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CN-122022699-A - Multi-wave collaborative operation resource optimization method, system, equipment and storage medium

CN122022699ACN 122022699 ACN122022699 ACN 122022699ACN-122022699-A

Abstract

The invention discloses a multi-wave collaborative operation resource optimization method, a system, equipment and a storage medium, which comprise the steps of S1, obtaining multi-wave operation parameters, generating a three-dimensional linkage mapping table of window time, resource state and space position, S2, constructing a comprehensive optimization function taking minimum global total operation time and wave collaborative redundancy time and maximum window time utilization rate and multi-resource utilization rate as objective functions, configuring multi-class constraint to form a multi-objective optimization model, S3, adopting binary and real hybrid coding to code the model to generate an initial population, optimizing through elite reservation and self-adaption operation, outputting an optimal decision variable, S4, adopting a branch delimitation method to verify a scheduling scheme, outputting a final scheme if the optimal decision variable passes, otherwise, adjusting weight coefficient and genetic parameter and returning to S3. According to the invention, the problem of decoupling of windows and resources is solved through three-dimensional linkage mapping, and the global optimal scheduling of multi-wave collaborative operation is realized by combining an improved genetic algorithm with branch delimitation verification.

Inventors

  • Hong Wanfu
  • ZHAO BOWEI
  • Guo Shenting
  • HUANG ZAIBIN

Assignees

  • 厦门渊亭信息科技有限公司

Dates

Publication Date
20260512
Application Date
20260414

Claims (10)

  1. 1. The multi-wave collaborative operation resource optimization method under the window time constraint is characterized by comprising the following steps: S1, acquiring various parameters of a wave number set, a working unit set, a vehicle set, an elevator set, a warehouse set and a passable path set of multi-wave collaborative operation, and generating a three-dimensional linkage mapping table among window time, resource state and space position based on the various parameters; S2, constructing a comprehensive optimization function taking the minimum global total operation duration and wave-time cooperative redundancy time and the maximum window time utilization rate, the vehicle average utilization rate, the elevator average utilization rate and the warehouse average utilization rate as target functions based on the parameters and the three-dimensional linkage mapping table, and configuring time constraint, resource constraint, loading and association constraint, path and space constraint and decision variable constraint for the comprehensive optimization function to form a multi-target optimization model; S3, coding the multi-objective optimization model by adopting a mixed coding mode combining binary coding and real number coding to generate an initial population, optimizing the initial population by combining elite retention strategy and genetic iteration operation of self-adaptive parameter adjustment, and outputting an optimal individual meeting iteration termination conditions and a corresponding optimal decision variable; And S4, verifying the scheduling scheme corresponding to the optimal decision variable by adopting a branch-and-bound method to obtain a verification result, outputting a final scheduling scheme comprising operation time sequence, resource allocation and path planning if the verification result is passed, adjusting the weight coefficient of the comprehensive optimization function in the step S2 and the parameter of the genetic iteration operation in the step S3 if the verification result is not passed, returning to the step S3, and carrying out solution iteration again by taking the adjusted parameter as input.
  2. 2. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 1, wherein generating a three-dimensional linkage mapping table between window time, resource status and spatial position based on the parameters in step S1 comprises the following sub-steps: s11, determining global operation starting time and last wave ending time according to window time parameters of each wave in the acquired wave set, and dividing a time axis according to preset fixed time granularity; S12, recording real-time available states and position coordinates of each vehicle, real-time available states and position coordinates of each elevator, real-time available states and position coordinates of each warehouse and traffic states of each path according to the acquired parameters of the vehicle set, the elevator set, the warehouse set and the traffic path set, and aiming at each time unit on the time axis, so as to acquire resources and path states; S13, associating the window time parameters of each wave in the wave set and the binding operation window time parameters of each operation unit in the operation unit set in each wave with the resource and the path state to generate the three-dimensional linkage mapping table.
  3. 3. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 1, wherein in step S2, based on the parameters and the three-dimensional linkage mapping table, a comprehensive optimization function is constructed to minimize global total operation duration and wave collaborative redundancy time and maximize window time utilization, vehicle average utilization, elevator average utilization and warehouse average utilization as objective functions, and the expression of the comprehensive optimization function is: ; Wherein, the Representing a comprehensive optimization function; For the weight coefficient set according to the service priority, and satisfy And ; The global total operation duration is calculated according to the actual starting and ending time of all the wave times; is the cooperative redundant time among the waves; average utilization rate of the vehicle; the average utilization rate of the elevator is obtained; the average utilization rate of the warehouse is; Is window time average utilization.
  4. 4. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 3, wherein the expression of the global total operation duration is: wherein For the actual end time of the last wave-time, The global earliest job time; the expression of the cooperative redundancy time among the waves is as follows: wherein As the total number of wave times, 、 Respectively the first The actual start time and the actual end time of the individual wave times, Is the first The actual start time of the individual wave times; the expression of the average utilization rate of the vehicle is as follows: wherein As a total number of vehicles, In the first place for a single vehicle The time utilization within the individual wave times, 、 Respectively the single vehicle is at the first The start occupation time and the end occupation time of each wave number; the expression of the average utilization rate of the elevator is as follows: wherein For the total number of elevators, In the first elevator The time utilization within the individual wave times, 、 Respectively the single elevator is at the first The start occupation time and the end occupation time of each wave number; the expression of the average utilization rate of the warehouse is as follows: wherein For the total number of warehouses, In the first place for a single warehouse The time utilization within the individual wave times, 、 Respectively the single warehouse is at the first The start occupation time and the end occupation time of each wave number; The expression of the window time average utilization rate is as follows: wherein Is the first The total number of work units in a single wave pass, Is the first Within the first wave Binding window utilization for individual job units, 、 The actual start time and the actual end time of the unit of work respectively, 、 The earliest start time and the latest end time of the job unit, respectively.
  5. 5. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 1, wherein in step S3, a hybrid coding mode combining binary coding and real number coding is adopted to code the multi-objective optimization model to generate an initial population, the initial population is optimized through genetic iteration operation combined with elite retention strategy and adaptive parameter adjustment, and an optimal individual and a corresponding optimal decision variable meeting iteration termination conditions are output, and the method comprises the following sub-steps: s31, coding the multi-objective optimization model by adopting a mixed coding mode combining binary coding and real number coding, wherein each bit binary code in the binary coding section corresponds to one decision variable For indicating whether or not it is the first Within the wave number Individual work unit selection by vehicle Elevator Warehouse Path and route The resource combination scheme is formed, and a window compliance identification bit is additionally arranged in a binary code segment, and a real code segment represents the actual starting time of each operation unit Average utilization rate of the vehicle Average utilization of said elevator Average utilization rate of the warehouse The wave-time cooperative redundancy time Is a value of (2); s32, generating an initial population according to the binary coding section and the real coding section to obtain the initial population; s33, optimizing the initial population by adopting genetic iterative operation of elite retention strategy and self-adaptive parameter adjustment, wherein the method specifically comprises the steps of checking whether each individual in the initial population meets the time constraint, resource constraint, loading and association constraint, path and space constraint and decision variable constraint one by one, removing individuals which do not meet any constraint to obtain a feasible population, calculating the fitness value of each individual in the feasible population, using the individuals with the preset proportion of the fitness value ranked as elite individuals to be reserved to the next generation, dynamically adjusting the cross probability and variation probability according to the variance of the fitness value of the current population, executing cross operation and variation operation on the individuals except for the elite individuals to generate child individuals, checking whether the child individuals meet the time constraint, resource constraint, loading and association constraint, path and space constraint and decision variable constraint one by one, removing the individuals which do not meet any constraint to obtain the feasible child individuals, combining the elite individuals and the feasible child individuals to form a new generation population, and repeating the process until the iteration termination condition is met; S34, outputting an optimal individual and a corresponding optimal decision variable which meet the iteration termination condition.
  6. 6. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 1, wherein in step S4, a branch-and-bound method is adopted to verify a scheduling scheme corresponding to the optimal decision variable, and a verification result is obtained, and the method comprises the following sub-steps: S41, solving by adopting a branch-and-bound method and taking an objective function and constraint conditions of the multi-objective optimization model as the basis to obtain a reference solution and a corresponding reference objective function value; s42, substituting the optimal decision variable output in the step S3 into the comprehensive optimization function, and calculating to obtain an objective function value to be verified; S43, comparing the objective function value to be verified with the reference objective function value, and verifying whether the tasks in the scheduling scheme are all completed and whether the window time is completely in compliance, and the average utilization rate of the vehicle Average utilization of said elevator Average utilization rate of the warehouse Whether or not they are respectively not lower than the minimum utilization threshold value of the vehicle Elevator minimum utilization threshold Warehouse minimum utilization threshold And obtaining the verification result.
  7. 7. The method for optimizing multi-wave collaborative operation resources under window time constraint according to claim 1, wherein the step S4 is to adjust the weight coefficient of the comprehensive optimization function in the step S2 and the parameter of the genetic iterative operation in the step S3, specifically, sequentially adjusting the weight coefficient in the comprehensive optimization function according to a priority order And adjusting the crossover probability and the mutation probability in the step S3, and adjusting a preset vehicle minimum utilization threshold value, an elevator minimum utilization threshold value and a warehouse minimum utilization threshold value.
  8. 8. A multi-wave collaborative job resource optimization system under window time constraints for implementing the method of any one of claims 1-7, comprising: The parameter acquisition and mapping module is used for acquiring various parameters of a wave number set, a working unit set, a vehicle set, an elevator set, a warehouse set and a passable path set of multi-wave number collaborative operation, and generating a three-dimensional linkage mapping table among window time, resource state and space position based on the acquired various parameters; The model construction module is used for constructing a comprehensive optimization function taking the minimum global total operation duration and wave-time cooperative redundancy time and the maximum window time utilization rate, the vehicle average utilization rate, the elevator average utilization rate and the warehouse average utilization rate as objective functions based on the parameters and the three-dimensional linkage mapping table, and configuring time constraint, resource constraint, loading and association constraint, path and space constraint and decision variable constraint for the comprehensive optimization function to form a multi-objective optimization model; The optimization solving module is used for encoding the multi-objective optimization model by adopting a mixed encoding mode combining binary encoding and real number encoding to generate an initial population, optimizing the initial population by combining elite retention strategy and genetic iteration operation of self-adaptive parameter adjustment, and outputting an optimal individual meeting iteration termination conditions and a corresponding optimal decision variable; And the verification and output module is used for verifying the scheduling scheme corresponding to the optimal decision variable by adopting a branch-and-bound method to obtain a verification result, outputting a final scheduling scheme comprising operation time sequence, resource allocation and path planning if the verification result is passed, adjusting the weight coefficient of the comprehensive optimization function and the parameter of the genetic iteration operation if the verification result is not passed, and triggering the optimization solving module to re-solve the iteration by taking the adjusted parameter as input.
  9. 9. 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 the processor, when executing the computer program, implements the steps of the multi-wave collaborative job resource optimization method under the window time constraint of any one of claims 1 to 7.
  10. 10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the multi-wave collaborative job resource optimization method under the window time constraint of any one of claims 1-7.

Description

Multi-wave collaborative operation resource optimization method, system, equipment and storage medium Technical Field The invention relates to the technical field of warehouse logistics, in particular to a multi-wave collaborative operation resource optimization method, a multi-wave collaborative operation resource optimization system, multi-wave collaborative operation resource optimization equipment and a storage medium. Background Under the background of rapid development of the modern warehouse logistics industry, multi-wave warehouse-in and warehouse-out collaborative operation scenes of the connection of an underground stereoscopic warehouse and a ground city road network are increasingly common, and the scenes have the remarkable characteristics of complex operation flow, severe constraint conditions and high multi-resource collaborative requirements, and become one of core bottlenecks for improving warehouse logistics efficiency. The multi-wave collaborative operation mode is used as a key mode for meeting the requirements of large-scale and multi-batch operation, requires each wave operation to complete the whole flow operation within a specified window time, requires three heterogeneous core resources of collaborative dispatching of transport vehicles, vertical transfer elevators and warehouse locations, simultaneously considers the utilization rate of ground road network traffic space and underground warehouse operation space, and belongs to the typical multi-constraint, multi-objective and multi-dimensional NP-hard optimization problem. At present, the storage logistics multi-frequency operation scheduling field still faces a plurality of outstanding technical pain points, namely firstly window time is decoupled from multi-resource state, the existing scheme is used for restraining window time of a single operation link, the full-link linkage matching is not carried out on a warehouse operation window, an elevator transportation window and a road network passing window, double waste of 'idle operation window but occupied core resources' or 'ready core resources but over-period operation window' easily occurs, unsmooth inter-frequency resource connection and high operation interruption rate are caused, secondly multi-objective optimization unbalance is carried out, most schemes only take the shortest total operation duration or the highest single resource utilization rate as an optimization target, window time compliance rate, multi-resource equilibrium utilization rate, space conflict avoidance rate and other multi-dimensional indexes are not synchronously considered, the optimization result is not considered, thirdly, full-link management and control are not carried out on a vehicle passing path, elevator transportation time sequence and space collision risk of warehouse operation are not carried out on a ground cross-space operation scene, a resource minimum utilization rate threshold is not set, idle and configuration unbalance easily occurs, and the traditional genetic algorithm is easy to solve the adaptability is difficult to fall into a local adaptive genetic algorithm design and has a multi-resource coupling constraint mechanism, and a self-adaption demand is difficult to adapt to a local adaptive-speed constraint and a local-adaptive model. Therefore, a technical scheme capable of realizing global collaborative optimization of window time, space and multiple resources is needed in the art so as to break pain points in the prior art, improve operation efficiency and reduce operation cost. Disclosure of Invention In order to solve the problems of decoupling of window time and resource state, unbalance of multi-objective optimization, insufficient space-time constraint consideration and poor adaptability of a solving algorithm in the prior art, the invention provides a multi-wave collaborative operation resource optimization method, a multi-wave collaborative operation resource optimization system, multi-wave collaborative operation resource optimization equipment and a multi-wave collaborative operation resource optimization storage medium, so as to solve the technical defect problem. The invention provides a multi-wave collaborative operation resource optimization method under window time constraint, which comprises the following steps: S1, acquiring various parameters of a wave number set, a working unit set, a vehicle set, an elevator set, a warehouse set and a passable path set of multi-wave collaborative operation, and generating a three-dimensional linkage mapping table among window time, resource state and space position based on the various parameters; S2, constructing a comprehensive optimization function taking the minimum global total operation duration and wave-time cooperative redundancy time and the maximum window time utilization rate, the vehicle average utilization rate, the elevator average utilization rate and the warehouse average utilization rate as target functions b