Joint Scheduling Optimization System for Terminal AGV and TGV Based on Aerial Rail Transit

Abstract

The present invention belongs to the technical field of equipment operation scheduling, and discloses a joint scheduling optimization system for quay AGV and TGV based on aerial ropeway transportation, including: a regional division module, which collects the quay operation area and divides the operation area; a model planning module, which constructs a linear integer programming model with the optimization objectives of minimizing the maximum completion time and minimizing the total delay time, and sets constraint conditions for the operation processes of AGV and TGV according to the divided operation areas for constraint; a scheduling optimization module, which performs multi-objective optimization on the linear integer programming model based on the NSGA-II algorithm, solves the optimized linear integer programming model, and obtains the optimal joint scheduling scheme for AGV and TGV. The present invention balances the system throughput and operation stability through regional task division and resource constraints, and provides reliable decision-making support for the container transfer operation of the railway-water combined transport port under complex engineering environments.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation scheduling. More specifically, the present invention relates to a joint scheduling optimization system for quay AGV and TGV based on aerial rail transportation. Background Art

[0002] Traditional horizontal handling equipment focuses on task assignment and path planning. Although the operation efficiency of gantry crane loading and unloading equipment is relatively stable and controllable, its contribution to the overall efficiency improvement of the quay is gradually slowing down. The aerial rail transportation and collection system has the advantages of small floor area and strong line adaptability due to its elevated facilities in the air, and has become a new solution to solve the connection between the port and the railway.

[0003] With the continuous increase in quay business volume and the growing demand for multimodal transport, against this background, optimizing horizontal handling equipment, especially the scheduling of automated guided vehicles (AGVs, which are transportation equipment that can automatically drive without a driver through guidance technologies such as lasers, magnetic strips, and vision) and track-guided vehicles (TGVs, which are transportation equipment that rely on fixed tracks (such as aerial tracks) to drive), has become the key breakthrough point for improving quay operation efficiency. Task assignment aims to efficiently and reasonably dispatch operation tasks to AGVs and TGVs to ensure the quick and accurate completion of tasks; scheduling optimization strives to plan the optimal usage mechanism for equipment to achieve efficient resource utilization and maximize operation efficiency.

[0004] Currently, in actual operations, many factors such as the task execution status, operation rules, resource occupancy, operation time constraints of horizontal transportation equipment, as well as the number of container operation tasks, the number of equipment, and operation time in the system are not comprehensively considered. Precise arrangement of the task sequence and execution time for AGVs and TGVs may result in the lack of a coordination mechanism for shared resources in the separate scheduling system, leading to the problem that AGVs and TGVs are prone to blockage due to time conflicts or resource occupancy during task handover.

[0005] In view of this, the present invention proposes a joint scheduling optimization system for quay AGV and TGV based on aerial rail transportation to solve the above problems. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art and achieve the above object, the present invention provides the following technical solution: A joint scheduling optimization system for quay AGV and TGV based on aerial rail transportation, comprising:

[0007] Area Division Module: Collect the terminal operation area, divide the operation area, and obtain the operation area of the aerial rail system and the internal operation area of the automated terminal. The operation area of the aerial rail system includes an aerial rail collection and distribution operation subsystem for controlling the TGV to perform container transfer tasks between both ends of a preset fixed track.

[0008] Model Planning Module: With the minimization of the maximum completion time and the minimization of the total delay time as the optimization objectives, construct a linear integer programming model, and set constraint conditions for the operation processes of AGVs and TGVs according to the divided operation areas for constraint.

[0009] Scheduling Optimization Module: Based on a multi-objective optimization algorithm, perform multi-objective optimization on the linear integer programming model, solve the optimized linear integer programming model, and obtain the optimal scheduling plan for AGVs and TGVs.

[0010] Furthermore, the method for dividing the operation area includes:

[0011] Collect the terminal layout data, define the operation area from the shoreline to the yard as the internal operation area of the automated terminal, and define the aerial rail transportation network connecting the terminal and the railway freight yard as the operation area of the aerial rail system;

[0012] The internal operation area of the automated terminal successively includes a horizontal transportation operation subsystem, a shoreline operation subsystem, and a yard operation subsystem from the shoreline to the yard. Among them, the shoreline operation subsystem is used to control the quay crane to load and unload containers, the horizontal transportation operation subsystem is used for the AGV to travel between the quay crane and the yard, and between the quay crane and the aerial rail terminal end, and the yard operation subsystem is used to manage the storage and scheduling of containers in the yard.

[0013] Furthermore, the method for setting constraint conditions for the operation processes of AGVs and TGVs for constraint includes:

[0014] Constrain the task assignment and operation sequence of AGVs and TGVs;

[0015] Constrain the operation rules of AGVs and TGVs;

[0016] Constrain the resource capacity;

[0017] Constrain the relationship between each link of the operation task and the time step;

[0018] Constrain the time relationship of each operation link:

[0019] Constrain the time relationship of AGVs and TGVs during the task handover process;

[0020] Constrain the value range of the decision variables that control the task assignment, execution order, time arrangement, and resource occupancy of AGVs and TGVs.

[0021] Furthermore, the method for obtaining the optimal scheduling scheme of AGV and TGV includes:

[0022] Encoding each AGV, TGV, and task type to obtain chromosome individuals;

[0023] Randomly generating an initial chromosome population;

[0024] Presetting an ideal point where both the maximum completion time and the total delay time are minimized;

[0025] Stratifying the population according to the dominance relationship to obtain the non-dominated sorting result;

[0026] Calculating the Euclidean distance of each individual to the ideal point, and sorting the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance;

[0027] Selecting the individuals with Euclidean distance higher than the preset distance threshold from each layer as parents to enter the next generation;

[0028] For tasks sharing the same loading and unloading area or being continuously executed, perform a crossover operation of swapping associated task blocks, detect and repair the crossover result to obtain repaired crossover individuals;

[0029] Combining the number of iterative updates to perform dynamic mutation on the task assignment or timing to obtain mutant individuals;

[0030] For each non-dominated solution in the parents, identify the longest task chain, and optimize the longest task chain through a tabu search strategy to obtain an optimized longest task chain;

[0031] Replacing the corresponding individuals in the parents with the optimized longest task chain to form an offspring population;

[0032] Merging the parents, repaired crossover individuals, mutant individuals, and offspring population to obtain a new population, performing non-dominated processing on the individuals in the new population, and obtaining the top W optimal individuals of the non-dominated processing result;

[0033] Using the top W optimal individuals as the new parents for the next round of iteration;

[0034] Through J rounds of iteration, screening out the optimized scheduling scheme with the minimum maximum completion time and total delay time.

[0035] Furthermore, each gene in the chromosome includes the equipment number of the AGV or TGV and the task type identifier; where when the task type identifier is an overhead rail task, the task is assigned to the TGV, otherwise the corresponding gene is an invalid gene.

[0036] Furthermore, the method for obtaining the non-dominated sorting result includes:

[0037] Step 1: Maintain a domination set and a dominated counter for each individual, with the initial values set to an empty set and 0 respectively;

[0038] Step 2: Traverse the domination relationships of any pair of individuals in the population, judge the domination relationships, and count the domination sets and the number of dominated individuals of each individual;

[0039] Step 3: Obtain all individuals with a dominated number of 0, mark them as the first non-dominated individuals, summarize the first non-dominated individuals into the first layer of the non-dominated sorting result, and remove the first non-dominated individuals from the population to obtain an updated population;

[0040] Step 4: For the remaining individuals in the updated population, update the dominated numbers of the remaining individuals to obtain the updated dominated numbers of the remaining individuals. For each of the removed first non-dominated individuals, traverse the corresponding domination set, and subtract 1 from the dominated value of each individual in the corresponding domination set to obtain the updated dominated numbers of the remaining individuals;

[0041] Step 5: Repeat Step 3 until all individuals are assigned to the non-dominated sorting result.

[0042] Furthermore, the method for judging the domination relationship includes:

[0043] If the maximum completion time of individual f is not greater than the maximum completion time of individual g, the total delay time of individual f is not greater than the total delay time of individual g, and any one objective value of individual f is less than the corresponding objective value of individual g, then it is judged that individual f dominates individual g, and the objective values include the maximum completion time and the total delay time.

[0044] Furthermore, the method for counting the domination sets and the number of dominated individuals of each individual includes:

[0045] If individual p dominates individual q, add p to the domination set of q and increment the dominated number of q by 1;

[0046] Traverse the domination relationships between individual p and all individuals to obtain the domination set and the number of dominated individuals corresponding to individual p.

[0047] Furthermore, the method for obtaining the longest task chain includes:

[0048] For each non-dominated solution in the parent generation, traverse its task assignment sequence. For the AGV task chain, accumulate the loading, transportation, unloading times and the no-load driving time of each task; for the TGV task chain, accumulate the loading, transportation, unloading times and the no-load driving time of each task; among them, comprehensively analyze the time constraints between tasks;

[0049] Select the task chain with the longest cumulative time as the longest task chain.

[0050] Further, the method for obtaining the optimized longest task chain includes:

[0051] Every L iterations, swap the AGV or TGV assigned tasks of adjacent tasks on the longest task chain, and shorten the no-load travel time according to a preset adjustment ratio.

[0052] Further, the method for obtaining the first W optimal individuals of the non-dominated processing result includes:

[0053] Perform non-dominated sorting on the individuals in the new population, calculate the Euclidean distance from each individual in the new population to the ideal point, sort the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance, and obtain the updated non-dominated sorting result; retain the first W optimal individuals of the updated non-dominated sorting result as the first W optimal individuals of the non-dominated processing result.

[0054] The technical effects and advantages of the joint scheduling optimization system for terminal AGV and TGV based on aerial ropeway transportation of the present invention:

[0055] The present invention realizes fully enclosed transportation of railway-water combined transportation through the aerial ropeway system, avoids dependence on off-site container trucks, and reduces traffic interference around the port; solves the coordination problem caused by the physical property differences between AGV and TGV, and ensures the matching of equipment task types and operation rules; combines a multi-objective optimization algorithm to minimize the makespan and total delay time through multi-objective optimization, integrates the constraints of AGV and TGV collaborative scheduling, and avoids the overall efficiency loss caused by single-objective optimization in separate scheduling; and realizes the efficient coordination of AGV and TGV through task chain optimization and resource dynamic allocation in high-load scenarios, reducing equipment waiting and no-load time; balances the system throughput and operation stability through regionalized task division and resource constraints, and provides reliable decision-making support for the container transfer operation of railway-water combined transportation ports in complex engineering environments based on aerial ropeway transportation. Brief Description of the Drawings

[0056] Figure 1 It is a structure diagram of the joint scheduling optimization system for terminal AGV and TGV based on aerial ropeway transportation of the present invention;

[0057] Figure 2 It is a schematic diagram of the relationship between the operation area and the subsystems of the present invention;

[0058] Figure 3 It is a schematic diagram of the structure of the terminal with aerial ropeway transportation of the present invention;

[0059] Figure 4 It is a schematic diagram of container task allocation of the present invention;

[0060] Figure 5 It is a schematic diagram of the relationship between container task operation and time step of the present invention. Detailed Embodiments

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 As shown in the figure, this embodiment provides a combined dispatching and optimization system for quay AGV and TGV based on aerial ropeway transportation, including:

[0064] Area division module: collect the quay operation area and divide the operation area;

[0065] The method for dividing the operation area includes:

[0066] Referring to Figure 2 , collect the quay layout data (such as the position of quay cranes, yard range, railway freight yard coordinates, loading and unloading area coordinates, transfer point coordinates, track intersection coordinates, etc.), define the operation area from the shoreline to the yard as the internal operation area of the automated quay. The internal operation area of the automated quay successively includes a shoreline operation subsystem, a horizontal transportation operation subsystem, and a yard operation subsystem from the shoreline to the yard. Among them, the shoreline operation subsystem is used to control the quay crane to load and unload containers. The horizontal transportation operation subsystem is used for the AGV to travel between the quay crane and the yard, and between the quay crane and the aerial ropeway terminal. The yard operation subsystem is used to manage the storage and dispatching of containers in the yard. By dividing the shoreline operation subsystem, the yard operation subsystem, and the horizontal transportation operation subsystem in the internal operation area of the automated quay, the core task scope of the AGV (horizontal transportation between the quay crane and the yard, and between the quay crane and the aerial ropeway terminal) is clarified, and the "last mile" support for container collection and distribution is provided through the TGV. Moreover, the time windows of the quay crane and the yard crane determine the loading and unloading sequence of the AGV, and it is possible to ensure the coordinated operation of the AGV and the quay crane through constraint conditions in the later stage to avoid waiting and delays. The fast loading and unloading rules of the AGV in the exchange area and the constraint conditions for empty running can reduce the idle time of equipment and improve the internal transfer efficiency of the quay.

[0067] Referring to Figure 3, the empty rail transportation network connecting the front of the terminal and the railway freight yard is defined as the operation area of the empty rail system; the operation area of the empty rail system includes the empty rail collection and distribution operation subsystem, which is used to control the TGV to perform container transfer tasks between the two ends of the preset fixed track; the operation area of the empty rail system realizes the seamless connection between the front of the terminal and the railway freight yard through the empty rail collection and distribution subsystem, provides a dedicated operation space for the TGV, and solves the long-distance and cross-regional transportation problems that cannot be covered by the AGV in the combined rail and water transportation; the TGV only performs import and export tasks, which can avoid overlapping with the internal tasks of the AGV in the terminal, and ensure its efficient use of track resources through subsequent constraint conditions; the TGV travels along the fixed track, and its empty running time is ensured to be controllable through constraints, providing deterministic parameters for the scheduling model.

[0068] The area division module can clarify the physical operation boundaries and equipment responsibilities of the AGV and the TGV through area division, and can avoid task overlap; it solves the coordination problem caused by the physical property differences between the AGV and the TGV, and ensures that the task types and operation rules of the equipment match.

[0069] Model planning module: Taking minimizing the maximum completion time and minimizing the total delay time as the optimization objectives, a linear integer programming model is constructed, and constraint conditions are set for the operation processes of the AGV and the TGV; among them, the maximum completion time is the total time required to complete the terminal operation.

[0070] The model planning module constructs a linear integer programming model based on the following assumptions: The stacking positions of the containers in the yard and on the ship are known; all containers have the same size, and the AGV and the TGV can only transport one container at a time; the scheduling plans of the quay crane and the yard crane are predetermined and known, which determine the time window information of all containers; the traveling speeds of the AGV and the TGV are known and fixed, and the speeds of the quay crane and the empty rail for loading and unloading containers are known and fixed; the AGV leaves immediately after loading and unloading the container in the exchange area, vacating the operation position for the next AGV waiting to enter the exchange area; the termination exchange areas of the previous operation tasks and the starting exchange areas of the subsequent operation tasks of the AGV and the TGV are both known.

[0071] The linear integer programming model is constructed as follows:

[0072] ;

[0073] Among them, is a continuous variable, representing the delay time of task , is the set of all tasks; is a continuous variable, representing the maximum completion time; is a continuous variable, representing task Completion time; minimizing the makespan can ensure that the maximum value of all task completion times is as small as possible, avoid a decrease in overall efficiency caused by delays in individual tasks, directly improve the quay throughput efficiency, and especially reduce the impact of "bottleneck" tasks in high-load scenarios; minimizing the total tardiness can minimize the sum of the tardiness of all tasks, ensure that tasks are completed within the time window as much as possible, improve the punctuality of task execution, and reduce the interruption of the port operation chain caused by tardiness.

[0074] Referring to Figure 3 and Figure 4 , the methods for setting constraints on the operation processes of AGVs and TGVs include:

[0075] Constraining the task assignment and operation sequence of AGVs and TGVs; such as:

[0076] ;

[0077] ;

[0078] Among them, is the set of AGVs; is the set of TGVs; is the index of horizontal transportation equipment; is the set of all tasks; is the virtual initial task; is the virtual end task; is the set of operation tasks related to the aerial ropeway system, is the set of import tasks for transporting containers from the railway into the port, is the set of export tasks for transporting containers from the port into the railway; is a 0-1 variable. If the horizontal transportation equipment is continuously executing task and task then it is 1, otherwise it is 0; is a 0-1 variable. If the horizontal transportation equipment is continuously executing task and task then it is 1, otherwise it is 0; Through the above formula, it can be ensured that each task must be executed by a unique AGV or TGV, avoiding duplicate assignment or omission of tasks and ensuring the rational use of resources.

[0079] Such as: ;

[0080] ;

[0081] ;

[0082] ;

[0083] Among them, is a 0-1 variable. If the horizontal transportation device continuously executes the virtual initial task and task then it is 1, otherwise it is 0; is a 0-1 variable. If the horizontal transportation device continuously executes task and the virtual end task then it is 1, otherwise it is 0; Through the above formula, it can be ensured that the task sequence of the AGV / TGV must be continuously executed from the virtual start task to the virtual end task, forcing the tasks to be arranged in logical order to prevent equipment idleness or task interruption.

[0084] For example:

[0085] ;

[0086] ;

[0087] ;

[0088] Among them, is a 0-1 variable. If the horizontal transportation device is continuously executing task and task then it is 1, otherwise it is 0; is a 0-1 variable, indicating that the horizontal transportation device continuously executes task and task then it is 0, otherwise it is 1, indicating that for any horizontal transportation device and any task , performing the operation from task to itself is not allowed; Through the above formula, it can be ensured that there must be a clear pre - and post - successor relationship between tasks, ensuring that the equipment can immediately connect to the next task after completing the current task, reducing the no - load time.

[0089] Constraints are imposed on the operation rules of AGVs and TGVs; For example:

[0090] ;

[0091] ;

[0092] Among them, in the value range is , is the loading link for the AGV to execute task , For the transportation process of the AGV to perform tasks in the transportation link For the unloading process of the AGV to perform tasks in the unloading link For the loading process of the TGV to perform tasks in the loading link is the set of operation tasks related to the aerial ropeway For the transportation process of the TGV to perform tasks in the transportation link For the unloading process of the TGV to perform tasks in the unloading link is the set of all relevant operation links in the TGV heavy-load operation; is a 0-1 variable, indicating that the horizontal transportation equipment executes within the time step then it is 1, otherwise it is 0; is the set of time steps; is the set of all relevant operation links in the AGV heavy-load operation; Through the above formula, it can be ensured that within the same time step, the TGV / AGV processes at most one heavy-load operation, which can avoid the equipment being occupied by multiple tasks at the same time and prevent operation conflicts;

[0093] For example:

[0094] ;

[0095] ;

[0096] ; Among them, is the set of internal wharf operation tasks; Through the above formula, it can be ensured that the aerial ropeway tasks must be executed by the TGV, and the ordinary tasks must be executed by the AGV, which can clarify the equipment division of labor and ensure the physical rules matching between the aerial ropeway system and the ground transportation;

[0097] For example:

[0098] ;

[0099] ;

[0100] ;

[0101] ;

[0102] Among them, is the no-load driving operation link of the TGV; is a 0-1 variable, indicating that the horizontal transportation equipment executes within the time step It is 1; otherwise it is 0. is the no-load driving operation link of the AGV; is a 0-1 variable representing the horizontal transportation equipment at is executed within the time step It is 1; otherwise it is 0. Through the above formula, it can ensure the vehicle occupancy limit in the no-load driving link, prevent resource waste during no-load driving, and optimize equipment utilization.

[0103] Constraints are imposed on the resource capacity; for example:

[0104] ; where is a 0-1 variable, indicating that if the operation link is executed within the time step it is 1; otherwise it is 0, is the operation link, is the operation, is the set of all operations, is the heavy-load operation set of the horizontal transportation equipment required to execute the task For the operation , is the set of TGV heavy-load operations required to execute the task of the set, is the set of AGV heavy-load operations required to execute the task of the set, is the set of no-load driving operations of the horizontal transportation equipment required to be executed for the conversion process For the operation , is the job network; through the above formula, it can be ensured that each operation link can only be executed by one device within the same time step, and multi-device competition for key resources such as loading and unloading areas and tracks can be avoided;

[0105] For example ; where indicates that if the task operation link needs to use resources, it is 1; otherwise it is 0; indicates the upper limit of the job links that the resources can serve simultaneously;

[0106] For example:​​​

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] wherein, is a 0-1 variable. If the horizontal transportation device uses resources within the time step, it is 1; otherwise it is 0. is a 0-1 variable. If the horizontal transportation device executes the operation step within the time step, it is 1; otherwise it is 0. is a set of resources related to the aerial rail system; is a 0-1 variable. If the horizontal transportation device in the TGV set executes the operation step within the time step, it is 1; otherwise it is 0. is the transportation step for the TGV to execute the task ; is a 0-1 variable. If the horizontal transportation device in the TGV set uses the loading and unloading resources (including parking) at the terminal within the time step, it is 1; otherwise it is 0. and are both a sufficiently large positive number; is a 0-1 variable. If the horizontal transportation device in the TGV set uses the loading and unloading resources (including parking) at the terminal within the time step, it is 1; otherwise it is 0. is a 0-1 variable. If the horizontal transportation device in the TGV set uses the loading and unloading resources (including parking) at the railway freight yard terminal within the time step, it is 1; otherwise it is 0. and are both a sufficiently large positive number; is a 0-1 variable. If the horizontal transportation equipment in the TGV set uses the loading and unloading resources (including parking) at the railway freight yard terminal within the time step then it is 1, otherwise it is 0; is a 0-1 variable. If the horizontal transportation equipment in the TGV set performs the operation link within the time step then it is 1, otherwise it is 0. is the number of TGV devices; Through the above formula, it can ensure the resource occupancy limit of TGV during different task conversions, ensure the coherence of the loading and unloading resources of the aerial ropeway system, and avoid task stagnation caused by resource conflicts.

[0114] Constraints are imposed on the relationship between each link of the operation task and the time step; For example:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] ;

[0122] Among them, is a 0-1 variable. If the link is executed after the time step then it is 1, otherwise it is 0; is a 0-1 variable. If the link is executed before the time step then it is 1, otherwise it is 0; is a 0-1 variable, indicating that if the link is executed within the time step then it is 1, otherwise it is 0; is a 0-1 variable, indicating that if the link is executed after the time step then it is 1, otherwise it is 0; is a 0-1 variable, indicating that if the link is executed before the time step then it is 1, otherwise it is 0; is a 0-1 variable, indicating that if the link is within the time step It is 1 if it is executed within, otherwise 0; is a 0-1 variable, indicating that if the link is executed after time step it is 1, otherwise 0; is a 0-1 variable, indicating that if the link is executed before time step it is 1, otherwise 0; is a continuous variable, indicating the start time of the link ; is the start time of time step ; is the next time step of; is a sufficiently large positive number; is a 0-1 variable. If the link is executed within time step it is 1, otherwise 0; is a continuous variable, indicating the end time of the link ; is a 0-1 variable. If the link is executed before time step it is 1, otherwise 0; is a 0-1 variable. If the link is executed after time step it is 1, otherwise 0;

[0123] Constraints on the time relationship of each operation link: For example:

[0124] ;

[0125] ;

[0126] ; ;

[0127] ;

[0128] ;

[0129] ;

[0130] ;

[0131] Among them, is a continuous variable, indicating the end time of the loading link for the AGV to execute task ; is a continuous variable, indicating the time required for the AGV to complete the loading link of task ; is the upper limit of the time window for the task ; is a continuous variable representing the end time of the transportation section when the AGV executes the task ; is a continuous variable representing the time required for the transportation section when the AGV completes the task ; is a continuous variable representing the end time of the unloading section when the AGV executes the task ; is a continuous variable representing the time required for the unloading section when the AGV completes the task ; is a continuous variable representing the end time of the loading section when the TGV executes the task ; is a continuous variable representing the time required for the loading section when the TGV completes the task ; is a continuous variable representing the end time of the transportation section when the TGV executes the task ; is a continuous variable representing the time required for the transportation section when the TGV completes the task ; is a continuous variable representing the end time of the unloading section when the TGV executes the task ; is a continuous variable representing the time required for the unloading section when the TGV completes the task; By the above formula, it can ensure that the time sequence of the operation sections is strictly defined (such as loading → transportation → unloading), which can force the task to be executed according to the physical process. For example, the AGV must complete loading before starting transportation; It can also define the delay time of the task (the difference between the actual completion time and the time window), quantify the task delay, and provide a calculation basis for the optimization objective. ;

[0132] Constraints are imposed on the time of the AGV and TGV during the task handover process; For example:

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] ;

[0161] Among them, is a continuous variable, representing the end moment of the no-load driving operation link in the process of the TGV performing task conversion; is a continuous variable, representing the time required for the no-load driving operation link in the process of the TGV performing task conversion; is a continuous variable, representing the time required for the no-load driving operation link in the process of the TGV performing task conversion; is a continuous variable, representing the time required for the no-load driving operation link in the process of the TGV performing task conversion; is a continuous variable representing the end time of the loading section when the TGV executes a task of the loading section; is a continuous variable representing the time required for the loading section when the TGV completes a task of the loading section; is a continuous variable representing the end time of the no-load driving operation section during the task conversion process of the AGV of the no-load driving operation section; is a continuous variable representing the time required for the no-load driving operation section during the task conversion process of the AGV of the no-load driving operation section; is the no-load driving operation section; is a continuous variable representing the end time of the loading section when the AGV executes a task of the loading section; is a continuous variable representing the time required for the loading section when the AGV completes a task of the loading section; Through the above formula, the time connection between the AGV and the TGV at the handover point (such as the unloading end time ≤ the no-load driving start time) can be clarified, as well as the gantry crane operation time limit during the handover of the export task and the import task, which can ensure seamless cooperation of the equipment during the handover process, avoid waiting or blocking; and coordinate the action timing of the ground equipment and the aerial rail equipment to reduce the handover delay.

[0162] Constrain the value range of the decision variable:

[0163] ;

[0164] ;

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] ;

[0173] ;

[0174] where, is the horizontal transportation equipment When performing tasks and tasks in the set of all tasks; is a 0-1 variable. If the horizontal transportation equipment uses resources in the time step then it is 1, otherwise it is 0. Through the above formula, a 0-1 variable can be defined to represent task allocation and equipment resource occupancy, and the upper and lower bounds of time variables can be defined (such as task start time ≥ ready time). It can transform the scheduling problem into a solvable discrete optimization problem and limit the reasonable range of variables to avoid non-physical solutions in the model (such as tasks starting before being ready).

[0175] The above constraint steps solve the resource competition problem by setting time connection constraints and resource capacity constraints, etc., ensuring seamless cooperation between AGV and TGV at the handover point.

[0176] Scheduling optimization module: Based on the multi-objective optimization algorithm, perform multi-objective optimization on the linear integer programming model, solve the optimized linear integer programming model, and obtain the optimal scheduling plan for AGV and TGV.

[0177] The methods for obtaining the optimal scheduling plan for AGV and TGV include:

[0178] Encode each AGV, TGV, and task type to obtain chromosome individuals. Each gene in the chromosome consists of two parts, including the equipment number of the AGV or TGV and the task type identifier. Among them, when the task type identifier is an overhead rail task, the task is assigned to the TGV, otherwise the corresponding gene is an invalid gene. The chromosome gene contains the equipment number and the task type identifier, which can ensure that tasks are allocated according to the job area, avoiding invalid cross-area allocation; reducing the equipment cross-area travel time (such as AGV not needing to enter the overhead rail area), shortening the total time consumption of the task chain; matching tasks with equipment types, reducing waiting due to equipment incompatibility (such as TGV not needing to handle internal terminal tasks); setting the way of invalid genes can make genes other than overhead rail tasks invalid, forcing the TGV to only execute import and export tasks; enabling the TGV to focus on long-distance transportation, improving the efficiency of the overhead rail system; avoiding waste of TGV resources on internal terminal tasks and ensuring the timely completion of import and export tasks.

[0179] Randomly generate an initial chromosome population to ensure that the allocation of each task satisfies the constraints. The initial chromosome population satisfies constraints such as task allocation and resource capacity, providing a feasible starting point for optimization.

[0180] Preset the ideal point with the minimum makespan and total tardiness. The ideal point is dynamically updated according to the individual with the minimum makespan and total tardiness in each generation. Updating the ideal point based on the optimal individual in each generation can guide the NSGA-II algorithm (Non-dominated Sorting Genetic Algorithm, a multi-objective optimization algorithm based on genetic algorithm) towards the optimal solution. Using the ideal point as a benchmark can drive the population to evolve towards a shorter makespan. The goal of minimizing the total tardiness of the ideal point can also prompt the algorithm to prioritize the repair of high-tardiness task chains.

[0181] Stratify the population according to the dominance relationship to obtain the non-dominated sorting result. Stratifying according to the dominance relationship can preferentially select solutions that optimize both the makespan and total tardiness, avoiding the situation of unilateral deterioration.

[0182] Calculate the Euclidean distance from each individual to the ideal point, and sort the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance.

[0183] Preset a distance threshold, and select individuals with Euclidean distances higher than the distance threshold from each layer as parents to enter the next generation. The distance threshold can be used to select evenly distributed individuals, maintain population diversity, and avoid local optima.

[0184] For tasks sharing the same loading and unloading area or consecutive tasks, perform a crossover operation to preferentially exchange associated task blocks, and detect and repair the crossover result to obtain repaired crossover individuals. Performing crossover on shared loading and unloading areas or consecutive tasks can reduce the waiting time between tasks.

[0185] Dynamically mutate the task assignment or timing in combination with the number of iterative updates to obtain mutated individuals. Adjusting the mutation intensity according to the number of iterations can explore new solutions in the early stage and make fine adjustments in the later stage.

[0186] For each non-dominated solution in the parent generation, identify the longest task chain and optimize the longest task chain through a tabu search strategy to obtain an optimized longest task chain. The tabu search strategy can shorten the idle time by exchanging adjacent task assignments every L iterations.

[0187] Replace the corresponding individuals in the parent generation with the optimized longest task chain to form the offspring population.

[0188] Merge the parent population, repair the crossover individuals, mutation individuals, and offspring population to obtain a new population. Perform non-dominated sorting on the individuals in the new population, and calculate the Euclidean distance from each individual in the new population to the ideal point. Sort the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance to obtain the updated non-dominated sorting result; retain the top W optimal individuals in the updated non-dominated sorting result; screening the top W optimal solutions through non-dominated sorting and Euclidean distance can ensure progress in each generation and accelerate the search to obtain the optimal scheduling plan.

[0189] Use the top W optimal individuals as the new parents for the next round of iteration;

[0190] Through J rounds of iteration, screen out the optimal scheduling plan with the minimum makespan and total tardiness.

[0191] The method for obtaining the non-dominated sorting result includes:

[0192] Step 1: Maintain a domination set and a domination counter for each individual, with the initial values set to an empty set and 0;

[0193] Step 2: Traverse the domination relationship between any pair of individuals in the population, judge the domination relationship, and count the domination set and the number of dominated individuals for each individual;

[0194] Step 3: Obtain all individuals with a domination count of 0, mark them as the first non-dominated individuals, summarize the first non-dominated individuals into the first layer of the non-dominated sorting result, and remove the first non-dominated individuals from the population to obtain an updated population;

[0195] Step 4: For the remaining individuals in the updated population, update the number of dominated individuals of the remaining individuals to obtain the updated number of dominated individuals of the remaining individuals. For each removed first non-dominated individual, traverse the corresponding domination set, and subtract 1 from the number of dominated individuals of each individual in the corresponding domination set to obtain the updated number of dominated individuals of the remaining individuals;

[0196] Step 5: Repeat Step 3 until all individuals are assigned to a certain layer of the non-dominated sorting result.

[0197] The method for judging the domination relationship includes:

[0198] If the makespan of individual f is not greater than the makespan of individual g, the total tardiness of individual f is not greater than the total tardiness of individual g, and any one of the objective values of individual f is less than the corresponding objective value of individual g, then it is judged that individual f dominates individual g, and the objective values include the makespan and the total tardiness.

[0199] The method for counting the domination set and the number of dominated individuals for each individual includes:

[0200] If individual p dominates individual q, add p to the domination set of q and increment the domination count of q by 1;

[0201] Traverse the domination relationships between individual p and all individuals to obtain the domination set and domination count corresponding to individual p.

[0202] The method for obtaining the longest task chain includes:

[0203] For each non-dominated solution in the parent generation, traverse its task assignment sequence. For the AGV task chain, accumulate the loading, transportation, unloading times and the no-load travel time of each task; for the TGV task chain, accumulate the loading, transportation, unloading times and the no-load travel time of each task; among them, comprehensively analyze the time constraints between tasks;

[0204] Select the task chain with the longest cumulative time as the longest task chain.

[0205] The method for obtaining the optimized longest task chain includes:

[0206] Every L iterations, swap the AGV or TGV assignment tasks of adjacent tasks on the longest task chain and shorten the no-load travel time according to a preset adjustment ratio; L can be dynamically adjusted according to the population diversity.

[0207] The above multi-objective optimization algorithm, such as the NSGA-II algorithm, shortens the no-load time of the longest task chain through tabu search, and also adjusts the mutation intensity according to the number of iterations, preferentially swapping shared loading and unloading areas or consecutive tasks to reduce waiting time, enhancing the search efficiency of the algorithm in the multi-device collaborative scenario. The genetic algorithm for separate scheduling lacks such targeted design.

[0208] Embodiment 2

[0209] This embodiment provides a joint scheduling optimization system for quay AGV and TGV in an aerial ropeway transportation, including:

[0210] Second Optimization Module: Taking the makespan and total tardiness as the state vector, and the crossover probability and mutation probability as the actions, the crossover probability and mutation probability are adjusted based on the reinforcement learning algorithm. Through the reinforcement learning framework, the real-time state (system performance metrics) is directly associated with the key parameters of the genetic algorithm (crossover / mutation probability) to achieve the adaptive optimization of the scheduling strategy. Specifically, when the system detects that the makespan is long, the reinforcement learning increases the mutation probability to explore a better task assignment sequence; if the total tardiness is high, the crossover strategy is dynamically adjusted to preferentially exchange task blocks with time window conflicts and reduce the tardiness. Through this feedback mechanism, the algorithm can balance global search and local exploitation under complex constraints (such as resource capacity, task handover time), continuously optimize the task assignment, timing planning, and resource utilization of AGV and TGV, and finally achieve the synchronous minimization of the makespan and total tardiness, significantly improving the operation efficiency and stability of the automated terminal.

[0211] As described above, only the specific embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0212] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A joint scheduling optimization system for quay AGV and TGV based on aerial ropeway transportation, characterized in that Including: Area division module: Collect the terminal operation area, divide the operation area, and obtain the operation area of the aerial rail system and the internal operation area of the automated terminal. The operation area of the aerial rail system includes an aerial rail collection and distribution operation subsystem for controlling the TGV to perform container transfer tasks between the two ends of a preset fixed track. Model planning module: With minimizing the makespan and minimizing the total delay time as the optimization objectives, construct a linear integer programming model, and set constraints on the operation processes of AGVs and TGVs according to the divided operation areas: Constrain the task assignment and operation sequence of AGVs and TGVs; Constrain the operation rules of AGVs and TGVs; Constrain the resource capacity; Constrain the relationship between each link of the operation task and the time step; Constrain the time relationship of each operation link: Constrain the time relationship of AGVs and TGVs during the task handover process; Constrain the value range of decision variables that control the task assignment, execution order, time arrangement, and resource occupancy of AGVs and TGVs; Scheduling optimization module: Perform multi-objective optimization on the linear integer programming model based on a multi-objective optimization algorithm, solve the optimized linear integer programming model, and obtain the optimal scheduling plan for AGVs and TGVs. Among them, the method for obtaining the optimized longest task chain includes: Every L iterations, exchange the task assignment of the AGV or TGV for adjacent tasks on the longest task chain, and shorten the no-load driving time according to a preset adjustment ratio; Take the makespan and the total delay time as the state vector, and the crossover probability and the mutation probability as the actions, and adjust the crossover probability and the mutation probability based on the reinforcement learning algorithm; Each gene in the chromosome includes the equipment number of the AGV or TGV and the task type identifier. When the task type identifier is an aerial rail task, the task is assigned to the TGV, otherwise the corresponding gene is an invalid gene.

2. The quay AGV and TGV joint scheduling optimization system based on aerial ropeway transportation according to claim 1, characterized in that, The method for dividing the operation area includes: Collect the terminal layout data, define the operation area from the shoreline to the yard as the internal operation area of the automated terminal, and define the aerial rail transportation network connecting the terminal and the railway freight yard as the operation area of the aerial rail system; The internal operation area of the automated terminal sequentially includes a horizontal transportation operation subsystem, a shoreline operation subsystem, and a yard operation subsystem from the shoreline to the yard. Among them, the shoreline operation subsystem is used to control the quay crane to load and unload containers, the horizontal transportation operation subsystem is used for the AGV to drive between the quay crane and the yard, and between the quay crane and the aerial rail terminal end, and the yard operation subsystem is used to manage the storage and scheduling of containers in the yard.

3. The dock AGV and TGV joint scheduling optimization system based on aerial ropeway transportation according to claim 1, wherein The method for obtaining the optimal scheduling plan for AGVs and TGVs includes: Encode each AGV, TGV, and task type to obtain chromosome individuals; Randomly generate an initial chromosome population; Preset the ideal point with both the makespan and the total delay time minimized; Stratify the population according to the dominance relationship to obtain the non-dominated sorting result; Calculate the Euclidean distance from each individual to the ideal point, and sort the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance; Select the individuals with the Euclidean distance higher than the preset distance threshold from each layer as the parents to enter the next generation; For tasks sharing the same loading and unloading area or being executed continuously, perform cross - operations on the exchange - related task blocks, detect and repair the cross - results to obtain repaired cross - individuals; Dynamically mutate the task assignment or timing in combination with the number of iterative updates to obtain mutated individuals; For each non - dominated solution in the parent generation, identify the longest task chain, and optimize the longest task chain through a tabu search strategy to obtain an optimized longest task chain; Replace the corresponding individuals in the parent generation with the optimized longest task chains to form an offspring population; Merge the parent generation, repaired cross - individuals, mutated individuals, and offspring population to obtain a new population, perform non - dominated processing on the individuals in the new population, and obtain the top W optimal individuals of the non - dominated processing results; Use the top W optimal individuals as the new parent generation for the next round of iteration; Through J rounds of iteration, screen out the optimized scheduling plan with the minimum makespan and total tardiness; 4. The quay AGV and TGV joint scheduling optimization system based on aerial ropeway transportation according to claim 3, characterized in that The method for obtaining the non - dominated sorting result includes: Step 1: Maintain a domination set and a domination counter for each individual, with the initial values set to an empty set and 0 respectively; Step 2: Traverse the domination relationship between any pair of individuals in the population, judge the domination relationship, and count the domination set and the number of dominated individuals for each individual; Step 3: Obtain all individuals with a domination count of 0, mark them as the first non - dominated individuals, summarize the first non - dominated individuals into the first layer of the non - dominated sorting result, and remove the first non - dominated individuals from the population to obtain an updated population; Step 4: For the remaining individuals in the updated population, update the number of dominated individuals of the remaining individuals to obtain the updated number of dominated individuals of the remaining individuals. For each removed first non - dominated individual, traverse the corresponding domination set, and subtract 1 from the number of dominated individuals of each individual in the corresponding domination set to obtain the updated number of dominated individuals of the remaining individuals; Step 5: Repeat Step 3 until all individuals are assigned to the non - dominated sorting result.

5. The optimized system for joint dispatching of quay AGV and TGV based on aerial ropeway transportation according to claim 4, characterized in that The method for judging the domination relationship includes: If the makespan of individual f is not greater than the makespan of individual g, the total tardiness of individual f is not greater than the total tardiness of individual g, and any one of the objective values of individual f is less than the corresponding objective value of individual g, then it is judged that individual f dominates individual g. The objective values include the makespan and the total tardiness.

6. The quay AGV and TGV joint scheduling optimization system based on aerial ropeway transportation according to claim 4, characterized in that, The method for counting the domination set and the number of dominated individuals for each individual includes: If individual p dominates individual q, add p to the domination set of q and increment the number of dominated individuals of q by 1; Traverse the domination relationship between individual p and all individuals to obtain the domination set and the number of dominated individuals corresponding to individual p.

7. The quay AGV and TGV joint scheduling optimization system based on aerial ropeway transportation according to claim 3, characterized in that The method for obtaining the longest task chain includes: For each non - dominated solution in the parent generation, traverse its task assignment sequence. For the AGV task chain, accumulate the loading, transportation, unloading time and the no - load driving time of each task; for the TGV task chain, accumulate the loading, transportation, unloading time and the no - load driving time of each task; among them, comprehensively analyze the time constraints between tasks; Select the task chain with the longest cumulative time as the longest task chain.

8. The optimized joint scheduling system for terminal AGV and TGV based on aerial ropeway transportation according to claim 3, characterized in that The method for obtaining the top W optimal individuals of the non - dominated processing result includes: Perform non-dominated sorting on the individuals in the new population, calculate the Euclidean distance from each individual in the new population to the ideal point, sort the individuals in each layer of the non-dominated sorting result in ascending order according to the value of the Euclidean distance, and obtain the updated non-dominated sorting result; retain the top W optimal individuals in the updated non-dominated sorting result as the top W optimal individuals of the non-dominated processing result.

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