Transportation resource scheduling method and device

Abstract

The present disclosure provides a method and device for scheduling transportation resources. The method comprises: obtaining scheduling data; in the case that the unassigned staff points are not empty, starting a vehicle and setting the starting point of the path as the departure point, and selecting a staff point with the lowest travel cost from the departure point to the staff point to join the path of the vehicle; based on the end point of the current path of the vehicle, selecting a node that meets a second preset condition from the remaining unassigned staff points or target locations to join the path; the second preset condition includes that the total travel cost increment after adding is the smallest and meets the constraint condition, and the constraint condition includes at least one of the vehicle capacity constraint, the time of arriving at the target location within the corresponding soft time window, and the path uniqueness constraint; in the case that there is no node meeting the constraint condition, returning the path from the end point to the departure point, and calculating the cost of the vehicle; when all staff points are assigned, outputting the scheduling information of all vehicles. The efficiency of scheduling transportation resources can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method and apparatus for scheduling transportation resources. Background Technology

[0002] In related technologies, underground transportation capacity scheduling is crucial for the efficient operation of underground work. Currently, underground transportation capacity scheduling typically revolves around the Vehicle Routing with Time Windows (VRPTW) problem, aiming to minimize transportation costs while considering time window constraints and service quality. For example, some models plan vehicle routes from the origin to multiple nodes by setting vehicle capacity limits and time window ranges (including hard and soft time windows), ensuring vehicles complete their transportation tasks within a specified time and with the lowest total travel cost. While this approach achieves transportation capacity scheduling, the scheduling logic tends to prioritize serving nearby employee locations, leading to the neglect of distant employee locations and resulting in transportation omissions. Summary of the Invention

[0003] This disclosure provides a method and apparatus for scheduling transportation resources.

[0004] According to a first aspect of this disclosure, a method for scheduling transportation capacity resources is provided, comprising:

[0005] Obtain scheduling data; the scheduling data includes multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points; If the unassigned employee point is not empty, the vehicle is started with the departure point as the starting point of the route, and an employee point that meets the first preset condition is selected from the employee points to join the route of the vehicle; wherein, the first preset condition includes the lowest travel cost from the departure point to the employee point. Based on the destination of the vehicle's current route, select a node that meets the second preset condition from the remaining unassigned employee points or the target location and add it to the route; the second preset condition includes minimizing the increase in total travel cost after addition and satisfying constraints, the constraints include at least one of the following: vehicle capacity constraint, arrival time at the target location within the corresponding soft time window, and path uniqueness constraint. If no node satisfies the constraints, the path returns from the destination to the departure point, and the cost of the vehicle is calculated. Once all employee points have been assigned, output the scheduling information for all vehicles; wherein the scheduling information includes at least one of the following: the vehicle's route employee point sequence and the cost.

[0006] According to a second aspect of this disclosure, a capacity resource scheduling device is provided, comprising: The data acquisition module is used to acquire scheduling data; the scheduling data includes multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points. The node addition module is used to start a vehicle with the departure point as the starting point when the unassigned employee point is not empty, and to select an employee point that meets a first preset condition to add the vehicle's route; wherein, the first preset condition includes the lowest travel cost from the departure point to the employee point. The node allocation module is used to select nodes that meet a second preset condition from the remaining unallocated employee points or the target location based on the destination of the vehicle's current path and add them to the path. The second preset condition includes minimizing the increase in total travel cost after adding the node and satisfying constraints. The constraints include at least one of the following: vehicle capacity constraint, arrival time at the target location within a corresponding soft time window, and path uniqueness constraint. The cost calculation module is used to return the route from the destination to the departure point and calculate the cost of the vehicle if no node satisfies the constraints. The scheduling module is used to output the scheduling information of all vehicles after all employee points have been allocated; wherein the scheduling information includes at least one of the vehicle's route employee point sequence and cost.

[0007] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0008] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0009] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0010] In this embodiment, scheduling data is acquired, including multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points. If the unassigned employee points are not empty, a vehicle is started with the departure point as the starting point, and employee points meeting a first preset condition are selected from the employee points to join the vehicle's path. The first preset condition includes minimizing the travel cost from the departure point to the employee point. Based on the vehicle's current path endpoint, nodes meeting a second preset condition are selected from the remaining unassigned employee points or the destination location to join the path. The second preset condition includes minimizing the increase in total travel cost after joining and satisfying constraints, including at least one of vehicle capacity constraints, arrival time at the destination location being within a corresponding soft time window, and path uniqueness constraints. If no node satisfies the constraints, the path returns from the destination to the departure point, and the vehicle's cost is calculated. These steps are repeated until all employee points are assigned, and scheduling information for all vehicles is output. The scheduling information includes at least one of the vehicle's path employee point sequence and cost. In this way, by starting vehicles one by one when unassigned employee points are not empty, the first employee point is selected based on the condition of the lowest travel cost from the departure point to the employee point. Then, based on the current path endpoint, the path is expanded according to the principle of minimizing the increase in total travel cost after addition and satisfying the constraints. When no node satisfies the constraints, the path is returned to the departure point and the cost is calculated. The above process is repeated until all employee points are assigned and the scheduling information is output. Under the conditions of satisfying vehicle capacity constraints, arrival time at the destination within the corresponding soft time window, and path uniqueness constraints, all employee points can be effectively allocated. The scheduling information includes the sequence of employee points along the vehicle path and the cost. This not only improves the efficiency of transportation resource scheduling, but also avoids neglecting distant employee points and prevents transportation omissions.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is a schematic diagram of an underground transportation resource scheduling scenario provided by an embodiment of the present disclosure; Figure 2 A flowchart illustrating a transportation capacity scheduling method provided in this embodiment of the disclosure; Figure 3 A schematic diagram illustrating the functional relationship between employee satisfaction and hard time window range, provided for embodiments of this disclosure; Figure 4 This is a schematic diagram of a transportation resource scheduling device provided in an embodiment of the present disclosure. Detailed Implementation

[0013] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0014] In related technologies, the scheduling of underground transportation resources is a crucial link in ensuring the efficient and orderly conduct of underground operations. Its core lies in the rational planning of vehicle routes and departure schedules to meet the needs of employees arriving at their work locations within a specified time, while simultaneously controlling transportation costs. Figure 1 The illustration depicts a scenario for scheduling underground transportation resources. In underground transportation scheduling, the distance difference between employee locations and departure points is often not fully considered. Employee locations farther from the departure point frequently become full before vehicles reach nearby locations, preventing employees from being transported. This fails to meet hard time window constraints and significantly reduces employee satisfaction due to lack of accessibility, ultimately affecting the fairness and effectiveness of scheduling. Therefore, it is necessary to rationally plan vehicle routes and departure schedules to ensure employees arrive on time and control costs. For example, related technologies typically revolve around the Vehicle Routing Problem with Time Windows (VRPTW). This type of solution aims to minimize transportation costs while considering time window constraints and service quality. For instance, some models plan vehicle routes from the origin to multiple nodes by setting vehicle capacity limits and time window ranges (including hard and soft time windows), ensuring vehicles complete transportation tasks within a specified time and with the lowest total travel cost. At the algorithmic level, heuristic methods such as greedy algorithms are often used, progressively selecting nodes with the smallest cost increments to expand the path, prioritizing the satisfaction of constraints to construct a feasible scheduling solution. While these measures take time window constraints into account, they do not include situations where transportation is unreachable due to differences in distance between employee locations and departure points in the quantitative system, nor do they design strict constraints on the correspondence between "employee locations and work locations." Therefore, they are insufficient in balancing the needs of employee locations at different distances and improving the overall scheduling effectiveness.

[0015] Based on this, the present disclosure provides a method for scheduling transportation resources. This method involves starting vehicles one by one when unassigned employee points are not empty. First, the first employee point is selected based on the condition of having the lowest travel cost from the departure point to the employee point. Then, based on the current path endpoint, the path is expanded according to the principle of minimizing the increase in total travel cost after adding the employee point while satisfying constraints. If no node satisfies the constraints, the path is returned to the departure point and the cost is calculated. This process is repeated until all employee points are assigned and scheduling information is output. This method can effectively allocate all employee points while satisfying vehicle capacity constraints, arrival times within the corresponding soft time window, and path uniqueness constraints. It outputs scheduling information containing the vehicle path employee point sequence and costs, thereby not only improving the efficiency of transportation resource scheduling but also preventing the neglect of distant employee points and avoiding transportation omissions.

[0016] The following description, with reference to the accompanying drawings, describes a method and apparatus for scheduling transportation resources according to embodiments of the present disclosure.

[0017] Figure 2 This is a flowchart illustrating a transportation capacity scheduling method provided in an embodiment of this disclosure. Figure 2 As shown, the method includes the following steps: S201, Obtain scheduling data.

[0018] The scheduling data includes multiple employee locations, departure points, destination locations, vehicle capacity, and unassigned employee locations.

[0019] In this embodiment of the disclosure, when performing capacity scheduling, scheduling data can be acquired first to provide the necessary data foundation for subsequent route planning and vehicle allocation. For example, the scheduling data may specifically include: multiple employee locations (the work locations of employees who need to be picked up and dropped off), departure points (fixed starting points for vehicle departure), destination locations (the destinations employees need to reach, which usually correspond to employee locations), vehicle capacity (the maximum number of people a single vehicle can carry), and unassigned employee locations (initially the set of all unassigned employee locations). This scheduling data can constitute the basic elements of capacity scheduling to clarify the core questions of "who needs to be transported, where they depart from, where they go, and what transportation method is used." It is understood that the acquisition methods may include reading from a database, receiving user input, or calling external system interfaces.

[0020] S202, if the unassigned employee points are not empty, start the vehicle and the starting point of the route is the departure point, and select an employee point that meets the first preset condition to join the vehicle's route.

[0021] The first preset condition includes minimizing the travel cost from the departure point to the employee's location.

[0022] In this embodiment of the disclosure, a single vehicle's route can be initialized. For example, it can be first determined whether the unassigned employee points are not empty. If the unassigned employee points are not empty, a vehicle can be started, and the starting point of the vehicle's route can be set as the departure point. Then, among the unassigned employee points, employee points that meet a first preset condition can be selected to join the vehicle's route. For example, if the employee point meets the condition of having the lowest travel cost from the departure point to the employee point, the travel cost from each unassigned employee point to the departure point can be calculated, and the employee point with the lowest travel cost from the departure point to the employee point can be selected as the first employee point to join the vehicle's route.

[0023] S203, based on the destination of the vehicle's current path, select a node that meets the second preset condition from the remaining unassigned employee points or target locations to add to the path.

[0024] The second preset condition includes minimizing the increase in total driving cost after addition and satisfying the constraints, which include at least one of the following: vehicle capacity constraint, arrival time at the target location within the corresponding soft time window, and path uniqueness constraint.

[0025] In this embodiment of the disclosure, the path can be further expanded based on the vehicle's existing path. For example, the current path's endpoint can be used as the starting point, and nodes satisfying a second preset condition can be selected from the remaining unassigned employee points or target locations to add to the vehicle's path. The second preset condition may include minimizing the increase in total travel cost after addition and satisfying constraints. These constraints may include at least one of the following: vehicle capacity constraint, arrival time at the target location within a corresponding soft time window, and path uniqueness constraint. As an example, the vehicle capacity constraint could be: the sum of the number of employees already added to the vehicle's path is calculated as the current passenger capacity; before each new node is selected, it is determined whether the cumulative passenger capacity after adding the employee points corresponding to the new node exceeds the vehicle capacity; if it does, adding the new node to the path is prohibited. The arrival time at the target location within the corresponding soft time window can be calculated by summing the vehicle's departure time and the travel time between adjacent nodes along the route, and then determining whether the arrival time falls within the soft time window corresponding to the target location. If the vehicle's current destination is an employee point, it is also necessary to determine whether the estimated arrival time from that employee point to its corresponding target location satisfies the soft time window constraint. The path uniqueness constraint can be: the same employee point or target location can only be assigned to one vehicle path, and nodes already assigned to other vehicle paths cannot be added to the current path again.

[0026] S204: If no node satisfies the constraints, return the path from the destination to the departure point and calculate the vehicle's cost.

[0027] In this embodiment of the disclosure, the current vehicle's route extension can be terminated and cost settlement can be performed. For example, if there are no nodes satisfying the constraints among the remaining unassigned employee points or the target location, the route can be returned from the current endpoint to the departure point, completing the vehicle's closed route. Then, the vehicle's cost can be calculated, including: determining the vehicle's activation cost based on its activation status; determining the vehicle's travel cost based on the travel distance between adjacent nodes in the vehicle's route and the travel cost per unit distance; and determining the vehicle's total cost based on the activation cost and the travel cost.

[0028] S205, once all employee points have been assigned, output the dispatch information for all vehicles.

[0029] The scheduling information includes at least one of the following: the vehicle's route, employee point sequence, and cost.

[0030] In this embodiment, the iterative execution and result output of the overall scheduling process can be controlled. For example, steps S202 to S304 can be repeated until all employee points are assigned. As an example, after each vehicle completes its route construction and returns to its departure point, if there are still unassigned employee points, the next vehicle can be started to continue executing steps S202 to S304. Once all employee points are assigned, the scheduling information for all vehicles is output. This scheduling information may include at least one of the vehicle's route employee point sequence and cost, and may also include the departure time of each vehicle, total travel distance, the vehicle number to which each employee point is assigned, and their order in the route.

[0031] In this embodiment, scheduling data is acquired, including multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points. If the unassigned employee points are not empty, a vehicle is started with the departure point as the starting point, and employee points meeting a first preset condition are selected from the employee points to join the vehicle's path. The first preset condition includes minimizing the travel cost from the departure point to the employee point. Based on the vehicle's current path endpoint, nodes meeting a second preset condition are selected from the remaining unassigned employee points or the destination location to join the path. The second preset condition includes minimizing the increase in total travel cost after joining and satisfying constraints, including at least one of vehicle capacity constraints, arrival time at the destination location being within a corresponding soft time window, and path uniqueness constraints. If no node satisfies the constraints, the path returns from the destination to the departure point, and the vehicle's cost is calculated. These steps are repeated until all employee points are assigned, and scheduling information for all vehicles is output. The scheduling information includes at least one of the vehicle's path employee point sequence and cost. In this way, by starting vehicles one by one when unassigned employee points are not empty, the first employee point is selected based on the condition of the lowest travel cost from the departure point to the employee point. Then, based on the current path endpoint, the path is expanded according to the principle of minimizing the increase in total travel cost after addition and satisfying the constraints. When no node satisfies the constraints, the path is returned to the departure point and the cost is calculated. The above process is repeated until all employee points are assigned and the scheduling information is output. Under the conditions of satisfying vehicle capacity constraints, arrival time at the destination within the corresponding soft time window, and path uniqueness constraints, all employee points can be effectively allocated. The scheduling information includes the sequence of employee points along the vehicle path and the cost. This not only improves the efficiency of transportation resource scheduling, but also avoids neglecting distant employee points and prevents transportation omissions.

[0032] In some possible implementations, prior to obtaining the scheduling data, the following steps are also included: Based on the hard time window and ideal time period of the target location corresponding to the employee point, the soft time window of the employee point under different satisfaction levels is determined.

[0033] The hard time window represents the time boundary that employees must reach or leave the target location. Employee satisfaction is zero if the hard time window boundary is exceeded. The soft time window is centered on the ideal time period. Different satisfaction levels correspond to different upper and lower limits of the soft time window. The higher the satisfaction level, the closer the soft time window is to the ideal time period.

[0034] In this embodiment of the disclosure, preprocessing can be performed before acquiring scheduling data to establish a time constraint benchmark for employee locations. For example, for each employee location, a soft time window can be determined at different satisfaction levels based on the hard time window and ideal time period of its corresponding target location. The hard time window represents the time boundary at which the employee must arrive at or leave the target location; employee satisfaction is zero if the employee exceeds this hard time window boundary. The ideal time period is the optimal time interval for the employee to arrive at or leave the target location. The soft time window is centered on this ideal time period, with different upper and lower limits corresponding to different satisfaction levels; the higher the satisfaction level, the closer the soft time window is to the ideal time period. This step provides a basis for subsequent judgment on whether the arrival time at the target location meets the soft time window constraint, enabling the scheduling method to flexibly control the strictness of time constraints based on different satisfaction requirements.

[0035] In some possible implementations, calculating the cost of the vehicle includes: Determine the vehicle startup cost based on the availability of all vehicles. The vehicle's travel cost is determined based on the travel distance between adjacent nodes in the vehicle's path and the travel cost per unit distance. The cost of the vehicle is determined based on the start-up cost and the driving cost.

[0036] In this embodiment of the disclosure, when calculating the vehicle cost, the vehicle activation cost can be determined based on the vehicle's activation status. That is, once a vehicle is activated to perform a scheduling task, a fixed activation cost is incurred. This cost is unrelated to whether the vehicle actually carries passengers or the distance traveled; it only depends on whether the vehicle is put into scheduling use. The vehicle's operating cost can be determined based on the distance traveled between adjacent nodes in the vehicle's path and the operating cost per unit distance. For example, based on the adjacent nodes sequentially passed through in the vehicle's path (including departure points, employee points, target locations, and the return departure point), the distance between each pair of adjacent nodes can be calculated. The distances of each segment are added together to obtain the total distance traveled. Then, the total distance traveled is multiplied by a preset operating cost per unit distance to obtain the vehicle's operating cost. Afterward, the vehicle cost can be determined based on the activation cost and the operating cost. That is, the determined activation cost and operating cost are added together, and the sum is the vehicle's total cost, used for cost items in subsequent output scheduling information and cost accounting of the overall scheduling plan.

[0037] In some possible implementations, the constraints also include: If the vehicle's path includes employee points, then the path must include the target location corresponding to the employee point, and the target location must be located after the employee point in the path. If the vehicle's route does not include employee points, then the route must not include the target location corresponding to the employee points.

[0038] In this embodiment of the disclosure, the pairing relationship between employee points and their corresponding target locations in the vehicle path can be defined as a supplement to the constraints. For example, the constraints may further include: if the vehicle path contains an employee point, then the path must contain the target location corresponding to that employee point, and the target location must be located after the employee point in the path. That is, when an employee point is assigned to the current vehicle path, its corresponding target location must subsequently be included in the same path, and the access order of the target location must not be earlier than that of the employee point, to ensure that employees are picked up before being transported to their destination. If the vehicle path does not contain an employee point, then the path must not contain the target location corresponding to that employee point. That is, when an employee point is not assigned to the current vehicle path, its corresponding target location must not be added to the path alone, to prevent the vehicle from arriving at the target location empty or having no employees to deliver to. In this way, it can be further ensured that employee points and target locations appear in pairs in a reasonable order in the vehicle path, avoiding path logic errors.

[0039] In some possible implementations, selecting employee points that meet a first preset condition from among the employee points to join the vehicle's path includes: Calculate the travel cost from each unassigned employee point to the departure point; Select the employee point with the lowest travel cost from the departure point to the employee point as the employee point to be added to the route.

[0040] In this embodiment of the disclosure, when selecting an employee point that meets the first preset condition to add to the vehicle route from unassigned employee points, the travel cost from each unassigned employee point to the departure point can be calculated first. That is, for each employee point in the current set of unassigned employee points, the travel cost from the departure point to that employee point is calculated separately. This travel cost can be a quantifiable indicator such as distance, time, or comprehensive cost. Then, the employee point with the lowest travel cost from the departure point to the employee point can be selected as the employee point to be added to the route. For example, among the travel costs from each unassigned employee point to the departure point calculated above, the employee point with the lowest travel cost can be selected and added to the current vehicle route as the first employee point, and then removed from the set of unassigned employee points.

[0041] In some possible implementations, the method further includes: Obtain the preset satisfaction level; Based on the preset satisfaction level, determine the soft time window constraint corresponding to each employee point. The scheduling is executed based on soft time window constraints, and the output is a scheduling scheme corresponding to the preset satisfaction level; where the higher the satisfaction level, the higher the number of vehicles required, the total travel distance and the total cost.

[0042] In this embodiment of the disclosure, scheduling schemes with different satisfaction levels can also be generated. For example, a preset satisfaction level can be obtained first. For instance, before executing the scheduling, a preset satisfaction level parameter can be obtained, which can be used to characterize the strictness of employees' requirements regarding the time constraints for arriving at the target location. Then, based on the preset satisfaction level, the soft time window constraint corresponding to each employee point can be determined. For example, based on the obtained satisfaction level, combined with a soft time window determined based on a hard time window and an ideal time period, the upper and lower limits of the soft time window corresponding to the satisfaction level can be selected as the soft time window constraint for each employee point's target location. The higher the satisfaction level, the closer the soft time window is to the ideal time period, and the stricter the constraint. Afterwards, scheduling can be executed based on the soft time window constraint, outputting a scheduling scheme corresponding to the preset satisfaction level. For example, using the aforementioned determined soft time window constraint as the basis for judging the time of arrival at the target location, steps S202 to S305 can be repeated to complete the allocation of all employee points, outputting a scheduling scheme corresponding to the preset satisfaction level. Understandably, the higher the satisfaction level, the stricter the soft time window constraint, the more limited the range of selectable nodes, and the higher the required number of vehicles, total driving distance, and total cost.

[0043] In some possible implementations, vehicle capacity constraints include: The total number of employees at each employee point already included in the vehicle route is calculated as the current passenger capacity. Before selecting a new node, determine whether the cumulative passenger capacity after adding the number of employees corresponding to the new node exceeds the vehicle capacity. If the cumulative passenger volume after adding the number of employees corresponding to the new node exceeds the vehicle capacity, then adding the new node to the path is prohibited.

[0044] In this embodiment, the process of determining vehicle capacity constraints may include: accumulating the sum of the number of employees at each employee point already added to the vehicle path as the current passenger capacity. For example, for all employee points already included in the current vehicle path, the corresponding number of employees is summed to obtain the current passenger capacity of the vehicle. Then, before selecting a new node each time, it can be determined whether the cumulative passenger capacity after adding the number of employees corresponding to the new node exceeds the vehicle capacity. For example, before selecting a new node from the remaining unassigned employee points or target locations to add to the path, the number of employees corresponding to the new node can be determined first, and this number can be added to the current passenger capacity to obtain the cumulative passenger capacity after addition. It can then be determined whether this cumulative passenger capacity exceeds the preset vehicle capacity. If the cumulative passenger capacity after adding the number of employees corresponding to the new node exceeds the vehicle capacity, the addition of the new node to the path can be prohibited. That is, when the above determination result is that the cumulative passenger capacity exceeds the vehicle capacity, the new node does not meet the vehicle capacity constraint and cannot be added to the current vehicle path; it must be reselected from other candidate nodes.

[0045] In some possible implementations, the time to reach the target location falls within a corresponding soft time window, including: The arrival time at the destination is calculated by summing up the vehicle's departure time and the travel time between adjacent nodes along the route. Determine whether the arrival time at the target location falls within the corresponding soft time window of the target location; If the vehicle's current destination is an employee point, it is also necessary to determine whether the estimated arrival time from the employee point to its corresponding target location satisfies the soft time window constraint.

[0046] In this embodiment of the disclosure, the process of determining whether the arrival time at the target location is within the corresponding soft time window in step S203 can include: firstly, calculating the arrival time at the target location by accumulating the vehicle's departure time and the travel time between each adjacent node in the path. For example, the arrival time can be calculated by accumulating the vehicle's departure time and the travel time between each adjacent node along the path from the departure point to the target location. Then, it can be determined whether the arrival time at the target location is within the soft time window corresponding to the target location. For example, the calculated arrival time at the target location can be compared with the upper and lower limits of the soft time window corresponding to the target location to determine whether the time is within the soft time window range. Afterwards, if the vehicle's current destination is an employee point, it is also necessary to determine whether the expected arrival time from the employee point to its corresponding target location satisfies the soft time window constraint. For example, when the current destination of a vehicle path is an employee point, before adding the employee point as a candidate node to the path, in addition to determining the time of arrival at the employee point, it is also necessary to further estimate the arrival time from the employee point to its corresponding target location, and determine whether the estimated arrival time meets the corresponding soft time window constraint; if the estimated arrival time does not meet the soft time window constraint, then adding the employee point to the current path is prohibited.

[0047] In some possible implementations, the scheduling information may also include the departure time of each vehicle, the total distance traveled, the vehicle number assigned to each employee point, and their order in the route.

[0048] In this embodiment of the disclosure, the scheduling information, in addition to the vehicle's route employee point sequence and cost, may also include: the departure time of each vehicle, i.e., the departure time when each vehicle starts executing the route; the total travel distance of each vehicle, i.e., the sum of the travel distances between adjacent nodes in each vehicle's route; the vehicle number assigned to each employee point, i.e., the vehicle identifier number assigned to each employee point; and the order of each employee point in the route, i.e., the access order number of each employee point in its assigned vehicle's route. Through the output of the above scheduling information, the operational arrangements of each vehicle and the allocation of each employee point can be fully reflected.

[0049] To make the methods provided in this disclosure clearer, the following examples will be used for illustration.

[0050] The capacity resource scheduling method provided in this disclosure may include the following processing: 4.1 Model Construction.

[0051] In the problem of underground capacity scheduling, the model construction revolves around the goal of minimizing costs, while also taking into account employee satisfaction. The specific content can be summarized as follows: 4.1.1 Variable assumptions and symbolic representation.

[0052] (1) Basic information related: Service time range of the planned cycle T This indicates that the total number of employee points is... N The total number of employee work locations is M The departure point is represented by 216, and the node numbers are 1 to 216. N Orders for employees, N +1 to N + M For employees' work locations.

[0053] (2) Route and vehicle related: train number k The path contains a set of nodes. R k The set of employee points included in the work point is: N i Employee Points i The number of people is q i This indicates the number of employees transported in a single trip; the total number of available vehicles is [number missing]. V The maximum transport capacity of a single vehicle is Q The vehicle's speed is v 1 .

[0054] (3) Time-related: vehicle arrival node i The time is The departure time of the train is ;node i To the node j The shortest distance is Travel time .

[0055] (4) Satisfaction level is related to time window: employee satisfaction level is used as... express ), which is directly proportional to service quality; employee points i The hard time window is Soft time window (satisfaction) (below) ,and .

[0056] The relationship between employee satisfaction and the time window is represented by the fact that each contracted unit has a constraint on the time employees arrive at the unit within the planning period. (Hard time window) Employees must arrive at their workplace within this time frame. Employees do not want to arrive too early or too late, but rather within a narrow, ideal timeframe. Arriving within the (soft time window), display This paper measures employee satisfaction by the time employees arrive at or leave their workplace, assuming the ideal time frame for employee arrival or departure is... (According to the survey) if employees arrive or leave within this time period, most employees are very satisfied; this satisfaction level is set at 100%. When the arrival time is earlier than... or later At this point, employee satisfaction begins to decline until it exceeds the hard time window range, at which point the employee satisfaction will drop to 0. This functional relationship is as follows: Figure 3 As shown. Based on the above analysis, the employee design in this paper... i The point is the satisfaction level. The formula for calculating the time window is as follows:

[0057]

[0058] in, For the ideal time period, For hard time windows, also as Figure 2 As shown.

[0059] (5) Cost is related to decision variables: The cost per unit distance traveled by the vehicle is The one-time start-up cost is Employee Points The target location is ; 0-1 integer decision variables Indicates the first Whether the vehicle is in use (1 for used, 0 for unused); Indicates vehicle From node Directly reach the node (1 for yes, 0 for no).

[0060] 4.1.2 Objective function.

[0061] The model aims to minimize the sum of transportation costs for all vehicles within the planning period. The objective function is:

[0062] The first item is the total cost of vehicle operation. This determines which vehicles will be used (by...). Decide, Indicates the use of the first (vehicles) to control the fixed costs of starting the vehicle ( (Partial). The second item is the total cost of starting the vehicle, and the planned driving route for each vehicle (by...). Decide, Indicates the first Vehicle from node Drive directly to the node To minimize the variable costs of vehicle operation Part of, among which For nodes (Short distance).

[0063] 4.1.3 Constraints.

[0064]

[0065] (1) Path integrity constraints: After a vehicle leaves the departure point, it must continuously visit nodes (Equation 1); the bus starts from the departure point and the first node is the employee's point (Equation 2); the bus ends at the departure point and the node before the destination is the employee's target location (Equation 3).

[0066] (2) Uniqueness and necessity constraints of access: Each train can access the same employee's target location at most once (Equation 4); each employee's target location is accessed by at least one train (Equation 5).

[0067] (3) Pick-up and drop-off constraints: The vehicle must take the employees it carries to their corresponding target locations (Equation 6); if an employee is not picked up or dropped off, the employee will not visit their corresponding target location (Equation 7).

[0068] (4) Time constraints: There is no waiting time after the vehicle arrives at the node (Equation 8); the time of arrival at the employee's target location must be within the soft time window (Equations 9 and 10).

[0069] (5) Capacity constraint: The cumulative algebraic sum of passenger load at each node shall not exceed the maximum transport capacity of the vehicle (Equation 11).

[0070] 4.2 Algorithm Design.

[0071] Core idea: Based on the principle of "making the decision with the minimum cost increment each time," gradually construct train routes, prioritizing the satisfaction of constraints, and ultimately covering all employee locations. The specific implementation is as follows: Step 1: Initialize parameters and data: (1) Input basic data: all employee points Work location Departure point Geographical location; number of employees at each location Target location Vehicle capacity Unit operating cost Start-up costs Satisfaction Corresponding soft time window Unassigned employee point set (Initially all employee points).

[0072] (2) Initialization: Number of vehicles already in use All vehicles are in "unused" status. The path set is empty.

[0073] Step 2: Construct the initial route for the new train: (1) If (Unassigned employee points exist), start a new vehicle. , The starting point of the route is the departure point (0).

[0074] (2) From Select the first employee point: Prioritize the point with the lowest travel cost from the departure point to this employee point. (Right now Add it to the path and mark it. from Removed from the middle.

[0075] Step 3: Greedily expand the current train's route: (1) Select the next node: Based on the current path's endpoint (set as current), select the node with the smallest increase in total travel cost after adding it from the remaining unassigned employee points or their corresponding target locations. ; (2) If current is an employee point: the next node can be another employee point (the passenger capacity after addition must be met). or its corresponding target location (The arrival time must be within the soft time window). The cost increment is... ; (3) If current is the target location: the next node can only select unassigned employee points. (Requires meeting the passenger capacity requirements after addition) The cost increment is ; (4) Constraint check: Add node Then, the following must be met: vehicle capacity constraint, that is, the cumulative passenger volume (the sum of the number of employees) shall not exceed Time window constraint, i.e., the time to reach the target location within... Internal; path uniqueness, meaning that the target location is not visited repeatedly.

[0076] If there exist nodes that satisfy the constraints Add it to the path and update the current path. ;like It's an employee point, from Remove from .

[0077] Step 4: Terminate the current train route: If there are no nodes to add (or adding any node would violate the constraints), terminate the current route: return from the current destination (which must be the destination) to the departure point (0), and calculate the total distance traveled by the current route (all nodes in the route). (sum of each), the total cost is "driving expenses" Total distance Startup costs ".

[0078] Step 5: Iterate until all employee points are assigned: Repeat steps until (All employee points were assigned to trains).

[0079] Step 6: Output the result: Output all vehicles used ( The vehicles, the routes (node ​​sequences) for each train, and the total cost (the sum of the costs for all trains).

[0080] 4.3 Analysis of Experimental Design Results

[0081] This disclosure also includes a survey and experimental data acquisition of a coal mine. The mine's underground transportation capacity information during the planned work hours of 6:00-8:30 was obtained, and the simulation experimental data presented in this paper was generated based on this information. A total of 5 underground work sites were involved, with 1315 people and 210 employee points distributed over a 30km radius. Within a 30km rectangular area; these 5 work sites have different geographical locations and time window constraints; the vehicle's approved passenger capacity is 47, the vehicle start-up fee is 15 yuan, the driving cost is 1.3 yuan / km, and the vehicle speed is assumed to be 60km / h; the coordinates of the departure point are (12,13), and the information of the work sites is shown in Table 1.

[0082] Table 1. Downhole transport capacity information (during working hours)

[0083] Based on the experimental data above, the soft time window information of employees in each unit under the ideal satisfaction level was obtained through surveys. The soft time window information for satisfaction levels of 100%, 90%, and 80% was calculated, as shown in Table 2.

[0084] Table 2. Soft time windows based on different levels of satisfaction.

[0085] Table 3 Comparison of experimental results under different satisfaction levels

[0086] Table 3 shows that: ① Improving employee satisfaction comes at the cost of increased costs, total travel distance, and number of trips. Decision-makers can choose the optimal solution based on these indicators and different perspectives; ② For large-scale data involving 210 employee locations in a single service, the greedy algorithm can obtain the ideal solution within a reasonable timeframe under different satisfaction levels, demonstrating feasibility; ③ The model and algorithm can effectively minimize operating costs. Table 3 lists the dispatching plan under a 90% satisfaction level.

[0087] Table 4 Vehicle Routing and Scheduling Plans at 90% Satisfaction Rate

[0088] The capacity resource scheduling method provided in this disclosure can quantitatively calculate employee satisfaction based on hard and soft time windows; it takes into account the iterative optimization process of covering long-distance employee points, and ensures the algorithmic implementation of transportation accessibility through an iterative mechanism of "continuously building new train services until all employee points are allocated"; the greedy path construction algorithm based on the criterion of "minimizing cost increment" can be suitable for large-scale emergency scenario scheduling. In this way, transportation accessibility and fairness can be guaranteed: to address the problem that existing technologies are prone to omitting long-distance employee points, the algorithm logic of "iteratively covering all employee points" and "pick-up and drop-off corresponding constraints" avoid omissions, meet the needs of employee points at different distances, and improve fairness. Optimize employee satisfaction: to break through the limitation of existing methods that only associate with soft time windows, "transportation unreachability (hard time window default)" is included in the evaluation (satisfaction is 0 in ultra-hard time windows), and soft time windows corresponding to different satisfaction levels are designed to achieve refined quantification and optimization. Balancing cost and efficiency: With the goal of minimizing total cost, the greedy algorithm optimizes the path by selecting the node with the smallest cost increment. Experiments show that the total cost is 2239.4 yuan when the satisfaction rate is 80%, and the calculation time for 210 employee points is 4.51-5.57 seconds, demonstrating controllable cost and high efficiency. Enhancing feasibility and flexibility: Multi-dimensional constraints ensure compliance, support switching between different satisfaction targets, and allow users to choose between "cost-first" or "satisfaction-first" solutions as needed, adapting to diverse scenarios.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method.

[0090] According to embodiments of this disclosure, this disclosure also provides a transportation capacity scheduling device. For example, Figure 4 This is a schematic diagram of a transportation capacity resource scheduling device provided in an embodiment of the present disclosure. The transportation capacity resource scheduling device 400 includes: Data acquisition module 410 is used to acquire scheduling data; the scheduling data includes multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points; The node addition module 420 is used to start a vehicle with the departure point as the starting point when the unassigned employee point is not empty, and to select an employee point that meets a first preset condition to add the vehicle's path; wherein, the first preset condition includes the lowest travel cost from the departure point to the employee point. The node allocation module 430 is used to select nodes that meet the second preset conditions from the remaining unallocated employee points or the target location based on the destination of the current route of the vehicle and add them to the route. The second preset conditions include minimizing the increase in total travel cost after adding the node and satisfying the constraints. The constraints include at least one of the following: vehicle capacity constraint, arrival time at the target location within the corresponding soft time window, and route uniqueness constraint. The cost calculation module 440 is used to return the route from the destination to the departure point and calculate the cost of the vehicle when there is no node that satisfies the constraints. The scheduling module 450 is used to output scheduling information for all vehicles after all employee points have been allocated; wherein the scheduling information includes at least one of the following: the vehicle's path employee point sequence and the cost.

[0091] Furthermore, it also includes a soft time window determination module, used for: Based on the hard time window and ideal time period of the target location corresponding to the employee point, a soft time window is determined for the employee point under different satisfaction levels; wherein, the hard time window represents the time boundary that the employee must arrive at or leave the target location, and the employee satisfaction is zero if the employee exceeds the boundary of the hard time window; the soft time window is centered on the ideal time period, and different satisfaction levels correspond to different upper and lower limits of the soft time window, and the higher the satisfaction level, the closer the soft time window is to the ideal time period.

[0092] Furthermore, the cost calculation module 440 is used for: Based on the fact that all the vehicles are in use, determine the starting cost of the vehicles; The travel cost of the vehicle is determined based on the travel distance between each adjacent node in the vehicle's path and the travel cost per unit distance. The cost of the vehicle is determined based on the start-up cost and the driving cost.

[0093] Furthermore, the constraints also include: If the vehicle's path includes employee points, then the path must include the target location corresponding to the employee point, and the target location must be located after the employee point in the path; If the vehicle's path does not contain an employee point, then the path must not contain the target location corresponding to that employee point.

[0094] Furthermore, the node joining module 420 is used for: Calculate the travel cost from each unassigned employee point to the departure point; The employee point with the lowest travel cost from the departure point to the employee point is selected as the employee point to be added to the route.

[0095] Furthermore, it also includes a solution output module, used for: Obtain the preset satisfaction level; Based on the preset satisfaction level, determine the soft time window constraint corresponding to each employee point; Scheduling is performed based on the soft time window constraint, and a scheduling scheme corresponding to the preset satisfaction level is output; wherein, the higher the satisfaction level, the higher the number of vehicles required, the total travel distance, and the total cost.

[0096] Furthermore, the vehicle capacity constraint includes: The total number of employees at each employee point already included in the vehicle route is calculated as the current passenger capacity. Before selecting a new node each time, determine whether the cumulative passenger capacity after adding the number of employees corresponding to the new node exceeds the vehicle capacity. If the cumulative passenger volume after adding the number of employees corresponding to the new node exceeds the vehicle capacity, then adding the new node to the path is prohibited.

[0097] Furthermore, the time of arrival at the target location within the corresponding soft time window includes: The arrival time at the target location is calculated by summing the departure time of the vehicle and the travel time between each adjacent node in the route. Determine whether the time of arrival at the target location falls within the soft time window corresponding to the target location; If the vehicle's current destination is an employee point, it is also necessary to determine whether the estimated arrival time from the employee point to its corresponding target location satisfies the soft time window constraint.

[0098] Furthermore, the scheduling information also includes the departure time of each vehicle, the total travel distance, the vehicle number assigned to each employee point, and their order in the route.

[0099] It should be noted that the description of the features in the embodiment corresponding to the capacity resource scheduling device can be found in the relevant description of the embodiment corresponding to the capacity resource scheduling method, and will not be repeated here.

[0100] Embodiments of this disclosure also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0101] Embodiments of this disclosure also provide a computer-readable storage medium storing a computer program configured to perform the steps in any of the above method embodiments when executed.

[0102] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0103] Embodiments of this disclosure also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0104] Embodiments of this disclosure also provide another computer program product, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0105] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0106] The above provides a detailed description of a transportation capacity scheduling method disclosed herein. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this disclosure without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this disclosure.

Claims

1. A method for scheduling transportation capacity resources, characterized in that, include: Obtain scheduling data; The scheduling data includes multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points; If the unassigned employee point is not empty, the vehicle is started with the departure point as the starting point of the route, and an employee point that meets the first preset condition is selected from the employee points to join the route of the vehicle; wherein, the first preset condition includes the lowest travel cost from the departure point to the employee point. Based on the destination of the vehicle's current route, select a node that meets the second preset condition from the remaining unassigned employee points or the target location and add it to the route; the second preset condition includes minimizing the increase in total travel cost after addition and satisfying constraints, the constraints include at least one of the following: vehicle capacity constraint, arrival time at the target location within the corresponding soft time window, and path uniqueness constraint. If no node satisfies the constraints, the path returns from the destination to the departure point, and the cost of the vehicle is calculated. Once all employee points have been assigned, output the scheduling information for all vehicles; wherein the scheduling information includes at least one of the following: the vehicle's route employee point sequence and the cost.

2. The transportation capacity resource scheduling method according to claim 1, characterized in that, Before obtaining the scheduling data, the process also includes: Based on the hard time window and ideal time period of the target location corresponding to the employee point, a soft time window is determined for the employee point under different satisfaction levels; wherein, the hard time window represents the time boundary that the employee must arrive at or leave the target location, and the employee satisfaction is zero if the employee exceeds the boundary of the hard time window; the soft time window is centered on the ideal time period, and different satisfaction levels correspond to different upper and lower limits of the soft time window, and the higher the satisfaction level, the closer the soft time window is to the ideal time period.

3. The transportation capacity resource scheduling method according to claim 1, characterized in that, The calculation of the cost of the vehicle includes: Based on the fact that all the vehicles are in use, determine the starting cost of the vehicles; The travel cost of the vehicle is determined based on the travel distance between each adjacent node in the vehicle's path and the travel cost per unit distance. The cost of the vehicle is determined based on the start-up cost and the driving cost.

4. The transportation capacity resource scheduling method according to claim 1, characterized in that, The constraints also include: If the vehicle's path includes employee points, then the path must include the target location corresponding to the employee point, and the target location must be located after the employee point in the path; If the vehicle's path does not contain an employee point, then the path must not contain the target location corresponding to that employee point.

5. The transportation capacity resource scheduling method according to claim 1, characterized in that, The step of selecting employee points that meet the first preset condition to join the vehicle's path includes: Calculate the travel cost from each unassigned employee point to the departure point; The employee point with the lowest travel cost from the departure point to the employee point is selected as the employee point to be added to the route.

6. The transportation capacity resource scheduling method according to claim 2, characterized in that, The method further includes: Obtain the preset satisfaction level; Based on the preset satisfaction level, determine the soft time window constraint corresponding to each employee point; Scheduling is performed based on the soft time window constraint, and a scheduling scheme corresponding to the preset satisfaction level is output; wherein, the higher the satisfaction level, the higher the number of vehicles required, the total travel distance, and the total cost.

7. The transportation capacity resource scheduling method according to claim 1, characterized in that, The vehicle capacity constraints include: The total number of employees at each employee point already included in the vehicle route is calculated as the current passenger capacity. Before selecting a new node each time, determine whether the cumulative passenger capacity after adding the number of employees corresponding to the new node exceeds the vehicle capacity. If the cumulative passenger volume after adding the number of employees corresponding to the new node exceeds the vehicle capacity, then adding the new node to the path is prohibited.

8. The transportation capacity resource scheduling method according to claim 1, characterized in that, The time of arrival at the target location is within the corresponding soft time window, including: The arrival time at the target location is calculated by summing the departure time of the vehicle and the travel time between each adjacent node in the route. Determine whether the time of arrival at the target location falls within the soft time window corresponding to the target location; If the vehicle's current destination is an employee point, it is also necessary to determine whether the estimated arrival time from the employee point to its corresponding target location satisfies the soft time window constraint.

9. The transportation capacity resource scheduling method according to claim 1, characterized in that, The scheduling information also includes the departure time of each vehicle, the total travel distance, the vehicle number assigned to each employee point, and their order in the route.

10. A transportation capacity resource scheduling device, characterized in that, include: The data acquisition module is used to acquire scheduling data; the scheduling data includes multiple employee points, departure points, destination locations, vehicle capacity, and unassigned employee points. The node addition module is used to start a vehicle with the departure point as the starting point when the unassigned employee point is not empty, and to select an employee point that meets a first preset condition to add the vehicle's route; wherein, the first preset condition includes the lowest travel cost from the departure point to the employee point. The node allocation module is used to select nodes that meet a second preset condition from the remaining unallocated employee points or the target location based on the destination of the vehicle's current path and add them to the path. The second preset condition includes minimizing the increase in total travel cost after adding the node and satisfying constraints. The constraints include at least one of the following: vehicle capacity constraint, arrival time at the target location within a corresponding soft time window, and path uniqueness constraint. The cost calculation module is used to return the route from the destination to the departure point and calculate the cost of the vehicle if no node satisfies the constraints. The scheduling module is used to output the scheduling information of all vehicles after all employee points have been allocated; wherein the scheduling information includes at least one of the vehicle's route employee point sequence and cost.

Images