Multi-wave cooperative operation resource optimization method, system, device and storage medium
By constructing a three-dimensional linkage mapping table and using a hybrid coding genetic algorithm to optimize the multi-objective function, the problem of decoupling window time and resource status and multi-objective optimization in multi-wave collaborative operations in warehousing and logistics was solved, achieving efficient collaborative scheduling of resources and improved operational efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAMEN YUANTING INFORMATION TECH CO LTD
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-21
AI Technical Summary
In modern warehousing and logistics, multi-wave collaborative operations suffer from problems such as decoupling of window time and resource status, imbalance of multi-objective optimization, insufficient consideration of spatiotemporal constraints, and poor adaptability of solution algorithms, resulting in low operational efficiency, high costs, and resource waste.
A three-dimensional linkage mapping table of window time, resource status and spatial location is constructed. A hybrid coding genetic algorithm and branch and bound method are used to optimize the multi-objective function, realize global collaborative scheduling of resources, and ensure the balance between job timing and resource utilization.
It achieves linkage and matching between window time and resource status, improves operational efficiency, reduces resource waste, adapts to complex scenarios of underground automated warehouses and ground road network connections, and improves the reliability and engineering practicality of scheduling schemes.
Smart Images
Figure CN122022699B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of warehousing and logistics technology, specifically to a method, system, equipment, and storage medium for optimizing resources in multi-wave collaborative operations. Background Technology
[0002] Against the backdrop of the rapid development of the modern warehousing and logistics industry, multi-wave inbound and outbound collaborative operation scenarios connecting underground automated warehouses with the surface urban road network are becoming increasingly common. These scenarios are characterized by complex operation processes, stringent constraints, and high demands for multi-resource coordination, and have become one of the core bottlenecks in improving warehousing and logistics efficiency. As a key method to cope with large-scale, multi-batch operation needs, the multi-wave collaborative operation mode requires each wave of operation to complete the entire process within a specified window time. It also requires the coordinated scheduling of three heterogeneous core resources: transport vehicles, vertical transfer elevators, and storage spaces. At the same time, it must take into account the utilization rate of surface road network access space and underground warehouse operation space. This is a typical multi-constraint, multi-objective, and multi-dimensional NP-hard optimization problem.
[0003] Currently, the field of multi-wave operation scheduling in warehousing and logistics still faces many prominent technical challenges: First, the window time is decoupled from the status of multiple resources. Existing solutions mostly constrain the window time of a single operation link without coordinating and matching the warehouse operation window, elevator transfer window, and road network access window across the entire chain. This easily leads to a double waste of "the operation window is idle but the core resources are occupied" or "the core resources are ready but the operation window has expired," resulting in poor resource coordination between waves and a high rate of operation interruption. Second, there is an imbalance in multi-objective optimization. Most solutions only take the shortest total operation time or the highest utilization rate of a single resource as the optimization objective, without simultaneously considering the window time. The optimization results for various indicators, such as time compliance rate, multi-resource balanced utilization rate, and spatial conflict avoidance rate, are inconsistent and incomplete. Third, the spatiotemporal constraints are not adequately considered. For underground-to-ground cross-space operation scenarios, the spatial collision risks of vehicle passage paths, elevator transfer sequences, and warehouse operation lines are not fully controlled, and no minimum resource utilization threshold is set, which easily leads to resource idleness and configuration imbalance. Fourth, the solution algorithm has poor adaptability. Traditional heuristic algorithms such as genetic algorithms have not designed adaptive mechanisms for multi-constraint coupled models, resulting in slow convergence speed and easy getting trapped in local optima, making it difficult to adapt to the real-time scheduling requirements of large-scale scenarios.
[0004] Therefore, there is an urgent need in this field for a technical solution that can achieve global collaborative optimization of window time, space, and multiple resources, in order to overcome the pain points of existing technologies, improve operational efficiency, and reduce operational costs. Summary of the Invention
[0005] To address the problems of decoupling window time and resource status, imbalance in multi-objective optimization, insufficient consideration of spatiotemporal constraints, and poor adaptability of solution algorithms in existing technologies, this invention provides a multi-wave collaborative operation resource optimization method, system, device, and storage medium to solve the aforementioned technical deficiencies.
[0006] This invention proposes a resource optimization method for multi-wave collaborative operations under window time constraints, which includes the following steps: S1. Obtain the parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set and passable path set of the multi-wave collaborative operation, and generate a three-dimensional linkage mapping table between window time, resource status and spatial location based on the parameters. S2. Based on various parameters and a three-dimensional linkage mapping table, construct a comprehensive optimization function with the objective function of minimizing the total global operation time and wave coordination redundancy time, and maximizing the window time utilization rate, vehicle average utilization rate, elevator average utilization rate and warehouse average utilization rate. Configure time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model. S3. The multi-objective optimization model is encoded using a hybrid encoding method that combines binary encoding and real number encoding to generate an initial population. The initial population is then optimized by combining an elite retention strategy with an adaptive parameter adjustment genetic iteration operation. The optimal individual that meets the iteration termination condition and the corresponding optimal decision variable are output. S4. Use the branch and bound method to verify the scheduling scheme corresponding to the optimal decision variable and obtain the verification result. If the verification result is successful, output the final scheduling scheme including job timing, resource allocation and path planning. If the verification result is unsuccessful, adjust the weight coefficient of the comprehensive optimization function in step S2 and the parameters of the genetic iteration operation in step S3, and return to step S3. Use the adjusted parameters as input to solve the iteration again.
[0007] Preferably, step S1, which generates a three-dimensional linkage mapping table between window time, resource status, and spatial location based on various parameters, includes the following sub-steps: S11. Based on the window time parameters of each wave in the obtained wave set, determine the global operation start time and the end time of the last wave, and divide the time axis with a preset fixed time granularity. S12. Based on the parameters of the acquired vehicle set, elevator set, warehouse set and passable path set, for each time unit on the time axis, record the real-time availability status and location coordinates of each vehicle, each elevator, each warehouse, and the passability status of each path to obtain the resource and path status. S13. Associate the window time parameters of each wave in the wave set and the bound job window time parameters of each job unit in the job unit set within each wave with the resource and path status to generate a three-dimensional linkage mapping table.
[0008] Preferably, in step S2, based on various parameters and the three-dimensional linkage mapping table, a comprehensive optimization function is constructed with the objective function of minimizing the total global operation time and wave coordination redundancy time, and maximizing the window time utilization rate, vehicle average utilization rate, elevator average utilization rate, and warehouse average utilization rate. The expression of the comprehensive optimization function is as follows: ; in, Represents the comprehensive optimization function; The weighting coefficients are set according to business priorities, and satisfy the following conditions: and ; The total global operation time is calculated based on the actual start and end times of all waves; This refers to the collaborative redundancy time between waves; This refers to the average utilization rate of vehicles. This represents the average utilization rate of the elevator. This represents the average utilization rate of the warehouse. This represents the average utilization rate of the window time.
[0009] A further preferred expression for the total global job duration is: ,in This refers to the actual end time of the last wave. This is the earliest time the operation can begin globally. The expression for the cooperative redundancy time between waves is: ,in The total number of waves, , The first The actual start time and actual end time of each wave For the first The actual start time of each wave; The expression for average vehicle utilization rate is: ,in The total number of vehicles. For a single vehicle in the first Time utilization rate within each wave , Each vehicle in the first The start and end times of each wave; The expression for the average elevator utilization rate is: ,in The total number of elevators. For a single elevator in the first Time utilization rate within each wave , Each elevator is in the first The start and end times of each wave; The expression for average warehouse utilization rate is: ,in The total number of warehouses, For a single warehouse in the first Time utilization rate within each wave , Each warehouse in the first The start and end times of each wave; The expression for the average utilization rate of the window time is: ,in For the first The total number of work units within each wave. For the first Within the first wave Utilization of the bound window for each job unit , These are the actual start time and actual end time of the work unit, respectively. , These are the earliest start time and latest end time of the work unit, respectively.
[0010] Preferably, step S3 employs a hybrid encoding method combining binary and real-number encoding to encode the multi-objective optimization model, generating an initial population. The initial population is then optimized through a genetic iterative operation combining an elite retention strategy and adaptive parameter adjustment, outputting the optimal individual that satisfies the iteration termination condition and its corresponding optimal decision variable. This includes the following sub-steps: S31. A hybrid encoding method combining binary encoding and real number encoding is used to encode the multi-objective optimization model, wherein each bit of the binary code segment corresponds to a decision variable. The value of is used to indicate whether it is the th . The first wave within the [number]th Each work unit is selected by the vehicle. ,elevator ,storehouse and path The resource combination scheme is constructed, and a window compliance flag is added to the binary encoding segment. The real number encoding segment represents the actual start time of each job unit. Average vehicle utilization rate Average elevator utilization rate Average warehouse utilization rate and wave-to-wave coordination redundancy time The possible values of ; S32. Generate an initial population based on the binary code segment and the real number code segment to obtain the initial population; S33. The initial population is optimized using an elite retention strategy and adaptive parameter adjustment genetic iterative operation. Specifically, this includes: verifying whether each individual in the initial population satisfies time constraints, resource constraints, load and association constraints, path and space constraints, and decision variable constraints, and removing individuals that do not satisfy any of the constraints to obtain a feasible population; calculating the fitness value of each individual in the feasible population, and retaining the individuals with the highest fitness values (a predetermined proportion) as elite individuals to the next generation; dynamically adjusting the crossover and mutation probabilities based on the variance of the current population fitness values, and performing crossover and mutation operations on individuals other than elite individuals to generate offspring individuals, and verifying whether each offspring individual satisfies time constraints, resource constraints, load and association constraints, path and space constraints, and decision variable constraints, and removing individuals that do not satisfy any of the constraints to obtain feasible offspring individuals; merging elite individuals with feasible offspring individuals to form a new generation population, and repeating the above process until the iteration termination condition is met. S34. Output the optimal individual that satisfies the iteration termination condition and the corresponding optimal decision variable.
[0011] Preferably, step S4 uses the branch and bound method to verify the scheduling scheme corresponding to the optimal decision variable and obtain the verification result, including the following sub-steps: S41. Using the branch and bound method, the objective function and constraints of the multi-objective optimization model are used to solve the problem and obtain the reference solution and the corresponding reference objective function value. S42. Substitute the optimal decision variables output in step S3 into the comprehensive optimization function to calculate the objective function value to be verified. S43. Compare the objective function value to be verified with the reference objective function value, and verify whether all tasks in the scheduling scheme are completed and whether the window time is fully compliant, and the average vehicle utilization rate. Average elevator utilization rate and average warehouse utilization rate Whether they are not lower than the minimum vehicle utilization threshold elevator minimum utilization threshold Minimum warehouse utilization threshold , and obtain the verification results.
[0012] Preferably, in step S4, adjusting the weight coefficients of the comprehensive optimization function in step S2 and the parameters of the genetic iteration operation in step S3 specifically involves: adjusting the weight coefficients in the comprehensive optimization function sequentially according to priority. Adjust the crossover probability and mutation probability in step S3, and adjust the preset minimum utilization thresholds for vehicles, elevators, and warehouses.
[0013] This invention also proposes a multi-wave cooperative operation resource optimization system under window time constraints, for implementing any of the methods described above, including: The parameter acquisition and mapping module is used to acquire various parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set and passable path set of multi-wave collaborative operations, and generate a three-dimensional linkage mapping table between window time, resource status and spatial location based on the acquired parameters. The model building module is used to construct a comprehensive optimization function based on various parameters and a three-dimensional linkage mapping table. The objective function is to minimize the total global operation time and wave coordination redundancy time, and maximize the window time utilization, vehicle average utilization, elevator average utilization and warehouse average utilization. The module also configures time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model. The optimization solution module is used to encode the multi-objective optimization model using a hybrid encoding method that combines binary encoding and real number encoding, generate an initial population, and optimize the initial population through a genetic iterative operation that combines an elite retention strategy with adaptive parameter adjustment, outputting the optimal individual that satisfies the iteration termination condition and the corresponding optimal decision variable. The verification and output module is used to verify the scheduling scheme corresponding to the optimal decision variable using the branch and bound method and obtain the verification result. If the verification result is successful, the final scheduling scheme containing job timing, resource allocation and path planning is output. If the verification result is unsuccessful, the weight coefficients of the comprehensive optimization function and the parameters of the genetic iteration operation are adjusted, and the optimization solution module is triggered to use the adjusted parameters as input to re-solve and iterate.
[0014] The present invention also proposes a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the steps of the multi-wave cooperative operation resource optimization method under window time constraints as described above.
[0015] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-wave cooperative operation resource optimization method under window time constraints as described above.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By constructing a three-dimensional linkage mapping table of “window time-resource status-spatial location”, this invention realizes one-to-one mapping and linkage matching of window time, multiple resource status and work space, thus avoiding the double waste of idle window but occupied resources or ready resources but expired window from the source, making the connection between waves smoother, and improving window utilization and resource utilization simultaneously.
[0017] (2) This invention constructs a multi-objective comprehensive optimization function that includes the total global operation time, wave collaborative redundancy time, window time utilization rate, vehicle average utilization rate, elevator average utilization rate and warehouse average utilization rate, and configures time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints, thereby achieving a collaborative balance of multi-dimensional objectives, overcoming the limitations of single objective optimization, and improving overall operation efficiency while ensuring window compliance.
[0018] (3) This invention adopts a mixed binary and real number encoding method, embeds window compliance flag bits, performs constraint linkage verification in the population initialization, crossover and mutation stages, removes invalid individuals, and combines elite retention strategy and adaptive genetic operation to effectively overcome the defects of traditional genetic algorithm that is slow to converge and easy to get trapped in local optima. It can quickly output the global optimal solution and meet the real-time scheduling requirements of large-scale multi-wave operations across underground and ground space.
[0019] (4) The present invention uses the branch and bound method to double-check the solution results to ensure the compliance and optimality of the solution; at the same time, a dynamic optimization mechanism is established, which can adjust the weight coefficients, genetic algorithm parameters and resource utilization thresholds according to the priority order of the verification results, and can adapt to abnormal disturbances in the operation process to ensure that the operation is not interrupted and the window does not time out, which greatly improves the engineering practicality and reliability of the solution.
[0020] (5) This invention incorporates vertical transfer elevators into the core resource scheduling scope, supplements spatial collision constraints and space occupancy thresholds, and comprehensively covers three core resources: vehicles, elevators, and warehouses, as well as cross-space path planning. It can adapt to complex scenarios where underground three-dimensional warehouses are connected to ground road networks, and has a wider range of applications. Attached Figure Description
[0021] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments, taken with reference to the accompanying drawings: Figure 1 This is a flowchart of a resource optimization method for multi-wave collaborative operations under window time constraints; Figure 2 This is a schematic diagram of a multi-wave collaborative operation resource optimization system under window time constraints; Figure 3This is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present invention. Detailed Implementation
[0022] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.
[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.
[0024] Figure 1 The flowchart of the resource optimization method for multi-wave collaborative operations under window time constraints is shown. (Refer to...) Figure 1 This invention proposes a resource optimization method for multi-wave collaborative operations under window time constraints, which includes the following steps: S1. Obtain the parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set, and passable path set for multi-wave collaborative operations, and generate a three-dimensional linkage mapping table between window time, resource status, and spatial location based on the parameters. Specifically, step S1 includes the following sub-steps: S11. Based on the window time parameters of each wave in the obtained wave set, determine the global operation start time and the end time of the last wave, and divide the time axis with a preset fixed time granularity. Specifically, determine the wave set. ( (A positive integer representing the total number of waves), specifying the dedicated time window for each wave. ( For the first The earliest start time of each wave, For the first (Latest end time of each wave). The timeline is divided into sections from the start time of the overall operation to the end time of the last wave, with a minimum time granularity of 1 minute. Simultaneously, the set of operation units within each wave is clearly defined. ( For the first The total number of work units within each wave determines the specific task requirements for each work unit, including the type, quantity, and specifications of the physical items, the size and load capacity of the boxes, as well as the matching relationship between the physical items and the boxes, and the one-to-one correspondence between the boxes and the work objectives (such as outbound, inbound, and transfer), to ensure that the task requirements are quantifiable and executable.
[0025] S12. Based on the parameters of the acquired vehicle set, elevator set, warehouse set, and passable path set, for each time unit on the time axis, record the real-time availability and location coordinates of each vehicle, each elevator, each warehouse, and the passability status of each path to obtain the resource and path status. Specifically, statistically analyze the vehicle set. ( (Total number of vehicles), record the model, size, rated load capacity, current location coordinates, maximum speed, and available scheduling time for each vehicle; and compile statistics on the elevator set. ( (Total number of elevators), record the rated load, lifting speed, floors stopped, current operating status, and location coordinates of each elevator; statistically analyze the warehouse set. ( (Total number of warehouses), record the number of storage locations, single storage location size, rated storage capacity, location coordinates, current inventory status, and number of workbenches for each warehouse; and compile a set of accessible paths. ( For each path (total number of paths), the topological sequence, actual path length, and standard passage time are analyzed to clarify passage restrictions. For each time unit on the timeline, the real-time availability and location coordinates of all resources are recorded. Simultaneously, spatial placement rules are established for warehouses, elevators, and work aisles, including box spacing, vehicle parking positions, and work unit operating space, clarifying spatial safety thresholds to provide a basis for subsequent spatial collision constraints.
[0026] S13. Associate the window time parameters of each wave in the wave set and the bound job window time parameters of each job unit in the job unit set within each wave with the resource and path status to generate a three-dimensional linkage mapping table. Specifically, determine the bound window time for each job unit. ( For the first Within the first wave The earliest start time of each work unit. (its latest end time), and satisfies , Using 1 minute as the smallest time granularity, a three-dimensional linkage mapping table of "window time - resource status - spatial location" is established. The table records the availability status (idle / occupied), occupation duration, and real-time location coordinates of each vehicle, elevator, and warehouse within each wave time window and each work unit bound window, as well as the access status and spatial location constraints of the corresponding passable paths within the time period. This achieves one-to-one mapping and linkage matching of window time, multiple resource statuses, and work space.
[0027] As an example, a 3D linkage mapping table can be recorded in the following table format:
[0028] S2. Based on various parameters and a three-dimensional linkage mapping table, construct a comprehensive optimization function with the objective function of minimizing the total global operation time and wave coordination redundancy time, and maximizing the window time utilization rate, vehicle average utilization rate, elevator average utilization rate and warehouse average utilization rate. Configure time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model.
[0029] Specifically, with the premise that all tasks are 100% completed, a weighted summation method is used to integrate six core optimization objectives: minimizing the total global operation time, minimizing wave coordination redundancy time, maximizing window time utilization, maximizing vehicle average utilization, maximizing elevator average utilization, and maximizing warehouse average utilization, to construct a comprehensive objective function.
[0030] The comprehensive optimization function expression is: ; in, Represents the comprehensive optimization function; The weighting coefficients are set according to business priorities, and satisfy the following conditions: and ; The total global operation time is calculated based on the actual start and end times of all waves; This refers to the collaborative redundancy time between waves; The average vehicle utilization rate is calculated based on vehicle occupancy time. This represents the average utilization rate of the elevator. This represents the average utilization rate of the warehouse. This represents the average utilization rate within the window of time. Weighting coefficients can be calibrated using the analytic hierarchy process (AHP), entropy weighting, or actual measured data from real-world business scenarios.
[0031] The expression for the total global job duration is: ; in This refers to the actual end time of the last wave. This is the earliest time when the operation can start globally.
[0032] The expression for the cooperative redundancy time between waves is: ; in The total number of waves, , The first The actual start time and actual end time of each wave For the first The actual start time of each wave.
[0033] The expression for average vehicle utilization rate is: ; in The total number of vehicles. For a single vehicle in the first Time utilization rate within each wave , Each vehicle in the first The start and end times of each wave.
[0034] The expression for the average elevator utilization rate is: ; in The total number of elevators. For a single elevator in the first Time utilization rate within each wave , Each elevator is in the first The start and end times of each wave.
[0035] The expression for average warehouse utilization rate is: ; in The total number of warehouses, For a single warehouse in the first Time utilization rate within each wave , Each warehouse in the first The start and end times of each wave.
[0036] The expression for the average utilization rate of the window time is: ; in For the first The total number of work units within each wave. For the first Within the first wave Utilization of the bound window for each job unit , These are the actual start time and actual end time of the work unit, respectively. , These are the earliest start time and latest end time of the work unit, respectively.
[0037] Constraints include the following five categories: (1) Time constraints: including global time constraints, i.e., the start time of all work units is not earlier than the earliest global start time, i.e. ; The binding window constraint stipulates that the actual start time of each job unit is no earlier than the earliest start time of its bound window, and the actual end time is no later than the latest end time of its bound window. Furthermore, the actual duration of the job unit is no less than the preset minimum effective job duration. ,and ; Wave time constraints mean that the actual end time of all work units within each wave is no later than the latest end time of that wave, and the start time interval between adjacent waves does not exceed the preset maximum allowable redundancy time. ,and ; The job sequence constraint means that the actual end time of each job unit is equal to the sum of its actual start time, loading time, transfer time, and unloading time. ,in For loading time, For transit time, The unloading time is calibrated according to the scenario parameters.
[0038] (2) Resource constraints: including conflict-free constraints, that is, at the same time, the same vehicle, the same elevator, or the same warehouse can only be assigned to one work unit. (No vehicle conflicts) The elevator and warehouse constraint logic are consistent; Minimum utilization constraint: within each wave, the utilization rate of each vehicle must not be lower than a preset minimum vehicle utilization threshold. The utilization rate of each elevator shall not be lower than the preset minimum elevator utilization rate threshold. The utilization rate of each warehouse shall not be lower than the preset minimum warehouse utilization threshold. ,Right now , , .
[0039] (3) Loading and association constraints: including association constraints, namely, one-to-one matching of physical objects and boxes, one-to-one matching of boxes and vehicles, and one-to-one matching of physical objects and work targets; size and weight constraints, namely, the size of the boxes shall not exceed the rated size of vehicles, elevators and warehouses, and the total weight of physical objects and boxes shall not exceed the rated load-bearing capacity of vehicles, elevators and warehouses.
[0040] (4) Path and space constraints: including path topology constraints, that is, the passage path of each work unit belongs to the set of passable paths and follows the path topology sequence; space collision constraints, that is, at the same time, the coordinates of the outer rectangles of vehicles, boxes and work equipment in the work area do not overlap, and the space occupancy rate (i.e. the ratio of actual occupied space to available space) does not exceed the preset space safety threshold.
[0041] (5) Decision variable constraints: including 0-1 decision variable constraints, i.e., decision variables A value of 1 indicates that the resource combination option is selected, and a value of 0 indicates that it is not selected. Utilization rate variable constraint, i.e., average vehicle utilization rate Average elevator utilization rate and average warehouse utilization rate All are not lower than their corresponding minimum utilization threshold and not higher than 1, that is , , ; Uniqueness constraint, meaning that each job unit corresponds to a unique combination of resources and paths, and all job tasks are assigned, i.e. .
[0042] S3. A hybrid encoding method combining binary and real-number encoding is used to encode the multi-objective optimization model, generating an initial population. This initial population is then optimized through a genetic iterative operation combining an elite preservation strategy and adaptive parameter adjustment, outputting the optimal individual that satisfies the iteration termination condition and its corresponding optimal decision variable. Specifically, step S3 includes the following sub-steps: S31. A hybrid encoding method combining binary encoding and real number encoding is used to encode the multi-objective optimization model, wherein each bit of the binary code segment corresponds to a decision variable. The value of is used to indicate whether it is the th . The first wave within the [number]th Each work unit is selected by the vehicle. ,elevator ,storehouse and path The resource combination scheme is constructed, and a window compliance flag is added to the binary encoding segment. The real number encoding segment represents the actual start time of each job unit. Average vehicle utilization rate Average elevator utilization rate Average warehouse utilization rate and wave-to-wave coordination redundancy time The value of .
[0043] S32. Generate an initial population based on the binary and real number encoded segments. Set the population size. (Value range 50-100, preferably 80), set the initial crossover probability. Initial mutation probability Maximum number of iterations An initial population is randomly generated, and each individual corresponds to a complete resource scheduling and job timing scheme.
[0044] S33. The initial population is optimized using an elite preservation strategy and adaptive parameter adjustment through genetic iterative operations. Specifically, this includes: Each individual in the initial population is verified to ensure that it meets the constraints of time, resources, loading and association, path and space, and decision variables. Individuals that do not meet any of the constraints are removed to obtain a feasible population.
[0045] Calculate the fitness value of each individual in the feasible population. The fitness function is positively correlated with the overall objective function, and its expression is:
[0046] in For the fitness function, To correct the coefficient, ensure that the fitness value is always positive.
[0047] Individuals with the highest fitness values (top 10%) will be retained as elite individuals and directly carried over to the next generation of the population.
[0048] The crossover and mutation probabilities are dynamically adjusted based on the variance of the current population fitness values. Crossover probability Value range: 0.7-0.9, preferably 0.8; Probability of variation The value range is 0.01-0.05, with 0.03 being preferred.
[0049] The selection process employs a combination of roulette wheel selection and elite retention strategy. The top 10% of individuals in terms of fitness are directly retained for the next generation, while the remaining individuals are selected based on their fitness percentage using a roulette wheel selection method.
[0050] Crossover and mutation operations are performed on individuals other than elite individuals to generate offspring. In the crossover operation, binary code segments use a single-point crossover method, randomly selecting crossover points and swapping the gene segments after the crossover points of two parent individuals; real code segments use an arithmetic crossover method, generating offspring through linear combinations. In the mutation operation, mutations are performed on the window compliance flag and resource allocation bits of the binary code segments, and random mutations are performed on the real code segments within the allowed constraints.
[0051] After the crossover and mutation operations, each offspring individual is checked to see if it meets the time constraints, resource constraints, loading and association constraints, path and space constraints, and decision variable constraints. Individuals that do not meet any of the constraints are eliminated to obtain feasible offspring individuals.
[0052] Elite individuals are merged with feasible offspring to form a new generation population, and the above process is repeated until the iteration termination condition is met. The iteration termination condition includes: reaching the maximum number of iterations (range 100-200, preferably 150), or the change in the optimal fitness value over 10 consecutive generations being less than a preset threshold (preferably). The optimal individual meets the preset optimization goals (such as 100% window compliance rate, resource utilization rate, and shortest operation time).
[0053] S34. Output the optimal individual that satisfies the iteration termination condition and the corresponding optimal decision variable.
[0054] S4. The branch-and-bound method is used to verify the scheduling scheme corresponding to the optimal decision variables. If the verification result is successful, the final scheduling scheme, including job timing, resource allocation, and path planning, is output. If the verification result is unsuccessful, the weight coefficients of the comprehensive optimization function in step S2 and the parameters of the genetic iteration operation in step S3 are adjusted, and the process returns to step S3, using the adjusted parameters as input for a new iteration. Specifically, step S4 includes the following sub-steps: S41. Using the branch and bound method, the objective function and constraints of the multi-objective optimization model are used to solve the problem and obtain the reference solution and the corresponding reference objective function value.
[0055] S42. Substitute the optimal decision variables output in step S3 into the comprehensive optimization function to calculate the objective function value to be verified.
[0056] S43. Compare the objective function value to be verified with the reference objective function value, and verify whether all tasks in the scheduling scheme are completed and whether the window time is fully compliant, and the average vehicle utilization rate. Average elevator utilization rate and average warehouse utilization rate Whether they are not lower than the minimum vehicle utilization threshold elevator minimum utilization threshold Minimum warehouse utilization threshold , and obtain the verification results.
[0057] If the verification result is successful, the final scheduling scheme, including job timing, resource allocation, and path planning, will be output.
[0058] If the verification result is unsuccessful, adjust the parameters in the following order of priority: first adjust the weight coefficients in the comprehensive optimization function. Next, adjust the crossover and mutation probabilities in step S3, and finally adjust the preset minimum vehicle utilization threshold. elevator minimum utilization threshold Minimum warehouse utilization threshold And space safety thresholds.
[0059] After adjusting the parameters, return to step S3, and use the adjusted parameters as input to solve and iterate again until the output satisfies the premise of task completion, which is the global optimal multi-wave collaborative operation scheme in terms of time, space and resource utilization.
[0060] To further demonstrate the advantages of this invention in vertical space scenarios and elevator resource scheduling, a simulation case is provided below. Assume that the task of work unit 7 in wave 2 (10:00-12:00) is "Underground 3rd floor warehouse 2 (WH2-B3, coordinates..." "The goods are taken out of the warehouse and transferred to the ground dispatch point via elevator." After the operation is triggered, the system calls the three-dimensional linkage mapping table established in step S1, and searches for available elevator resources from the 3rd basement level to the ground level within the wave 2 window with a time granularity of 1 minute. The mapping table shows that elevator 2 (stops at the 3rd basement level, the 2nd basement level, and the ground level) is idle from 10:15 to 10:25, and its real-time location is the elevator entrance on the 3rd basement level. It is closest to the outbound warehouse WH2-B3 of operation unit 7, and the rated load and size of the elevator meet the box loading requirements of operation unit 7. The system quickly locks elevator 2 as a dedicated vertical transfer resource through the mapping table, and records the time period during which elevator 2 is occupied, updating the status of elevator 2 in the mapping table to occupied. Operation unit 7 dispatches a forklift to WH2-B3. After B3 leaves the warehouse and completes loading, it proceeds to elevator entrance 2. Based on the spatial collision constraints of step S2, the system searches the 3D mapping table in real time for the space occupancy of elevator entrance 2 during the period of 10:14-10:16. It finds that the transfer vehicle of work unit 8 is scheduled to arrive at elevator entrance 2 at 10:15, and there is a risk of overlap between the bounding rectangles of the two vehicles. The system immediately triggers spatial collision constraint verification, adjusts the travel sequence of the vehicle of work unit 8, delaying its arrival time at elevator entrance 2 to 10:26, and at the same time fine-tunes the travel speed of the forklift of work unit 7 to ensure that it enters elevator 2 on time at 10:15, completes vertical transfer to the ground at 10:20, and elevator 2 is free again at 10:25. This completely avoids physical collisions between the forklift and the transfer vehicle at the elevator entrance, achieving safe and coordinated vertical space operations.
[0061] Further reference Figure 2 As an implementation of the above method, this invention provides a schematic diagram of a multi-wave collaborative operation resource optimization system 200 under window time constraints. This system can be specifically applied to various electronic devices. The multi-wave collaborative operation resource optimization system 200 under window time constraints includes the following modules: The parameter acquisition and mapping module 210 is used to acquire various parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set and passable path set of the multi-wave collaborative operation, and generate a three-dimensional linkage mapping table between window time, resource status and spatial location based on the acquired parameters. The model building module 220 is used to construct a comprehensive optimization function based on various parameters and a three-dimensional linkage mapping table. The objective function is to minimize the total global operation time and wave coordination redundancy time, and maximize the window time utilization, vehicle average utilization, elevator average utilization and warehouse average utilization. The module also configures time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model. The optimization solution module 230 is used to encode the multi-objective optimization model using a hybrid encoding method that combines binary encoding and real number encoding, generate an initial population, and optimize the initial population by combining an elite retention strategy with an adaptive parameter adjustment genetic iteration operation, outputting the optimal individual that satisfies the iteration termination condition and the corresponding optimal decision variable. The verification and output module 240 is used to verify the scheduling scheme corresponding to the optimal decision variable using the branch and bound method, and obtain the verification result. If the verification result is successful, the final scheduling scheme containing job timing, resource allocation and path planning is output. If the verification result is unsuccessful, the weight coefficients of the comprehensive optimization function and the parameters of the genetic iteration operation are adjusted, and the optimization solution module is triggered to use the adjusted parameters as input to re-solve and iterate.
[0062] The present invention also proposes a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the steps of the multi-wave cooperative operation resource optimization method under window time constraints as described above.
[0063] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-wave cooperative operation resource optimization method under window time constraints as described above.
[0064] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system 300 suitable for implementing terminal devices or servers of the present invention. Figure 3 The terminal device or server shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.
[0065] like Figure 3As shown, the computer system 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 302 or programs loaded from storage section 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the computer system 300. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.
[0066] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a liquid crystal display (LCD) and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card and a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.
[0067] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this invention. It should be noted that the computer-readable medium described in this invention can be a computer-readable signal medium or a computer-readable medium or any combination thereof. The computer-readable medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0068] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0069] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0070] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this invention.
Claims
1. A resource optimization method for multi-wave collaborative operations under window time constraints, characterized in that, Includes the following steps: S1. Obtain the parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set, and passable path set for multi-wave collaborative operations, and generate a three-dimensional linkage mapping table between window time, resource status, and spatial location based on the parameters. This includes the following sub-steps: S11. Based on the window time parameters of each wave in the obtained wave set, determine the global operation start time and the end time of the last wave, and divide the time axis with a preset fixed time granularity. S12. Based on the parameters of the acquired vehicle set, elevator set, warehouse set and passable path set, for each time unit on the time axis, record the real-time availability status and location coordinates of each vehicle, the real-time availability status and location coordinates of each elevator, the real-time availability status and location coordinates of each warehouse, and the passability status of each path to obtain resource and path status. S13. Associate the window time parameters of each wave in the wave set and the bound job window time parameters of each job unit in the job unit set within each wave with the resource and path status to generate the three-dimensional linkage mapping table. S2. Based on the parameters and the three-dimensional linkage mapping table, construct a comprehensive optimization function with the objective function of minimizing the total global operation time and wave coordination redundancy time, and maximizing the window time utilization rate, vehicle average utilization rate, elevator average utilization rate and warehouse average utilization rate. Configure time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model. S3. The multi-objective optimization model is encoded using a hybrid encoding method that combines binary encoding and real number encoding to generate an initial population. The initial population is then optimized by combining an elite retention strategy with a genetic iteration operation that dynamically adjusts the crossover and mutation probabilities based on the variance of the current population fitness value. The optimal individual that meets the iteration termination condition and the corresponding optimal decision variable are output. S4. The branch and bound method is used to verify the scheduling scheme corresponding to the optimal decision variable. It is verified whether all tasks in the scheduling scheme are completed and whether the window time is compliant, and whether the average utilization rate of vehicles, elevators, and warehouses is not lower than the preset minimum utilization rate threshold, elevator minimum utilization rate threshold, and warehouse minimum utilization rate threshold, respectively. The verification result is obtained. If the verification result is passed, the final scheduling scheme including job timing, resource allocation, and path planning is output. If the verification result is not passed, the weight coefficients of the comprehensive optimization function in step S2 and the parameters of the genetic iteration operation in step S3 are adjusted, and the process is returned to step S3. The adjusted parameters are used as input to solve the iteration again.
2. The resource optimization method for multi-wave collaborative operations under window time constraints according to claim 1, characterized in that, In step S2, based on the parameters and the three-dimensional linkage mapping table, a comprehensive optimization function is constructed with the objective function of minimizing the total global operation time and wave coordination redundancy time, and maximizing the window time utilization rate, vehicle average utilization rate, elevator average utilization rate, and warehouse average utilization rate. The expression of the comprehensive optimization function is as follows: ； in, Represents the comprehensive optimization function; The weighting coefficients are set according to business priorities, and satisfy the following conditions: and ; The total global operation time is calculated based on the actual start and end times of all waves; This refers to the collaborative redundancy time between waves; This refers to the average utilization rate of vehicles. This represents the average utilization rate of the elevator. This represents the average utilization rate of the warehouse. This represents the average utilization rate of the window time.
3. The resource optimization method for multi-wave collaborative operations under window time constraints according to claim 2, characterized in that, The expression for the total global job duration is: ,in This refers to the actual end time of the last wave. This is the earliest time the operation can begin globally. The expression for the cooperative redundancy time between wavelets is: ,in The total number of waves, , The first The actual start time and actual end time of each wave For the first The actual start time of each wave; The expression for the average vehicle utilization rate is: ,in The total number of vehicles. For a single vehicle in the first Time utilization rate within each wave , The individual vehicles are respectively in the first The start and end times of each wave; The expression for the average utilization rate of the elevator is: ,in The total number of elevators. For a single elevator in the first Time utilization rate within each wave , The single elevator is respectively in the first The start and end times of each wave; The expression for the average warehouse utilization rate is: ,in The total number of warehouses, For a single warehouse in the first Time utilization rate within each wave , The individual warehouses are respectively in the first The start and end times of each wave; The expression for the average utilization rate of the window time is: ,in For the first The total number of work units within each wave. For the first Within the first wave Utilization of the bound window for each job unit , These are the actual start time and actual end time of the work unit, respectively. , These are the earliest start time and the latest end time of the work unit, respectively.
4. The resource optimization method for multi-wave collaborative operations under window time constraints according to claim 1, characterized in that, Step S3 employs a hybrid encoding method combining binary and real-number encoding to encode the multi-objective optimization model, generating an initial population. This initial population is then optimized through a genetic iterative operation that combines an elite retention strategy with dynamic adjustment of crossover and mutation probabilities based on the variance of the current population's fitness values. The output includes the optimal individual satisfying the iteration termination condition and the corresponding optimal decision variables, comprising the following sub-steps: S31. The multi-objective optimization model is encoded using a hybrid encoding method combining binary encoding and real number encoding, wherein each bit of the binary code in the binary encoding segment corresponds to a decision variable. The value of is used to indicate whether it is the th . The first wave within the [number]th wave Each work unit is selected by the vehicle. ,elevator ,storehouse and path The resource combination scheme is constructed, and a window compliance flag is added to the binary encoding segment. The real number encoding segment represents the actual start time of each job unit. The average utilization rate of the vehicles The average utilization rate of the elevator The average utilization rate of the warehouse and the wave-coordinated redundancy time The possible values of ; S32. Generate an initial population based on the binary encoded segment and the real number encoded segment, and obtain the initial population; S33. The initial population is optimized using an elite retention strategy and a genetic iterative operation that dynamically adjusts the crossover and mutation probabilities based on the variance of the current population fitness values. Specifically, this includes: verifying whether each individual in the initial population satisfies the time constraints, resource constraints, load and association constraints, path and space constraints, and decision variable constraints; removing individuals that do not satisfy any constraint to obtain a feasible population; calculating the fitness value of each individual in the feasible population; retaining individuals with the highest fitness values (a predetermined proportion) as elite individuals to the next generation; dynamically adjusting the crossover and mutation probabilities based on the variance of the current population fitness values; performing crossover and mutation operations on individuals other than the elite individuals to generate offspring individuals; verifying whether each offspring individual satisfies the time constraints, resource constraints, load and association constraints, path and space constraints, and decision variable constraints; removing individuals that do not satisfy any constraint to obtain feasible offspring individuals; merging the elite individuals with the feasible offspring individuals to form a new generation population; and repeating the above process until the iteration termination condition is met. S34. Output the optimal individual that satisfies the iteration termination condition and the corresponding optimal decision variable.
5. The resource optimization method for multi-wave collaborative operations under window time constraints according to claim 1, characterized in that, Step S4 uses the branch and bound method to verify the scheduling scheme corresponding to the optimal decision variable. It verifies whether all tasks in the scheduling scheme are completed and whether the window time is compliant, and whether the average vehicle utilization rate, average elevator utilization rate, and average warehouse utilization rate are not lower than the preset minimum utilization rate threshold, elevator minimum utilization rate threshold, and warehouse minimum utilization rate threshold, respectively. The verification results are obtained, including the following sub-steps: S41. Using the branch and bound method, the objective function and constraints of the multi-objective optimization model are used to solve the problem and obtain the reference solution and the corresponding reference objective function value. S42. Substitute the optimal decision variables output in step S3 into the comprehensive optimization function to calculate the objective function value to be verified; S43. Compare the objective function value to be verified with the reference objective function value, and verify whether all tasks in the scheduling scheme are completed and whether the window time is fully compliant, and the average vehicle utilization rate. The average utilization rate of the elevator and the average utilization rate of the warehouse Whether they are not lower than the minimum vehicle utilization threshold elevator minimum utilization threshold Minimum warehouse utilization threshold The verification result is obtained.
6. The resource optimization method for multi-wave collaborative operations under window time constraints according to claim 1, characterized in that, In step S4, the weight coefficients of the comprehensive optimization function described in step S2 and the parameters of the genetic iteration operation described in step S3 are adjusted. Specifically, the weight coefficients in the comprehensive optimization function are adjusted sequentially according to priority. Adjust the crossover probability and mutation probability in step S3, and adjust the preset minimum utilization thresholds for vehicles, elevators, and warehouses.
7. A resource optimization system for multi-wave collaborative operations under window time constraints, used to implement the method as described in any one of claims 1 to 6, characterized in that, include: The parameter acquisition and mapping module is used to acquire various parameters of the wave set, the work unit set, vehicle set, elevator set, warehouse set and passable path set of multi-wave collaborative operation, and generate a three-dimensional linkage mapping table between window time, resource status and spatial location based on the acquired parameters. The model building module is used to construct a comprehensive optimization function based on the parameters and the three-dimensional linkage mapping table. The objective functions are to minimize the total global operation time and wave coordination redundancy time, and to maximize the window time utilization rate, vehicle average utilization rate, elevator average utilization rate and warehouse average utilization rate. The module also configures time constraints, resource constraints, loading and association constraints, path and space constraints and decision variable constraints for the comprehensive optimization function to form a multi-objective optimization model. The optimization solution module is used to encode the multi-objective optimization model using a hybrid encoding method that combines binary encoding and real number encoding, generate an initial population, and optimize the initial population by combining an elite retention strategy with a genetic iteration operation that dynamically adjusts the crossover probability and mutation probability based on the variance of the current population fitness value, and outputs the optimal individual that meets the iteration termination condition and the corresponding optimal decision variable. The verification and output module is used to verify the scheduling scheme corresponding to the optimal decision variable using the branch and bound method. It verifies whether all tasks in the scheduling scheme are completed and whether the window time is compliant, and whether the average vehicle utilization rate, average elevator utilization rate, and average warehouse utilization rate are not lower than preset minimum utilization rate thresholds, elevator minimum utilization rate thresholds, and warehouse minimum utilization rate thresholds, respectively, to obtain the verification result. If the verification result is successful, the final scheduling scheme including job timing, resource allocation, and path planning is output. If the verification result is unsuccessful, the weight coefficients of the comprehensive optimization function and the parameters of the genetic iteration operation are adjusted, and the optimization solution module is triggered to re-solve and iterate using the adjusted parameters as input.
8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the resource optimization method for multi-wave cooperative operations under window time constraints as described in any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the resource optimization method for multi-wave cooperative operations under window time constraints as described in any one of claims 1 to 6.