different stages. The pseudo code of the algorithm is shown in Table 3, where the definition It is a collection of cars that have been started in the stage, AGV cars Available time t Avail,k Initialized to the completion time of the previous stage

[0115] Table 3

[0116]

[0117] (34) Path planning algorithm

[0118] The A* algorithm is used to plan the initial optimal path for the car, calculate the path time window, and detect whether there are conflicts. Different conflict resolution strategies are adopted for different conflict types. The A* algorithm will not be described in detail here. This embodiment focuses on the conflict detection method and conflict resolution strategy.

[0119] Calculating the path time window is the primary method for detecting path conflicts in a static environment. Once the vehicle's departure time, speed, starting point, destination, and route are determined, the time period from when the vehicle enters to when it exits a road section, known as the path time window, can be calculated. Two types of path conflicts can be categorized as follows:

[0120] 1. Same-direction conflict: Two cars enter the same road section in the same direction, and their path time windows overlap, as shown in Figure (5a). This conflict can be solved by ordering one car to wait for the other car to pass before entering the conflicting road section.

[0121] 2. Opposite-direction conflict: Two cars are traveling towards each other and will enter each other’s current or previous road segment, as shown in Figure (5b). The path time windows interact, such as Figure 6 The part within the dotted box is shown. Figure 7 A specific example is shown. AGV1 starts at node 5 and ends at node 1; AGV2 starts at node 1 and ends at node 6. Both vehicles start at the same time. If the waiting strategy is still used, AGV2 will have to wait until AGV1 reaches node 1 before it can depart. This long wait time may cause delivery delays. Therefore, AGV2's route needs to be replanned to avoid the conflicting route.

[0122] Combining waiting and local replanning to resolve conflicts is as follows:

[0123] Use the A* algorithm to plan the initial optimal path between the starting point and the end point Start traversing the collection from i=1 Nodes within Calculate the road sections passed The path time window is checked to see if there is a conflict. If there is a conflict in the same direction, the Wait; if there is a conflict, perform local replanning and update The specific steps of the local replanning are as follows:

[0124] Step 41) Traverse forward from l=i Nodes within Find the nearest intersection node If no intersection node is found when l=1, jump to step 43);

[0125] Step 42) do not consider and Judging from the intersection Is there any other passable direction? If so, use the A* algorithm to find it. If there are other paths between the endpoints, the algorithm ends; otherwise, let i = c and jump back to step 41);

[0126] Step 43) do not consider Judging from the starting point Check whether there are other traversable directions. If so, use the A* algorithm to find other paths between the starting point and the end point, and the algorithm ends. Otherwise, let the car wait at the starting point for a preset time, and then use the A* algorithm to re-plan the optimal path between the starting point and the end point, and the algorithm ends.

[0127] Repeat the above steps until there is no path conflict. The pseudo code of path planning is shown in Table 4.

[0128] Table 4

[0129]

[0130]

[0131]

[0132] (4) Experimental results and performance verification

[0133] In this example, a series of numerical experiments validated the performance of the proposed algorithm for solving the material delivery problem on an aircraft pulsating assembly line. All algorithms were coded in MATLAB R2022a and run on a PC equipped with an Intel Core i9 processor and 16GB of RAM. The research used process data from an actual aircraft pulsating assembly line, which consists of 298 assembly steps within a 6-hour period. Given the target node and material demand time for each step, a material demand within the range {1, 2, 3, 4, 5} and a line edge location within the range {1, 2, 3} were randomly generated for each step, forming a material delivery task. Assuming there are three identical pulsating assembly lines in the workshop, there are a total of 298 × 3 = 894 tasks. Parameter settings are detailed in Table 5. Table 6 captures the information for four tasks within a certain phase as an example. In Table 6, j is the task number; AO Code is the assembly step number corresponding to the task; and L / S / LS are the assembly line / station / line edge storage areas, representing the location of the assembly step.

[0134] Table 5 Parameter settings

[0135]

[0136]

[0137] Table 6 Material distribution task examples

[0138] j AO Code L / S / LS <![CDATA[t Dem,j (s)]]> <![CDATA[q j ]]> 15 25 1-1-1 24696 4 60 865 1-2-2 23400 4 358 865 2-2-2 23400 4 809 2936 3-5-2 25200 1

[0139] (41) Validation of path planning algorithm

[0140] A target node is randomly generated for each car. The car starts from the warehouse, reaches the target node, and then returns to the warehouse. Figure 8 The before-and-after comparison of the time windows of two conflicting paths resolved during the planning process is shown. It can be seen that the final result has no conflict characteristics such as overlapping or interactive time windows, which proves the effectiveness of the conflict resolution strategy.

[0141] (42) Task Allocation Algorithm Evaluation

[0142] The decision cycle T M +T A The system is divided into 12 stages, 0-11, with the length of stage h being L. When h = 1, L = 1 hour; when h > 1, L = 0.5 hours. Since stage 0 does not require just-in-time delivery and can be delivered before assembly begins, it is not considered in this section.

[0143] (1) Verification of the validity of time window constraints

[0144] Since the time window constraint is set not only to ensure timely delivery but also to reduce the accumulation of materials at the lineside, the effectiveness of the method is verified by observing the material storage quantity at the lineside space of each station on the assembly line. A simple priority rule method is used for comparison. This method only considers the AGV capacity constraint to divide the itinerary, artificially inserts idle time, and uses the FAFS rule for AGV assignment. The total time window penalty f of the present invention and the priority rule method is t They are 310.2 and 5891.2 respectively. Although both algorithms can deliver the materials before the end of the time window, the priority rule method will result in a higher penalty. Figure 9 It can be seen that the priority rule method will cause a large amount of materials to accumulate in the line edge area, while the present invention can always maintain a smaller amount of line edge materials, thereby achieving the purpose of reducing the accumulation of line edge materials.

[0145] (2) Cart assignment method

[0146] A common method of assigning cars is to assign the earliest trip to the earliest available car, which is called the First Arrive First Serve (FAFS) rule. The car assignment part adopts the FAFS rule and the method proposed in this article respectively, and the other parts of the task allocation algorithm remain unchanged. As can be seen from Table 7, the present invention realizes the function of adjusting the number of cars used in different stages. As can be seen from Table 8, the FAFS rule can achieve a better objective function value due to the use of more cars, but it also makes the waiting time of vehicles caused by conflicts longer, local replanning more frequent, and the algorithm running time longer. Although the present invention is slightly inferior to the method based on the FAFS rule in terms of objective function value, it improves resource utilization and algorithm efficiency, and achieves a balance between performance and cost.

[0147] Table 7 Experimental results of each stage under different car assignment methods

[0148]

[0149] Table 8 Total experimental results under different car assignment methods

[0150]

[0151]

[0152] (3) Trip division and departure time decision-making method

[0153] The proposed algorithm is compared with two other algorithms, TD-I and TD-II. TD-I and TD-II only consider AGV capacity constraints when dividing trips, and groups vehicles that do not exceed the maximum capacity are grouped together. Different idle time insertion methods are used when deciding the departure time. TD-I inserts idle time between stages, and all vehicles have an initial available time t in stage 1. Avail,0,k = 2h, k∈{1,...,6}. The initial available time t of the car k that is not started in the h-1 stage is Avail,h-1,k =min(max(T Avail,h-1 ),t Avail,h-1,k +L). Within a time window, the AGV immediately begins its next trip after returning to the warehouse. The TD-II algorithm inserts idle time between trips so that the delivery time of the first task in a trip coincides with the start of the time window. The AGV can wait for a period of time at the warehouse between trips. All other aspects are the same as those of the original algorithm. Table 9 shows that the proposed algorithm significantly reduces the time window penalty without significantly changing delivery costs.

[0154] Table 9 Experimental results under different trip division methods

[0155] algorithm <![CDATA[f d ]]> contrast <![CDATA[f t ]]> contrast TD-I 2454.0 -6.3％ 879.4 -64.7％ TD-II 2270.4 +1.0％ 425.0 -27.0％ The present invention 2300.3 / 310.2 /

[0156] (43) Performance at different production scales

[0157] The effectiveness of this invention in large-scale production scenarios was verified by adjusting the number of assembly lines and AGVs within the workshop. Table 10 shows the experimental results at two larger scales. The proposed model and algorithm are capable of handling such large-scale problems and demonstrate potential for application across different problem sizes.

[0158] Table 10 Experimental results under different production scales

[0159]

[0160]

[0161] In view of the characteristics of long production cycle, large number of tasks and uneven distribution of tasks over time, the present invention designs a phased scheduling method to achieve the coordination of task allocation and path planning under JIT requirements. For task allocation, a trip division method based on departure time estimation and a car assignment method based on heuristic rules are designed to decide the departure time and adjust the number of cars; for path planning, a hybrid path planning algorithm combining the A* algorithm with the heuristic method is designed to effectively avoid path conflicts. In order to prove the effectiveness of the proposed model and algorithm, an actual aircraft pulse assembly line was used as the research object. The experimental results show that: 1) the proposed algorithm can ensure the timely delivery of materials without delays and avoid path conflicts, meeting actual needs; 2) the algorithm can improve computing efficiency and resource utilization, and achieve a balance between performance and cost; 3) the algorithm has good scalability and good engineering application potential.

[0162] In future research, more complexities such as multiple warehouses or different types of AGVs can be explored. In addition, the inherent uncertainty in real production introduces dynamics in the material demand stage and the material delivery stage, which requires research on dynamic scheduling.

[0163] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A conflict-free AGV scheduling method for on-time material delivery within a workshop, characterized by: The method comprises the following steps: Get material delivery tasks, Divide the decision cycle into stages; In each stage, an independent scheduling algorithm is used to schedule AGVs, optimize the tasks and departure times of each AGV for each trip, and plan a conflict-free optimal path to achieve an AGV scheduling solution that minimizes time window penalties and delivery costs. The scheduling algorithm includes task allocation and path planning. In the task allocation, the itinerary is divided based on the departure time estimate, and the AGV is assigned to each itinerary based on heuristic rules. In the path planning, the A* algorithm is used for initial path planning based on the result of task allocation, and conflict detection is performed based on the path time window. Conflict resolution is performed by combining waiting and local replanning.

2. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 1 is characterized in that: The decision cycle is divided into multiple stages: all material distribution tasks are divided into demand time t Dem Divided into H+1 task sets, respectively denoted as The number of tasks in stage h is J h , the stage length is L; the task set Contains only t Dem =T M mission, Contains only t Dem ∈(T M ,T M +L] task, Contains only t Dem ∈(T M +T A -L,T M +T A ] task, where T A T is the assembly time of the product at a station, M is the time taken to move between two stations; For stage h (h≥1), enter the latest available time set of all AGVs after completing stage h-1 and task collections Output AGV scheduling plan renew And enter the next stage, t Avail,h-1,k represents the latest available time of AGV k in stage h-1, represents the set of AGV sequence numbers, K is the number of AGVs; tasks in stage h will not be advanced to stage h-1 or delayed to stage h+1 for processing.

3. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 1 is characterized in that: The independent scheduling algorithm adopts a genetic algorithm, and the specific steps of using the genetic algorithm to schedule AGVs are as follows: A chromosome is represented as is the task number, t Ld is the maximum lead time for material delivery, that is, the earliest time for task j to arrive at the target node is t Dem,j -t Ld , t Dem,j is the required time for task j; materials are allowed to arrive in advance, and the line-side storage capacity is used to ensure that the vehicle completes the delivery plan, and the lead time does not exceed t Ld ; Using the global search capability of genetic algorithm in the artificially set [t LL ,t LU ]Select the appropriate t Ld , the process is as follows: Generate the initial population: All tasks in the stage are assigned according to the required time t Dem Sort in ascending order. If the demand time is the same, sort in ascending order according to the distance from the target node to the warehouse; divide the sorted task sequence into several segments, and randomly sort the tasks in each segment to form the first J of the chromosome. h Position; finally in [t LL ,t LU ] Randomly select an integer in the interval as the last digit t Ld ; Decoding and fitness calculation: Each individual in the population is decoded and its fitness value is calculated. The decoding part includes task allocation and path planning. The task allocation algorithm divides the trip under the premise of satisfying the vehicle capacity constraint and delivery time constraint, assigns an AGV to each trip and determines its departure time. The starting point, end point, and departure time of the vehicle obtained by the task allocation algorithm are input into the path planning algorithm to obtain the optimal path without conflict. When calculating the individual fitness, the weighted addition of different objective values ​​is converted into a single objective problem. The individual fitness is expressed as: f=w1f t +w2f d Among them, w1 and w2 are weights, f t To minimize the objective function of the time window penalty, f d The objective function is to minimize the total delivery cost; Evolution: Evolve a new generation of population through selection, crossover, and mutation, and continuously iterate to approach the optimal solution.

4. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 3 is characterized in that: The objective function of minimizing the time window penalty is expressed as: Among them, p j is the delivery time t j Not in the time window [t Dem,j -T W ,t Dem,j ] within the penalty value, p j =α(t Dem,j -T W -t j )+β(t j -t Dem,j ) Among them, T w is the time window size, α, β are penalty factors for advance or delay, t j is the delivery time of task j, N j is the target node of task j; Represents the AGV serial number set; represents the set of program numbers for car k; Represents a set of discrete time sequence numbers; The objective function of minimizing the total delivery cost is expressed as: Where K is the number of AGVs, f k is the fixed cost of each start of the kth AGV, R k is the total number of trips of car k, s k is the cost per unit distance traveled by the kth AGV, d kr is the total distance of the k-th AGV’s r-th trip.

5. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 3 is characterized in that: The decision variables for AGV scheduling using genetic algorithms include: If the delivery task j is delivered by the kth AGV on its rth trip, then x jkr =1, otherwise x jkr =0; if there are multiple tasks to be delivered in one trip, the material demand time t Dem Sort in ascending order and deliver in sequence; If the kth AGV's rth trip starts at time m, then y krd =1, otherwise y krd =0; If the kth AGV passes through path node n at time m, then z kmn =1, otherwise z kmn =0; in, Represents a set of task sequence numbers; Represents the AGV serial number set; represents the set of program numbers for car k, R k is the total number of trips of car k; Represents a set of discrete time sequence numbers; Represents a set of path node sequence numbers.

6. The conflict-free AGV scheduling method for just-in-time material delivery in a workshop according to claim 5, characterized in that: The constraints for AGV scheduling using genetic algorithms include:

7. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 3 is characterized in that: The trip division based on the estimated departure time specifically includes the following steps: Step 1) Estimated departure time: Assume that the set of tasks to be transported in a trip According to the material demand time t of the task Dem , the shortest travel distance d, the vehicle speed v, and the maximum lead time t without considering path conflicts Ld Estimated time interval for the car to depart from the warehouse [t DL ,t DU ]: Step 11) Calculate the number of tasks x delivered in order i Total distance traveled; Step 12) Calculate the departure time interval Step 13) Check Does it intersect with the departure time interval of the previous task? If not, then task x i If it is not suitable for this delivery, the algorithm terminates; otherwise, it continues to loop and outputs the final interval [t DL ,t DU ]; Step 2) Itinerary division: Considering the capacity constraint of the car, the delivery task is divided into multiple trips and defined is the task set of the r-th trip, S T,r is the departure time interval of the rth trip. The trip division includes the following steps: Step 21) Set task x i With current itinerary The tasks in the group are merged to form a temporary group Step 22) Determine whether the vehicle capacity constraint is satisfied. If not, then assign task x to i Assign to the next trip and jump to step 21); if satisfied, execute the next step; Step 23) Temporary group Press t to complete all tasks in Dem Sort in ascending order, and determine whether there is a feasible departure time interval according to the estimated departure time interval in step 1. If not, assign task x to i Assign to the next trip and jump to step 21); if yes, execute the next step; Step 24) Set task x i Add to current trip.

8. The conflict-free AGV scheduling method for just-in-time material delivery in a workshop according to claim 3 is characterized in that: The AGV assignment for each trip based on heuristic rules is specifically as follows: Assume that the chromosome Divided into R trips, which is the task set for the rth trip Assign a car and decide its departure time t D,r , prioritize the earliest available car among the cars that have been started in the stage. If there is no car that meets the time constraint, then select the earliest available car among all the cars, among which the AGV car Available time t Avail,k Initialized to the completion time of the previous stage 9. The conflict-free AGV scheduling method for on-time material delivery within a workshop according to claim 1 is characterized in that: The conflict detection based on the path time window is specifically as follows: Determine the vehicle's departure time, speed, starting point, destination, and route. Calculate the time period from when the vehicle enters a road section to when it exits it, known as the path time window. Determine the path conflict type based on the path time window. Path conflict types include: Same-direction conflict: Two cars enter the same road section in the same direction, and their path time windows overlap; Opposite-direction conflict: Two vehicles are traveling towards each other and will enter each other's current or previous road segment, resulting in an interaction between the path time windows.

10. The conflict-free AGV scheduling method for just-in-time material delivery in a workshop according to claim 9, characterized in that: The conflict resolution by combining waiting and local replanning is specifically as follows: Use the A* algorithm to plan the initial optimal path between the starting point and the end point Start traversing the collection from i=1 Nodes within Calculate the road sections passed The path time window is checked to see if there is a conflict. If there is a conflict in the same direction, the Wait; if there is a conflict, perform local replanning and update The specific steps of the local replanning are as follows: Step 41) Traverse forward from l=i Nodes within Find the nearest intersection node If no intersection node is found when l=1, jump to step 43); Step 42) do not consider and Judging from the intersection Is there any other passable direction? If so, use the A* algorithm to find it. If there are other paths between the endpoints, the algorithm ends; otherwise, let i = c and jump back to step 41); Step 43) do not consider Judging from the starting point Check whether there are other traversable directions. If so, use the A* algorithm to find other paths between the starting point and the end point, and the algorithm ends. Otherwise, let the car wait at the starting point for a preset time, and then use the A* algorithm to re-plan the optimal path between the starting point and the end point, and the algorithm ends.

Images