Optimization Method for Unmanned Truck Scheduling in Dry Bulk Terminal Yard Based on Alternative Graph Model

Abstract

The present invention relates to the technical field of unmanned vehicle scheduling in ports, specifically an optimization method for unmanned truck scheduling in the yard of dry bulk terminals based on an alternative graph model. The method includes: determining the priority of unloading points according to the demand at the unloading points and the historical unloading frequency, and introducing real-time status monitoring to dynamically adjust the priority; based on the selected unloading points, allocating the optimal driving path for the vehicles, calculating the initial passing time window, and generating a multi-vehicle time window set; using the alternative graph model to model the passing process of unmanned trucks, constructing a mixed integer programming model, and adopting a rolling horizon optimization strategy to dynamically adjust the decision; aiming at minimizing the overall completion time and balancing the operation time, solving the mixed integer programming model to obtain the optimal scheduling plan for each vehicle. The present invention improves the scheduling efficiency of unmanned trucks in the yard of the port through unloading point selection and scheduling optimization based on the alternative graph model, and provides strong support for the automation and intelligence of port logistics operations.

Description

Technical Field

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

[0002] During the operation of a dry bulk cargo terminal, the yard is the core area for material transfer and storage. As the main transportation tool in the yard, unmanned vehicles are responsible for transporting materials from the ship unloading point to the designated stacking position in the yard. The rationality and efficiency of unmanned vehicle scheduling directly affect the operation efficiency and logistics cost 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 shortest-path-based scheduling. However, when facing a complex yard environment, these traditional methods are prone to problems such as vehicle conflicts, path congestion, and scheduling delays, thus affecting the overall operation efficiency. In addition, the dynamics and uncertainties 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] To solve the above problems, in recent years, some automatic guided vehicle (AGV) scheduling models and algorithms have been applied, such as path planning algorithms based on graph theory and dynamic resource reservation mechanisms. These methods improve the flexibility and operation efficiency of automatic guided vehicle scheduling by optimizing path selection, dynamically allocating resources, and real-time adjusting the scheduling plan.

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

[0006] In view of these deficiencies, the present invention proposes an optimization method for unmanned truck scheduling in the yard of a dry bulk cargo terminal, aiming to improve scheduling efficiency and operation performance. Summary of the Invention

[0007] To achieve the above object, the present invention provides an optimization method for unmanned truck scheduling in the yard of a dry bulk cargo terminal based on an alternative graph model, and the specific technical solutions are as follows:

[0008] Based on the demand and historical unloading frequency of the unloading point, 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 point, dynamically adjust the priority, and select the unloading point according to the priority;

[0009] Based on the selected unloading points, allocate the optimal driving routes for the vehicles to reach the corresponding unloading points, and calculate the initial passing time windows for each vehicle according to the distances of the routes and the optimal driving speeds; based on the calculation results, generate a set of passing time windows for multiple vehicles.

[0010] Based on the alternative graph model, model the passing process of driverless trucks, consider factors such as passing time, path conflicts, and passing order, construct a mixed-integer programming model, and calculate the optimal scheduling plan for driverless trucks.

[0011] Adopt a rolling horizon optimization strategy to dynamically adjust decisions according to real-time data during the scheduling execution process.

[0012] With the goal of minimizing the overall completion time and considering the balance of the operation times of each driverless truck, solve the mixed-integer programming model to determine the time windows and passing order of each vehicle on the passing routes.

[0013] Preferably, mark whether the unloading points are available unloading points according to the capacities of the unloading points; calculate the priorities of the unloading points according to the unloading frequencies of the unloading points; determine the set of available unloading points according to the capacity constraints and priority settings of the unloading points.

[0014] Sort the unloading points in the set of available unloading points from high to low according to the unloading point priorities, and select the unloading point with the highest priority as the target unloading point for the current unloading task.

[0015] Preferably, introduce a real-time monitoring mechanism for the status of the unloading points to dynamically track the status parameters of the unloading points, including: obtaining the real-time unloading volume of the unloading points, calculating the real-time idle capacity of the unloading points, and obtaining the real-time number of queuing vehicles at the unloading points.

[0016] When the real-time number of queuing vehicles at an unloading point exceeds the queuing vehicle number threshold, trigger the unloading point collaborative allocation mechanism, including: searching for idle unloading points in the adjacent area of the unloading point, that is, guiding the queuing vehicles at the unloading point to the idle unloading points.

[0017] Preferably, according to the selected unloading points, combine with the road network layout of the terminal to construct a road network topology graph.

[0018] Adopt the A* algorithm to calculate the shortest path of the vehicle.

[0019] According to the road network topology graph and the A* algorithm, calculate the scheduling information of vehicle-route-time.

[0020] Preferably, use the alternative graph model to model the passing process of driverless trucks.

[0021] Define the alternative graph model and decision variables, and construct a mixed-integer programming model to solve the planned scheduling of driverless trucks with the goal of minimizing the scheduling time of driverless trucks.

[0022] Preferably, divide the entire scheduling time range into consecutive scheduling time windows;

[0023] Based on the constructed alternative graph model and mixed integer programming model, solve to obtain the optimal path and the corresponding time window for each driverless truck.

[0024] Preferably, with the goal of minimizing the overall completion time and considering the balance of the operation time of each driverless truck, solve the mixed integer programming model to determine the time window and the passing order of each driverless truck on the passing path.

[0025] The driverless truck scheduling optimization system for the dry bulk terminal yard based on the alternative graph model, which is implemented based on the above-mentioned driverless truck scheduling optimization method for the dry bulk terminal yard based on the alternative graph model, includes: an unloading point selection module, a passing time calculation module, a scheduling calculation module, a rolling optimization module, and a multi-objective optimization module;

[0026] The unloading point selection module, based on the demand and historical unloading frequency of the unloading point, evaluates the current workload and usage frequency of each unloading point, determines the priority of each unloading point, introduces real-time status monitoring of the unloading point, dynamically adjusts the priority, and selects the unloading point according to the priority;

[0027] The passing time calculation module, based on the selected unloading point, assigns the optimal driving path for the vehicle to reach the corresponding unloading point, calculates the initial passing time window of each vehicle according to the distance of the path and the optimal driving speed; based on the calculation result, generates a set of passing time windows for multiple vehicles;

[0028] The scheduling calculation module, based on the alternative graph model, models the passing process of the driverless truck, considers factors such as passing time, path conflict, and passing order, constructs a mixed integer programming model, and calculates the optimal scheduling plan for the driverless truck;

[0029] The rolling optimization module adopts a rolling horizon optimization strategy and dynamically adjusts the decision according to real-time data during the scheduling execution process;

[0030] The multi-objective optimization module, with the goal of minimizing the overall completion time and considering the balance of the operation time of each driverless truck, solves the mixed integer programming model to determine the time window and the passing order of each vehicle on the passing path.

[0031] An electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory; the processor executes the above-mentioned driverless truck scheduling optimization method for the dry bulk terminal yard based on the alternative graph model by calling the computer program stored in the memory.

[0032] A computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to execute the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model.

[0033] Advantages of the present invention: By dynamically evaluating the real-time status of unloading points, determining the target unloading point by comprehensively considering capacity and queuing situation, triggering a collaborative allocation mechanism to achieve load balance, and allocating the optimal unloading point for vehicles, the resource utilization efficiency is improved.

[0034] Based on the selected unloading point, the present invention constructs a dynamic road network topology, calculates the shortest path using the A* algorithm, adjusts the path by combining the road section passing capacity and real-time congestion, and generates a set of paths and time windows for the vehicles to ensure efficient passage.

[0035] The present invention describes the vehicle passage conflicts and sequential relationships through an alternative graph model, constructs a mixed-integer programming model to minimize the overall completion time, solves the conflicts of vehicles in paths and unloading points, and optimizes the vehicle passage sequence and time allocation.

[0036] By dividing the scheduling time window, the present invention 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 scheme.

[0037] With minimizing the overall completion time as the main objective, and at the same time balancing the differences in vehicle operation times, the present invention constructs a weighted optimization objective to comprehensively consider priority and balance, and improves the fairness and overall efficiency of driverless truck scheduling. Description of the Drawings

[0038] Figure 1 It is a flowchart of the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model provided by the present invention;

[0039] Figure 2 It is a simplified traffic scenario of the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model provided by the present invention.

[0040] Figure 3 It is an alternative graph model of the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model provided by the present invention.

[0041] Figure 4 It is the solution result of the alternative graph of the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model provided by the present invention.

[0042] Figure 5 It is a structural diagram of the system for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model provided by the present invention. Detailed Embodiments

[0043] For a better understanding of the present invention, various aspects of the present invention will be described in more detail 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 do not 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.

[0044] In the drawings, for ease of illustration, the size, dimensions, and shape of the elements have been slightly adjusted. The drawings are for illustrative purposes only and are not drawn to an exact scale. As used herein, the terms "substantially", "about", and similar terms are used as terms of approximation and not as terms of degree, and are intended to account for the inherent deviations in measured or calculated values that would be recognized by a person of ordinary skill in the art. Additionally, in the present invention, the order in which the steps are described does not necessarily represent the order in which these steps occur in actual operation, unless otherwise explicitly specified or can be deduced from the context.

[0045] It should also be understood that expressions such as "comprises", "comprising", "has", "including", and / or "containing" are open-ended rather than closed-ended expressions in this specification, which means that the stated features, elements, and / or components exist, but do not exclude the existence of one or more other features, elements, components, and / or their combinations. In addition, when an expression such as "at least one of..." appears after a list of listed features, it modifies the entire list of features rather than just individual elements in the list. Further, when describing embodiments of the present invention, the use of "may" means "one or more embodiments of the present invention". And the term "exemplary" is intended to refer to an example or illustration.

[0046] Unless otherwise defined, all terms used herein (including engineering terms and scientific and technical terms) have the same meaning as commonly understood by a person of ordinary skill in the art to which the present invention pertains. It should also be understood that, unless explicitly stated in the present invention, words defined in a commonly used dictionary should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and should not be interpreted in an idealized or overly formal sense.

[0047] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may 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.

[0048] Example 1

[0049] Refer to Figure 1, which is the first embodiment of the present invention, provides an optimization method for the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model.

[0050] 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, and select the unloading points according to the priority.

[0051] Suppose there are unloading points, and the single unloading point number is , ; Define as the maximum capacity of the unloading point , as the cumulative unloading volume of the current unloading point ; When , the unloading point is marked as an available unloading point, otherwise the unloading point is marked as an unavailable unloading point.

[0052] Introduce the unloading frequency of the unloading point , which represents the average number of unloading times of the unloading point per unit time; Define the priority of the unloading point as , which is inversely proportional to , that is, the higher the priority , the lower the usage frequency of the unloading point , and it should be selected first.

[0053] According to the capacity constraint and priority setting of the unloading points, determine the set of available unloading points, screen the unloading points to be unloaded according to the capacity constraint. For any unloading point , if , then put the unloading point into the set of available unloading points, mark the unloading points exceeding the capacity as unavailable unloading points for a period of time, select all available unloading points. If all unloading points are unavailable, trigger the delay scheduling mechanism; When all available unloading points are integrated into the set of available unloading points.

[0054] Sort the unloading points in the set of available unloading points in descending order according to the unloading point priority , and select the unloading point with the highest priority as the target unloading point for the current unloading task.

[0055] Introduce a real-time monitoring mechanism for the status of the unloading point 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 , obtaining the real-time number of queuing vehicles at the unloading point .

[0056] When the real-time number of queuing vehicles at the unloading point exceeds the threshold of the number of queuing vehicles , trigger the collaborative dispatching mechanism for the unloading point, including: searching for idle unloading points in the adjacent area of the unloading point , that is , guiding the vehicles queuing at the unloading point to the unloading point , updating the real-time status parameters of the two unloading points, performing the unloading point selection step for all vehicles, and achieving load balance between unloading points. Among them, is the threshold of the idle capacity of the unloading point.

[0057] Step 1: Dynamically evaluate the real-time status and historical frequency of the unloading point, determine the target unloading point by comprehensively considering the capacity and queuing situation, trigger the collaborative dispatching mechanism to achieve load balance, allocate the optimal unloading point for the vehicle, and improve the resource utilization efficiency.

[0058] Step 2: Based on the selected unloading point, allocate the optimal driving path for the vehicle to reach the corresponding unloading point, calculate the initial passing time window for each vehicle according to the distance of the path and the optimal driving speed; based on the calculation results, generate a set of passing time windows for multiple vehicles, and the set contains the passing path of each vehicle and its corresponding time window.

[0059] According to the selected unloading point , combined with the road network layout of the wharf, construct a road network topology graph , where is the set of road network nodes, is the set of road segments.

[0060] For any road segment , define its length as , and the traffic capacity as ; for each vehicle to be scheduled , obtain the current position coordinates of the vehicle , and map the coordinates to the nearest road network node .

[0061] Adopt the A* algorithm, with Starting from as the end point, calculate the shortest path of the vehicle to obtain the number sequence of each road section on the path , where is the number of road sections on the path , and represents the number of the last road section on the path .

[0062] Combined with the lengths of each road section and the optimal driving speed of the vehicle , calculate the driving time of the vehicle passing through each road section to obtain the total passing time of the vehicle on the path . .

[0063] 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 , where is the safety distance between vehicles, and is the floor function.

[0064] Combined with the road section capacity limit, adjust the path of the vehicle in real time; if there is a road section in the path where the number of passing vehicles will exceed , then at start a new node to re-plan an avoidance path for the vehicle to bypass the congested road section and update the path information and passing time of the vehicle .

[0065] Based on the above calculations, calculate the path set of all vehicles to be scheduled and the corresponding passing time set to form the scheduling information of vehicle-path-time, where represents the th path, and represents the passing time of the

[0066] Step 2: Based on the selected unloading points, construct a dynamic road network topology, use the A* algorithm to calculate the shortest path, and adjust the path by combining the road section capacity and real-time congestion, generating a set of paths and time windows for the vehicles to ensure efficient passage.

[0067] Step 3: Based on the alternative graph model, model the passage process of driverless trucks, consider factors such as passage time, path conflicts, and passage order, construct a mixed integer programming model, and calculate the optimal scheduling plan for driverless trucks.

[0068] Use the alternative graph model to model the passage process of driverless trucks; define the alternative graph , where is the set of operations, is the set of fixed arcs, is the set of alternative arcs;

[0069] The set of operations represents all possible driving states of driverless trucks during the scheduling process, and each operation corresponds to a road section or an unloading point; among them, the starting operation represents the initial state of all vehicles, and the ending operation represents the final state of all vehicles after completing the scheduling task; the elements in the set are non-negative integers representing the operation numbers.

[0070] The set of fixed arcs represents the driving path of driverless trucks in the road network. The arc represents that the vehicle travels from the location where the operation is located to the location where the operation is located; the weight of the arc represents the driving time required for the vehicle to travel from the operation to the operation , and this time can be pre-calculated according to the road section length and vehicle speed.

[0071] The set of alternative arcs is used to describe the passage order relationship of different vehicles on the same road section or unloading point; the alternative arc indicates that there is a conflict in the passage order of two vehicles on the road section or unloading point, and the order needs to be determined; the weight of the arc represents the safe driving interval time between two vehicles, that is, the following vehicle needs to wait for the preceding vehicle to pass before starting to pass, and this time can be preset according to the vehicle speed, acceleration, and safety distance requirements.

[0072] Generate the selection set according to the set of alternative arcs , used to determine the actual passing order for each pair of vehicles with conflicting passing orders; for each pair of alternative arcs , the set contains two elements and , representing two possible passing orders respectively.

[0073] When solving the alternative graph model, for each pair of alternative arcs, only one element can be selected from the corresponding selection set, that is, the passing order of the two vehicles is determined.

[0074] Define decision variables: continuous variable represents the start time of vehicle when performing operation ; binary variable , when it means that vehicle first performs operation and then performs operation , when it means that vehicle first performs operation and then performs operation .

[0075] Construct a mixed-integer programming model with the objective function of minimizing the overall completion time, and the constraint conditions include:

[0076] ;

[0077] Among them, is a positive natural number, is the set of all unmanned trucks participating in the scheduling; respectively represent the start time of vehicle in operation , operation , vehicle in operation , operation and vehicle in operation , represents the successor operation of operation in the fixed arc set ; represents the best passing time of the vehicle from operation to operation , and n is the end operation.

[0078] The first constraint condition in the mixed-integer programming model is used to ensure that vehicles drive in the planned path order; is the safety interval time between vehicle and vehicle ; the second and third constraint conditions are used to ensure safe driving between vehicles. When When it represents the vehicle preceding the vehicle passes through the conflict section or unloading point first, otherwise the vehicle passes through first.

[0079] Solve the above mixed-integer programming model to obtain the optimal values of the decision variables and , then the optimal passing time and passing order of each driverless truck on each section can be determined, and a complete scheduling plan can be obtained.

[0080] To cope with the uncertain factors and random events in the actual scheduling process, a rolling horizon optimization strategy is adopted to dynamically adjust the scheduling plan; the entire scheduling horizon is divided into several consecutive scheduling periods. At the beginning of each scheduling period, according to the current vehicle state information and environmental parameters, the relevant parameters in the surrogate graph model are updated, and the mixed-integer programming model is solved again to obtain a new scheduling decision, realizing the rolling optimization of the scheduling plan.

[0081] Step 3 describes the vehicle passing conflict and sequence relationship through the surrogate graph model, constructs a mixed-integer programming model to minimize the overall completion time, solves the conflict problem of vehicles on the path and unloading point, and optimizes the vehicle passing order and time allocation.

[0082] Step 4: Adopt a rolling horizon optimization strategy to dynamically adjust the decision according to the real-time data during the scheduling execution.

[0083] Divide the entire scheduling time range into consecutive scheduling time windows, each window has a length of , represents the total length of the entire scheduling time range, and the th window is denoted as , where , is the start time of the th scheduling time window.

[0084] Define as the set of vehicles with unfinished tasks at the end of the previous scheduling period, as the set of vehicles that newly arrive at the current scheduling period and wait for task assignment, and the total vehicle set within the current scheduling period is .

[0085] For each vehicle in , according to its position coordinates at the moment of , update its remaining path and the estimated arrival time 。

[0086] Based on the constructed alternative graph model, using the vehicle in as the input, solve the mixed-integer programming model to obtain the optimal path of each vehicle within the scheduling period and the corresponding time window . , is the start time of the scheduling time window of vehicle , and is the end time of the scheduling time window of vehicle .

[0087] According to the optimization results, update the remaining path and estimated arrival time of each vehicle in , and move the vehicles that have been assigned tasks in to , waiting for continued scheduling in the next scheduling period;

[0088] Let , repeat vehicle scheduling until all scheduling periods end, that is .

[0089] Step 4 updates the vehicle status and environmental parameters in real time by dividing the scheduling time window, 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.

[0090] Step 5: Taking the minimization of the overall completion time as the optimization goal, and considering the balance of the operation time of each driverless truck at the same time, solve the mixed-integer programming model to determine the time window and passing order of each vehicle on the passing path.

[0091] Define as the maximum completion time of the overall task, where is the total passing time of vehicle , and is the set of all driverless trucks participating in the scheduling; the optimization goal is to minimize , that is ; to balance the operation time of each vehicle, introduce the vehicle operation time deviation , where is the average value of the operation time of all vehicles.

[0092] Define the total deviation as the secondary optimization goal, that is ; introduce the weight coefficient , construct the weighted optimization goal , and by adjusting the value of , control the relative importance of the maximum completion time and the balance of the operation time.

[0093] To ensure that the vehicle travels evenly to the unloading point, vehicle priority and unloading point priority are introduced; the priority of the dispatched vehicle is set higher than that of the undispatched vehicle to avoid the deadlock problem caused by the vehicle departing in advance; at the same time, the unloading point priority is added to prevent the over-concentration of the use of the unloading point and ensure the balance of scheduling; the weighted product method is used to adjust the objective function, considering the influence of different priority factors on the scheduling result, and the objective function is set as follows:

[0094] ;

[0095] Among them, represents the completion time of vehicle , and respectively represent the priorities of vehicle and the corresponding unloading point .

[0096] Step 5 takes minimizing the overall completion time as the main goal, while balancing the difference in vehicle operation time, constructs a weighted optimization goal to comprehensively consider priority and balance, and improves the fairness and overall efficiency of the unmanned truck scheduling.

[0097] Embodiment 2

[0098] Referring to Figures 2 to 4 , the second embodiment of the present invention provides a simplified traffic scenario of the unmanned truck scheduling optimization method for the dry bulk terminal yard based on the alternative graph model.

[0099] Figure 2 shows a simplified traffic scenario in the yard, which includes three vehicles: two vehicles A and B perform inbound transportation operations, and one vehicle C performs outbound transportation operations; in this scenario, the tasks of vehicle A and vehicle B are to drive in from outside the yard and enter the designated unloading area, while vehicle C drives out from inside the yard after completing the unloading operation.

[0100] Figure 2 The lines between the two blue dots in

[0101] represent path samples, and each path has a corresponding number.

[0102] The reference path samples of 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 a path point that repeats 2620.

[0103] The driving path of vehicle B is: 2636, 3150, 2623, 2620, (2620), 2971, 2611, 2580, 2582, 2581, 2602, 2601, 2596, 2595, 2594.

[0104] The driving path of vehicle C is: 2590, 2591, 2598, 2599, 2604, 2578, 2606, 2607, 2610, 2621, 2636.

[0105] Among them, the path samples: (3105, 2621), (2623, 2621), (2623, 2545), (2623, 2971), (2545, 2971), (2621, 2545), (2621, 2971) are associated paths.

[0106] Figure 3 An alternative graph model of the above example is shown. In Figure 3 each node represents the running state of the vehicle. For example, the node (1, 2636) represents the running of vehicle A on path 2636.

[0107] The driving paths of the vehicles are represented by each node in turn. The solid arrows represent fixed arcs, and the dashed and colored arrows represent alternative arcs.

[0108] There are a total of 8 conflicts between vehicle A and vehicle B, which are represented by alternative arcs; there are 4 conflicts between vehicle A and vehicle C and between vehicle B and vehicle C respectively.

[0109] For the same - direction path conflicts between A and B, 5 alternative arcs are constructed: ((2, 3150; 1, 2636), (1, 3150; 2, 2636);

[0110] ((2, 2623; 1, 3150), (1, 2623; 2, 3150));

[0111] ((2, 2620; 1, 2623), (1, 2620; 2, 2623));

[0112] ((2, 26201; 1, 2620), (1, 26201; 2, 2620));

[0113] ((2, 2971; 1, 26201), (1, 2545; 2, 26201)).

[0114] In addition, there is also a cross - path conflict between A and B, and 3 alternative arcs are constructed:

[0115] ((2, 2611; 1, 2545), (1, 2609; 2, 2971));

[0116] ((2, 2611; 1, 2623), (1, 2620; 2, 2971));

[0117] ((2, 2620; 1, 2609), (1, 2609; 2, 2623)).

[0118] For the reverse - path conflict between A and C, 1 alternative arc is constructed: ((3, out; 1, 2636)(1, 3150; 3, 2636)), where (3, out:1, 2636) means starting from output terminal 1 of node 3 and connecting to node 2636.

[0119] Similarly, there is also a cross - path conflict between A and C, and 3 alternative arcs are constructed: ((3, 2636; 1, 3150)(1, 2623; 3, 2621)), ((3, 2636; 1, 2623)(1, 2620; 3, 2621)), ((3, 2636; 1, 2545)(1, 2609; 3, 2621)).

[0120] The construction logic of the 4 alternative arcs between B and C is similar to the above.

[0121] Figure 4 The solution to the scheduling problem is described. From Figure 4 it can be seen the running order of each vehicle. Vehicle A enters the yard prior to vehicle B, vehicle C passes through the intersection area after vehicle A, and vehicle B starts its yard - entry operation after vehicle C leaves the yard; this order ensures that the path conflicts between vehicles are effectively avoided.

[0122] Embodiment 3

[0123] Referring to Figure 5 , the third embodiment of the present invention provides an unmanned truck scheduling optimization system for a dry - bulk terminal yard based on an alternative graph model;

[0124] The system includes: a unloading - point selection module, a travel - time calculation module, a scheduling calculation module, a rolling - optimization module, and a multi - objective optimization module.

[0125] The unloading point selection module evaluates the current workload and usage frequency of each unloading point based on the demand and historical unloading frequency of the unloading point, determines the priority of each unloading point, introduces real-time status monitoring of the unloading point, dynamically adjusts the priority, and selects the unloading point according to the priority.

[0126] The travel time calculation module allocates the optimal driving route for the vehicle to reach the corresponding unloading point based on the selected unloading point, calculates the initial travel time window of each vehicle according to the distance of the route and the optimal driving speed, and generates a set of multi-vehicle travel time windows based on the calculation results.

[0127] The scheduling calculation module models the passing process of the driverless truck based on the alternative graph model, constructs a mixed integer programming model considering factors such as travel time, path conflict, and passing order, and calculates the optimal scheduling plan for the driverless truck.

[0128] 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.

[0129] The multi-objective optimization module takes minimizing the overall completion time as the optimization goal, and at the same time considers the balance of the operation time of each driverless truck, solves the mixed integer programming model, and determines the time window and passing order of each vehicle on the passing route.

[0130] Embodiment 4

[0131] The present invention also provides an electronic device. The electronic device may include one or more processors and one or more memories. Among them, computer-readable code is stored in the memory, and when the computer-readable code is run by one or more processors, it can execute the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on the alternative graph model as described above.

[0132] The method or system according to the embodiment of the present invention can also be implemented by means of the architecture of the electronic device of the present invention.

[0133] The present invention also discloses a computer-readable storage medium.

[0134] Computer-readable instructions are stored on the computer-readable storage medium.

[0135] When the computer-readable instructions are run by the processor, the method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on the alternative graph model according to the embodiment of the present invention as described with reference to the above drawings can be executed.

[0136] The above order of the steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the above specific description order unless otherwise specifically stated.

[0137] In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, and these programs include 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.

[0138] In addition, 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 repetition.

[0139] As described above, the specific embodiments further elaborate on the object, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for the scheduling of driverless trucks in the yard of a dry bulk cargo terminal based on an alternative graph model, characterized in that, Including: Based on the demand at the offloading point and the historical offloading frequency, evaluate the current workload and usage frequency of each offloading point, determine the priority of each offloading point, introduce real-time status monitoring of the offloading point, dynamically adjust the priority, and select the offloading point according to the priority; Based on the selected offloading point, allocate the optimal driving route for the vehicle to reach the corresponding offloading point, calculate the initial passing time window for each vehicle according to the distance of the route and the optimal driving speed; based on the calculation results, generate a set of passing time windows for multiple vehicles; Based on the alternative graph model, model the passing process of the driverless truck, consider factors such as passing time, path conflict, and passing order, construct a mixed-integer programming model, and calculate the optimal scheduling plan for the driverless truck; Adopt a rolling time-domain optimization strategy to dynamically adjust the decision according to real-time data during the scheduling execution; With the goal of minimizing the overall completion time and considering the balance of the operation time of each driverless truck, solve the mixed-integer programming model to determine the time window and passing order of each vehicle on the passing path.

2. The method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on an alternative graph model according to claim 1, characterized in that, According to the offloading point capacity, mark whether the offloading point is an available offloading point; calculate the offloading point priority according to the offloading frequency of the offloading point; determine the set of available offloading points according to the offloading point capacity constraint and priority setting; Sort the offloading points in the set of available offloading points from high to low according to the offloading point priority, and select the offloading point with the highest priority as the target offloading point for the current offloading task.

3. The optimization method for the unmanned truck scheduling in the dry bulk terminal yard based on the alternative graph model according to claim 2, wherein Introduce a real-time status monitoring mechanism for the offloading point to dynamically track the status parameters of the offloading point, including: obtaining the real-time offloading volume of the offloading point, calculating the real-time idle capacity of the offloading point, and obtaining the real-time number of queuing vehicles at the offloading point; When the real-time number of queuing vehicles at an offloading point exceeds the queuing vehicle number threshold, trigger the offloading point collaborative allocation mechanism, including: searching for idle offloading points in the adjacent area of the offloading point, and guiding the queuing vehicles at the offloading point to the idle offloading point.

4. The method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on the alternative graph model according to claim 3, characterized in that According to the selected offloading point, combined with the road network layout of the wharf, construct a road network topology graph; Adopt the A* algorithm to calculate the shortest path of the vehicle; According to the road network topology graph and the A* algorithm, calculate the scheduling information of vehicle-path-time.

5. The method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on the alternative graph model according to claim 4, wherein Use the alternative graph model to model the passing process of the driverless truck; Define the alternative graph model and decision variables, and construct a mixed-integer programming model, with the goal of minimizing the scheduling time of the driverless truck, and solve the planning and scheduling of the driverless truck.

6. The method for optimizing the scheduling of driverless trucks in the dry bulk terminal yard based on the alternative graph model according to claim 5, characterized in that Divide the entire scheduling time range into consecutive scheduling time windows; Based on the constructed alternative graph model and mixed-integer programming model, solve to obtain the optimal path and the corresponding time window of each driverless truck.

7. The optimization method for the unmanned truck scheduling in the dry bulk terminal yard based on the alternative graph model according to claim 6, characterized in that, With the goal of minimizing the overall completion time and considering the balance of the operation time of each driverless truck, solve the mixed-integer programming model to determine the time window and passing order of each driverless truck on the passing path.

8. An unmanned truck scheduling optimization system for the yard of a dry bulk cargo terminal based on an alternative graph model, which is implemented based on the unmanned truck scheduling optimization method for the yard of a dry bulk cargo terminal based on an alternative graph model according to any one of claims 1 to 7, characterized in that Including: An offloading point selection module, a passing time calculation module, a scheduling calculation module, a rolling optimization module, and a multi-objective optimization module; The offloading point selection module, based on the demand at the offloading point and the historical offloading frequency, evaluates the current workload and usage frequency of each offloading point, determines the priority of each offloading point, introduces real-time status monitoring of the offloading point, dynamically adjusts the priority, and selects the offloading point according to the priority; The travel time calculation module allocates the optimal driving path for the vehicle to reach the corresponding unloading point based on the selected unloading point, and calculates the initial travel time window for each vehicle according to the distance of the path and the optimal driving speed; based on the calculation results, a set of multi-vehicle travel time windows is generated; The scheduling calculation module models the travel process of the driverless truck based on the alternative graph model, constructs a mixed integer programming model considering factors such as travel time, path conflict and travel order, and calculates the optimal scheduling plan for the driverless truck; The rolling optimization module adopts a rolling horizon optimization strategy to dynamically adjust decisions according to real-time data during the scheduling execution; The multi-objective optimization module takes minimizing the overall completion time as the optimization goal, and at the same time considers the balance of the operation time of each driverless truck, solves the mixed integer programming model, and determines the time window and travel order of each vehicle on the travel path.

9. An electronic device, characterized in that, Comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the driverless truck scheduling optimization method based on the alternative graph model according to 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: Stored with instructions, when the instructions run on a computer, the computer is caused to execute the driverless truck scheduling optimization method based on the alternative graph model according to any one of claims 1 to 7.

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