Dry bulk cargo terminal storage yard unmanned truck scheduling optimization method based on substitution graph model

Abstract

The invention relates to the technical field of wharf unmanned vehicle scheduling, in particular to a dry bulk wharf storage yard unmanned truck scheduling optimization method based on a substitution graph model, and the method comprises the steps: determining the priority of an unloading point according to the demand quantity of the unloading point and the historical unloading frequency, introducing real-time state monitoring, and dynamically adjusting the weight of the priority; based on the selected unloading point, distributing an optimal driving path for the vehicle, calculating an initial passing time window, and generating a multi-vehicle time window set; modeling the passing process of the unmanned truck by using a substitution graph model, constructing a mixed integer programming model, and dynamically adjusting a decision by using a rolling time domain optimization strategy; solving the mixed integer programming model by taking minimization of the overall completion time and balance operation time as targets to obtain an optimal scheduling scheme of each vehicle; according to the method, through unloading point selection and scheduling optimization based on the substitution graph model, the scheduling efficiency of the unmanned trucks in the storage yard of the wharf is improved, and powerful support is provided for automation and intellectualization of logistics operation of the wharf.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned vehicle scheduling at a terminal, and in particular to an unmanned truck scheduling optimization method for a dry bulk cargo terminal yard based on a substitution graph model. Background Art

[0002] In the operation process of dry bulk cargo terminals, the yard is the core area for material transfer and storage, and the unmanned vehicle, as the main means of transportation in the yard, is responsible for transporting materials from the unloading point to the designated stacking position in the yard. The rationality and efficiency of the unmanned vehicle dispatch directly affects the operating efficiency and logistics costs of the entire terminal.

[0003] Existing unmanned truck scheduling methods are mainly based on fixed paths and simple priority rules, such as first-in-first-out (FIFO) scheduling or scheduling based on the shortest path. However, these traditional methods are prone to vehicle conflicts, path congestion, scheduling delays and other problems when faced with complex yard environments, which in turn affects the overall operation efficiency. In addition, the dynamic and uncertain nature of yard operations, such as changes in loading and unloading points and fluctuations in vehicle flow, further exacerbate the complexity of unmanned truck scheduling.

[0004] In order to solve the above problems, some automatic guided vehicle (AGV) scheduling models and algorithms have been applied in recent years, such as graph-based path planning algorithms, dynamic resource reservation mechanisms, etc. These methods improve the flexibility and efficiency of automatic guided vehicle scheduling by optimizing path selection, dynamically allocating resources, and adjusting scheduling plans in real time.

[0005] However, existing technologies still have limitations when applied to unmanned truck scheduling. First, these methods fail to achieve refined control of the speed of unmanned trucks and cannot adapt to the diverse operational needs of trucks. Second, some methods rely on rule-driven methods and lack rigorous mathematical models as support, resulting in limited optimization effects.

[0006] In view of these shortcomings, the present invention proposes an unmanned truck scheduling optimization method suitable for dry bulk terminal yards, aiming to improve scheduling efficiency and operational performance. Summary of the invention

[0007] To achieve the above objectives, the present invention provides a method for optimizing the dispatch of unmanned trucks in a dry bulk cargo terminal yard based on a substitution graph model. The specific technical solution is as follows: Based on the demand and historical unloading frequency of the unloading points, the current workload and usage frequency of each unloading point are evaluated, the priority of each unloading point is determined, real-time status monitoring of the unloading points is introduced, the priority weight is dynamically adjusted, and the unloading points are selected according to the priority; Based on the selected unloading point, the optimal driving path to the corresponding unloading point is assigned to the vehicle, and the initial travel time window of each vehicle is calculated according to the distance of the path and the optimal driving speed; based on the calculation results, a set of travel time windows for multiple vehicles is generated; Based on the substitution graph model, the travel process of unmanned trucks is modeled, and the travel time, path conflict and travel sequence factors are considered to construct a mixed integer programming model to calculate the optimal scheduling plan for unmanned trucks. Adopt rolling time domain optimization strategy to dynamically adjust decisions based on real-time data during scheduling execution; Taking minimizing the overall completion time as the optimization goal and considering the balance of the operating time of each unmanned truck, the mixed integer programming model is solved to determine the time window and passage order of each vehicle on the passage path.

[0008] Preferably, according to the unloading point capacity, whether the unloading point is an available unloading point is marked; according to the unloading frequency of the unloading point, the unloading point priority is calculated; according to the unloading point capacity constraint and priority setting, the available unloading point set is determined; The unloading points in the available unloading point set are sorted from high to low according to the unloading point priority, and the unloading point with the highest priority is selected as the target unloading point of the current unloading task.

[0009] Preferably, a real-time monitoring mechanism for the status of the unloading point is introduced to dynamically track the status parameters of the unloading point, including: obtaining the real-time unloading volume of the unloading point, calculating the real-time idle capacity of the unloading point, and obtaining the real-time number of queued vehicles at the unloading point; When the number of real-time queued vehicles at an unloading point exceeds the threshold of the number of queued vehicles, the unloading point coordinated allocation mechanism is triggered, including: searching for idle unloading points in the vicinity of the unloading point, that is, guiding the queued vehicles at the unloading point to the idle unloading point.

[0010] Preferably, a road network topology map is constructed based on the selected unloading point and the road network layout of the terminal; Use the A* algorithm to calculate the shortest path for the vehicle; According to the road network topology and A* algorithm, the vehicle-path-time scheduling information is calculated.

[0011] Preferably, a substitution graph model is used to model the passage process of the unmanned truck; Define alternative graphical models and decision variables, and build a mixed integer programming model to solve the planning and scheduling of unmanned trucks with the goal of minimizing the scheduling time of unmanned trucks.

[0012] Preferably, the entire scheduling time range is divided into A continuous scheduling time window; Based on the constructed substitution graph model and mixed integer programming model, the optimal path and corresponding time window of each unmanned truck are solved.

[0013] Preferably, the optimization goal is to minimize the overall completion time, while considering the balance of the operating time of each unmanned truck, solve the mixed integer programming model, and determine the time window and passage order of each unmanned truck on the passage path.

[0014] The unmanned truck dispatch optimization system for dry bulk cargo terminal yard based on substitution graph model is implemented based on the unmanned truck dispatch optimization method for dry bulk cargo terminal yard based on substitution graph model, including: unloading point selection module, travel time calculation module, dispatch calculation module, rolling optimization module and multi-objective optimization module; The unloading point selection module evaluates the current workload and usage frequency of each unloading point based on the demand for unloading points and the historical unloading frequency, determines the priority of each unloading point, introduces real-time status monitoring of unloading points, dynamically adjusts the priority weight, and selects unloading points according to the priority; The travel time calculation module allocates the optimal travel path to the corresponding unloading point to the vehicle based on the selected unloading point, calculates the initial travel time window of each vehicle according to the distance of the path and the optimal travel speed; and generates a set of travel time windows for multiple vehicles based on the calculation results; The scheduling calculation module models the travel process of the unmanned truck based on the substitution graph model, considers the travel time, path conflict and travel sequence factors, constructs a mixed integer programming model, and calculates the optimal scheduling plan for the unmanned truck; The rolling optimization module adopts a rolling time domain optimization strategy to dynamically adjust decisions according to real-time data during the scheduling execution process; The multi-objective optimization module takes minimizing the overall completion time as the optimization goal, while considering the balance of the operating time of each unmanned truck, solving the mixed integer programming model, and determining the time window and passage order of each vehicle on the passage path.

[0015] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model by calling the computer program stored in the memory.

[0016] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model.

[0017] Beneficial effects of the present invention: The present invention dynamically evaluates the real-time status of the unloading point, determines the target unloading point based on the comprehensive capacity and queuing situation, triggers the coordinated deployment mechanism to achieve load balancing, allocates the optimal unloading point to the vehicle, and improves resource utilization efficiency.

[0018] The present invention constructs a dynamic road network topology based on the selected unloading point, uses the A* algorithm to calculate the shortest path, combines the road section capacity with the real-time congestion to adjust the path, generates the vehicle path and time window set, and ensures efficient traffic.

[0019] The present invention describes the relationship between vehicle traffic conflicts and sequences through an alternative graph model, constructs a mixed integer programming model to minimize the overall completion time, solves the conflict problem between vehicles on the path and unloading points, and optimizes the vehicle traffic sequence and time allocation.

[0020] The present invention divides the scheduling time window, updates the vehicle status and environmental parameters in real time, dynamically solves the optimization model to adjust the vehicle path and time window, adapts to random events and uncertainties, and continuously optimizes the execution effect of the scheduling plan.

[0021] The present invention takes minimizing the overall completion time as the main goal, while balancing the differences in vehicle operation time, constructing a weighted optimization goal to comprehensively consider priority and balance, and improving the fairness and overall efficiency of unmanned truck scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of the unmanned truck dispatch optimization method for a dry bulk cargo terminal yard based on a substitution graph model provided by the present invention; Figure 2 A simplified traffic scenario for the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model provided by the present invention.

[0023] Figure 3 A substitution graph model for the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model provided by the present invention.

[0024] Figure 4 The substitution graph solution result of the dry bulk terminal yard unmanned truck scheduling optimization method based on the substitution graph model provided by the present invention.

[0025] Figure 5 This is a structural diagram of the unmanned truck scheduling optimization system for the dry bulk terminal yard based on the substitution graph model provided by the present invention. DETAILED DESCRIPTION

[0026] In order to better understand the present invention, a more detailed description will be made of various aspects of the present invention with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0027] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0028] It should also be understood that expressions such as "comprises", "including", "having", "includes" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention". And, the term "exemplary" is intended to refer to an example or illustration.

[0029] Unless otherwise defined, all terms (including engineering terms and scientific and technological terms) used in this document have the same meaning as commonly understood by ordinary technicians in the field to which the present invention belongs. It should also be understood that unless otherwise clearly stated in the present invention, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0030] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] Example 1 Reference Figure 1 , which is the first embodiment of the present invention, provides an unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model.

[0032] Step 1: Based on the demand and historical unloading frequency of the unloading points, evaluate the current workload and usage frequency of each unloading point, determine the priority of each unloading point, introduce real-time status monitoring of the unloading points, dynamically adjust the priority weights, and select unloading points based on the priority.

[0033] Shared unloading points, each unloading point is numbered , ;definition Unload point The maximum capacity of The current uninstall point The cumulative unloading amount; when When uninstalling is marked as an available uninstall point, otherwise the point will be uninstalled Marked as an unavailable uninstall point.

[0034] Introducing unloading points Uninstall frequency , which indicates the unloading point per unit time The average number of unloading times; define the unloading point The priority is ,and Inversely proportional, that is, priority The higher the value, the higher the unloading point. The one with lower usage frequency should be selected first.

[0035] Determine the set of available unloading points based on unloading point capacity constraints and priority settings , select the unloading points according to the capacity constraint, for any unloading point ,like , then the uninstall point Put into the set of available uninstall points In the process, the unloading points that exceed the capacity are marked as unavailable unloading points for a period of time, and all available unloading points are selected. If all unloading points are unavailable, the delayed scheduling mechanism is triggered; when all available unloading points are integrated into an available unloading point set .

[0036] Set of available unloading points The unloading points in the system are ranked according to the unloading point priority. Sort from high to low and select the uninstall point with the highest priority As the target unloading point of the current unloading task.

[0037] Introduce a real-time monitoring mechanism for the status of unloading points to dynamically track the status parameters of unloading points, including: obtaining the status of unloading points Real-time uninstall volume , calculate the unloading point Real-time free capacity , get the uninstall point Real-time number of queued vehicles .

[0038] When there is an unloading point Real-time number of queued vehicles Exceeding the threshold of number of vehicles in queue When the unloading point coordination mechanism is triggered, it includes: Search for idle unloading points in the vicinity of ,Right now , the uninstall point Queued vehicles are directed to the unloading point , update the real-time status parameters of the two unloading points, perform the unloading point selection step for all vehicles, and achieve load balancing between unloading points. The idle capacity threshold of the uninstall point.

[0039] Step 1 dynamically evaluates the real-time status and historical frequency of the unloading point, determines the target unloading point based on the comprehensive capacity and queuing situation, triggers the coordinated dispatching mechanism to achieve load balancing, allocates the optimal unloading point to the vehicle, and improves resource utilization efficiency.

[0040] Step 2: Based on the selected unloading point, the optimal driving path to the corresponding unloading point is assigned to the vehicle. According to the distance of the path and the optimal driving speed, the initial travel time window of each vehicle is calculated; based on the calculation results, a set of multi-vehicle travel time windows is generated, which includes the travel path of each vehicle and its corresponding time window.

[0041] According to the selected unloading point , combined with the road network layout of the terminal, construct a road network topology map ,in is the set of road network nodes, A collection of road sections.

[0042] For any road segment , and define its length as , the traffic capacity is ; For each vehicle to be dispatched , get the vehicle Current location coordinates , the coordinates Map to the nearest road network node .

[0043] Using the A* algorithm, As a starting point, As the end point, calculate the vehicle The shortest path , get the number sequence of each section on the path ,in For path The number of road sections, Indicates the path The last segment number on the .

[0044] Combined with the length of each section and vehicles The best driving speed , calculate vehicle Driving time through each road segment , get the vehicle On the path Total travel time on .

[0045] Combined with the road capacity limit, for any road section , according to the road section capacity , calculate the maximum number of vehicles allowed to pass per unit time ,in is the safe distance between vehicles. is the floor function.

[0046] Combined with the road section capacity restrictions, vehicles Path Make real-time adjustments; if the path There are sections in The number of vehicles passing through will exceed , then in The starting node is the vehicle Re-plan an avoidance path , so that the vehicle Avoid congested roads and update vehicles route information and travel time.

[0047] According to the above calculation, the path set of all vehicles to be dispatched is calculated and the corresponding set of travel times , forming vehicle-path-time scheduling information, Indicates Paths, Indicates The travel time of a path.

[0048] Step 2 constructs a dynamic road network topology based on the selected unloading point, uses the A* algorithm to calculate the shortest path, combines the road section capacity with real-time congestion to adjust the path, generates the vehicle's path and time window set, and ensures efficient traffic.

[0049] Step 3: Based on the substitution graph model, the travel process of the unmanned truck is modeled, considering the travel time, path conflict and travel sequence factors, and a mixed integer programming model is constructed to calculate the optimal scheduling plan for the unmanned truck.

[0050] Use the substitution graph model to model the traffic process of unmanned trucks; define the substitution graph ,in For the operation set, is a fixed arc set, is the set of alternative arcs; Operation Collection Represents all possible driving states of the unmanned truck during the dispatch process. Each operation corresponds to a road section or an unloading point. Indicates the initial state of all vehicles and ends the operation Represents the final state of all vehicles completing the scheduling task; the set The elements in are non-negative integers, representing the numbers of the operations.

[0051] Fixed Arc Set represents the driving path of the unmanned truck in the road network, arc Indicates that the vehicle is operated Location driving to operation Location; arc Weight Indicates that the vehicle is operated To Operation The required travel time can be calculated in advance based on the length of the road section and the vehicle speed.

[0052] Alternative Arc Set Used to describe the order of different vehicles on the same road section or unloading point; alternative arc Indicates that there is a conflict in the order of passing between two vehicles on the road section or unloading point, and the order needs to be determined; arc Weight It indicates the safe driving interval time between two vehicles, that is, the latter vehicle needs to wait for the previous vehicle to pass before starting to pass. This time can be pre-set according to the vehicle speed, acceleration and safety distance requirements.

[0053] According to the set of replacement arcs Generate Selection Set , used to determine the actual passing order of each pair of vehicles with conflicting passing orders; for each pair of alternative arcs ,gather Contains two elements and , representing two possible passage orders.

[0054] When solving the substitution graph model, for each pair of substitution arcs, only one element can be selected from the corresponding selection set, that is, the passage order of the two vehicles is determined.

[0055] Defining decision variables: continuous variables Indicates vehicle In performing operations The start time of ,when When the vehicle performs the operation first Re-execute the operation ,when When the vehicle performs the operation first Re-execute the operation .

[0056] A mixed integer programming model is constructed. The objective function is to minimize the overall completion time. The constraints include: ; in, is a positive natural number, A collection of all unmanned trucks involved in the dispatch; Respectively represent vehicles In operation ,operate ,vehicle In operation ,operate and vehicles In operation The start time of Indicates operation In the fixed arc set The subsequent operations in Indicates that the vehicle is in operation To Operation The optimal travel time, n is the end operation.

[0057] The first constraint in the mixed integer programming model is used to ensure that the vehicles follow the planned path sequence; For vehicles and vehicles The second and third constraints are used to ensure the safe driving between vehicles. When Before the vehicle Pass through the conflicting road section or unloading point, otherwise the vehicle Pass first.

[0058] Solve the above mixed integer programming model and get the decision variables and By using the optimal value of , we can determine the optimal travel time and travel order of each unmanned truck on each road section and obtain a complete scheduling plan.

[0059] In order to cope with the uncertainties and random events in the actual scheduling process, a rolling time domain optimization strategy is adopted to dynamically adjust the scheduling plan. The entire scheduling time domain is divided into several continuous scheduling cycles. At the beginning of each scheduling cycle, the relevant parameters in the substitution graph model are updated according to the current vehicle status information and environmental parameters, and the mixed integer programming model is re-solved to obtain new scheduling decisions, thereby realizing rolling optimization of the scheduling plan.

[0060] Step 3 describes the relationship between vehicle traffic conflicts and sequences through a substitution graph model, constructs a mixed integer programming model to minimize the overall completion time, solves the conflict between vehicles on the path and unloading points, and optimizes the vehicle traffic sequence and time allocation.

[0061] Step 4: Adopt a rolling time domain optimization strategy to dynamically adjust decisions based on real-time data during the scheduling execution process.

[0062] The entire scheduling time range Divide into continuous scheduling time windows, each window length is , Represents the total length of the entire scheduling time range, The window is recorded as ,in , For the The start time of a scheduling time window.

[0063] definition is the set of vehicles that have not completed their tasks at the end of the previous scheduling cycle. is the set of vehicles that have just arrived in the current scheduling cycle and are waiting for assignment. The total set of vehicles in the current scheduling cycle is .

[0064] for Each car in , according to its Position coordinates at the moment , update its remaining path and estimated time of arrival .

[0065] Based on the constructed alternative graph model, The vehicles in are taken as input, and the mixed integer programming model is solved to obtain the scheduling period The optimal path for each vehicle and the corresponding time window , It is a vehicle The start time of the scheduling time window, It is a vehicle The end time of the scheduled time window.

[0066] According to the optimization results, update The remaining path and estimated arrival time of each vehicle in The vehicles that have been assigned tasks are moved into , waiting for the next scheduling cycle to continue scheduling; make , repeat the vehicle scheduling until all scheduling cycles are completed, that is, .

[0067] Step 4 divides the scheduling time window, updates the vehicle status and environmental parameters in real time, dynamically solves the optimization model to adjust the vehicle path and time window, adapts to random events and uncertainties, and continuously optimizes the execution effect of the scheduling plan.

[0068] Step 5: Taking minimizing the overall completion time as the optimization goal, while considering the balance of the operating time of each unmanned truck, solve the mixed integer programming model to determine the time window and passage order of each vehicle on the passage path.

[0069] definition is the maximum completion time of the overall task, where For vehicles The total travel time, is the set of all unmanned trucks involved in the dispatch; the optimization goal is to minimize ,Right now ; In order to balance the operating time of each vehicle, the vehicle operating time deviation is introduced ,in is the average operating time of all vehicles.

[0070] Defining Total Deviation , as the secondary optimization objective, that is ;Introduce weight coefficient , construct a weighted optimization objective , by adjusting The value of controls the relative importance of the balance between maximum completion time and job time.

[0071] To ensure that vehicles travel evenly to the unloading point, vehicle priority is introduced and uninstall point priority ; Set the priority of vehicles that have been dispatched to be higher than that of vehicles that have not been dispatched to avoid deadlock caused by early dispatch of vehicles; At the same time, add the priority of unloading points to prevent excessive concentration of unloading points and ensure balanced scheduling; Use the weighted product method to adjust the objective function, consider the impact of different priority factors on the scheduling results, and set the objective function as follows: ; in, Indicates vehicle The completion time, and Respectively represent vehicles and the corresponding unloading point priority.

[0072] Step 5 takes minimizing the overall completion time as the main goal, while balancing the differences in vehicle operation time, constructing a weighted optimization goal that comprehensively considers priority and balance, and improving the fairness and overall efficiency of unmanned truck scheduling.

[0073] Example 2 Reference Figures 2 to 4 ,The second embodiment of the present invention provides a simplified traffic scenario of the ,unmanned truck scheduling optimization method for a dry bulk terminal yard based on ,alternative graph model.

[0074] Figure 2 A simplified traffic scenario in the yard is shown, which includes three vehicles: two vehicles A and B perform entry transportation operations, and one vehicle C performs exit transportation operations; in this scenario, the tasks of vehicles A and B are to enter from outside the yard and enter the designated unloading area, while vehicle C drives out from the yard after completing the unloading operation.

[0075] Figure 2 The line between the two blue dots in the figure represents the path samples, and each path has a corresponding number.

[0076] Due to the restrictions of the yard area, overtaking is prohibited on the internal routes of the yard, and there is only a parking area that can be occupied simultaneously at the entrance. Other path resources cannot be occupied simultaneously; each vehicle must strictly follow the route specified in the figure, and the vehicle re-route planning issue is not considered.

[0077] The reference tracking path samples of the three vehicles are shown as follows: the driving path of vehicle A is: 2636, 3150, 2623, 2620, (2620), 2545, 2609, 2608, 2579, 2605, 2603, 2600, 2597, 2593, 2592, (2620) indicates the path point repeated with 2620.

[0078] The driving path of vehicle B is: 2636, 3150, 2623, 2620, (2620), 2971, 2611, 2580, 2582, 2581, 2602, 2601, 2596, 2595, 2594.

[0079] The driving path of vehicle C is: 2590, 2591, 2598, 2599, 2604, 2578, 2606, 2607, 2610, 2621, 2636.

[0080] Among them, the path samples: (3105, 2621), (2623, 2621), (2623, 2545), (2623, 2971), (2545, 2971), (2621, 2545), (2621, 2971) are associated paths.

[0081] Figure 3 shows an alternative graph model for the above example, in Figure 3 In , each node represents the operating status of a vehicle, for example, node (1, 2636) represents the operation of vehicle A on path 2636.

[0082] The vehicle's travel path is represented by each node in turn, solid arrows represent fixed arcs, and dashed and colored arrows represent alternative arcs.

[0083] There are 8 conflicts between vehicle A and vehicle B, represented by substitution arcs; there are 4 conflicts between vehicle A and vehicle C, and between vehicle B and vehicle C.

[0084] For the same-direction path conflict between A and B, five alternative arcs are constructed: ((2, 3150; 1, 2636), (1, 3150; 2, 2636); ((2,2623;1,3150), (1,2623;2,3150)); ((2,2620;1,2623),(1,2620;2,2623)); ((2,26201;1,2620),(1,26201;2,2620)); ((2, 2971; 1, 26201), (1, 2545; 2, 26201)).

[0085] In addition, there is a cross-path conflict between A and B, and three alternative arcs are constructed: ((2,2611; 1,2545), (1,2609; 2,2971)); ((2,2611;1,2623),(1,2620;2,2971)); ((2,2620;1,2609),(1,2609;2,2623)).

[0086] For the reverse path conflict between A and C, an alternative arc is constructed: ((3, out; 1, 2636)(1, 3150; 3, 2636)), where (3, out: 1, 2636) means starting from the output terminal 1 of node 3 and connecting to node 2636.

[0087] Similarly, there is a cross-path conflict between A and C, and three alternative arcs are constructed: ((3, 2636; 1, 3150)(1, 2623; 3, 2621)), ((3, 2636; 1, 2623)(1, 2620; 3, 2621)), and ((3, 2636; 1, 2545)(1, 2609; 3, 2621)).

[0088] The construction logic of the four alternative arcs between B and C is similar to the above.

[0089] Figure 4 Describes the solution to the scheduling problem, starting from Figure 4 The operation order of each vehicle can be seen in the figure. Vehicle A enters the yard before vehicle B, vehicle C passes through the intersection area after vehicle A, and vehicle B starts to enter the yard after vehicle C leaves the yard. This order ensures that path conflicts between vehicles are effectively avoided.

[0090] Example 3 Reference Figure 5 ,The third embodiment of the present invention provides an unmanned truck dispatch optimization system for dry bulk cargo terminal yard based on substitution graph model; The system comprises: an unloading point selection module, a travel time calculation module, a scheduling calculation module, a rolling optimization module and a multi-objective optimization module.

[0091] The unloading point selection module evaluates the current workload and usage frequency of each unloading point based on the demand for unloading points and historical unloading frequency, determines the priority of each unloading point, introduces real-time status monitoring of the unloading points, dynamically adjusts the priority weights, and selects unloading points based on the priorities.

[0092] The travel time calculation module allocates the optimal driving path to the corresponding unloading point to the vehicle based on the selected unloading point, calculates the initial travel time window of each vehicle according to the distance of the path and the optimal driving speed; and generates a set of travel time windows for multiple vehicles based on the calculation results.

[0093] The scheduling calculation module models the travel process of the unmanned truck based on the substitution graph model, considers the travel time, path conflict and travel sequence factors, constructs a mixed integer programming model, and calculates the optimal scheduling plan for the unmanned truck.

[0094] The rolling optimization module adopts a rolling time domain optimization strategy to dynamically adjust decisions according to real-time data during the scheduling execution process.

[0095] The multi-objective optimization module takes minimizing the overall completion time as the optimization goal, while considering the balance of the operating time of each unmanned truck, solving the mixed integer programming model, and determining the time window and passage order of each vehicle on the passage path.

[0096] Example 4 The present invention also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the above-mentioned method for optimizing the unmanned truck scheduling in a dry bulk terminal yard based on a substitution graph model may be executed.

[0097] The method or system according to the embodiment of the present invention may also be implemented with the aid of the architecture of the electronic device of the present invention.

[0098] The invention also discloses a computer-readable storage medium.

[0099] The computer-readable storage medium has computer-readable instructions stored thereon.

[0100] When the computer-readable instructions are executed by the processor, the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model according to the embodiments of the present invention described with reference to the above drawings can be executed.

[0101] The above sequence for the steps of the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless otherwise specifically stated.

[0102] In addition, in some embodiments, the present invention can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Therefore, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0103] In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0104] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method for unmanned truck dispatching in dry bulk cargo terminal yard based on substitution graph model, characterized in that: include: Based on the demand and historical unloading frequency of the unloading points, the current workload and usage frequency of each unloading point are evaluated, the priority of each unloading point is determined, the real-time status monitoring of the unloading point is introduced, the priority weight is dynamically adjusted, and the unloading point is selected according to the priority; Based on the selected unloading point, the optimal driving path to the corresponding unloading point is assigned to the vehicle, and the initial travel time window of each vehicle is calculated according to the distance of the path and the optimal driving speed; based on the calculation results, a set of travel time windows for multiple vehicles is generated; Based on the substitution graph model, the travel process of unmanned trucks is modeled, and the travel time, path conflict and travel sequence factors are considered to construct a mixed integer programming model to calculate the optimal scheduling plan for unmanned trucks. Adopt rolling time domain optimization strategy to dynamically adjust decisions based on real-time data during scheduling execution; Taking minimizing the overall completion time as the optimization goal and considering the balance of the operating time of each unmanned truck, the mixed integer programming model is solved to determine the time window and passage order of each vehicle on the passage path.

2. The method for optimizing the dispatching of unmanned trucks at a dry bulk cargo terminal yard based on a substitution graph model according to claim 1, characterized in that: According to the unloading point capacity, mark whether the unloading point is an available unloading point; calculate the unloading point priority according to the unloading frequency of the unloading point; determine the available unloading point set according to the unloading point capacity constraint and priority setting; The unloading points in the available unloading point set are sorted from high to low according to the unloading point priority, and the unloading point with the highest priority is selected as the target unloading point of the current unloading task.

3. The method for optimizing the dispatching of unmanned trucks at a dry bulk cargo terminal yard based on a substitution graph model according to claim 2, characterized in that: Introduce a real-time monitoring mechanism for the status of unloading points to dynamically track the status parameters of unloading points, including: obtaining the real-time unloading volume of unloading points, calculating the real-time idle capacity of unloading points, and obtaining the real-time number of queued vehicles at unloading points; When the number of real-time queued vehicles at an unloading point exceeds the threshold of the number of queued vehicles, the unloading point coordinated allocation mechanism is triggered, including: searching for idle unloading points in the vicinity of the unloading point, that is, guiding the queued vehicles at the unloading point to the idle unloading point.

4. The method for optimizing unmanned truck dispatching in a dry bulk cargo terminal yard based on a substitution graph model according to claim 3 is characterized in that: According to the selected unloading point and the road network layout of the terminal, a road network topology map is constructed; Use the A* algorithm to calculate the shortest path for the vehicle; According to the road network topology and A* algorithm, the vehicle-path-time scheduling information is calculated.

5. The method for optimizing unmanned truck dispatching at a dry bulk cargo terminal yard based on a substitution graph model according to claim 4, characterized in that: Modeling the traffic process of unmanned trucks using a substitution graph model; Define alternative graphical models and decision variables, and build a mixed integer programming model to solve the planning and scheduling of unmanned trucks with the goal of minimizing the scheduling time of unmanned trucks.

6. The method for optimizing unmanned truck dispatching at a dry bulk cargo terminal yard based on a substitution graph model according to claim 5, characterized in that: Divide the entire scheduling time range into A continuous scheduling time window; Based on the constructed substitution graph model and mixed integer programming model, the optimal path and corresponding time window of each unmanned truck are solved.

7. The method for optimizing the dispatching of unmanned trucks in a dry bulk cargo terminal yard based on a substitution graph model according to claim 6, characterized in that: Taking minimizing the overall completion time as the optimization goal and considering the balance of the operating time of each unmanned truck, the mixed integer programming model is solved to determine the time window and passage order of each unmanned truck on the passage path.

8. A dry bulk cargo terminal yard unmanned truck scheduling optimization system based on a substitution graph model, which is implemented based on the dry bulk cargo terminal yard unmanned truck scheduling optimization method based on a substitution graph model according to any one of claims 1 to 7, characterized in that: include: Unloading point selection module, travel time calculation module, scheduling calculation module, rolling optimization module and multi-objective optimization module; The unloading point selection module evaluates the current workload and usage frequency of each unloading point based on the demand for unloading points and the historical unloading frequency, determines the priority of each unloading point, introduces real-time status monitoring of unloading points, dynamically adjusts the priority weight, and selects unloading points according to the priority; The travel time calculation module allocates the optimal travel path to the corresponding unloading point to the vehicle based on the selected unloading point, calculates the initial travel time window of each vehicle according to the distance of the path and the optimal travel speed; and generates a set of travel time windows for multiple vehicles based on the calculation results; The scheduling calculation module models the travel process of the unmanned truck based on the substitution graph model, considers the travel time, path conflict and travel sequence factors, constructs a mixed integer programming model, and calculates the optimal scheduling plan for the unmanned truck; The rolling optimization module adopts a rolling time domain optimization strategy to dynamically adjust decisions according to real-time data during the scheduling execution process; The multi-objective optimization module takes minimizing the overall completion time as the optimization goal, while considering the balance of the operating time of each unmanned truck, solving the mixed integer programming model, and determining the time window and passage order of each vehicle on the passage path.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the unmanned truck scheduling optimization method for a dry bulk terminal yard based on a substitution graph model as described in any one of claims 1 to 7.

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