CN117035703A - Cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, system and equipment - Google Patents

Cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, system and equipment Download PDF

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CN117035703A
CN117035703A CN202311286304.4A CN202311286304A CN117035703A CN 117035703 A CN117035703 A CN 117035703A CN 202311286304 A CN202311286304 A CN 202311286304A CN 117035703 A CN117035703 A CN 117035703A
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辛欣
高晋升
周素霞
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Beijing University of Civil Engineering and Architecture
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Abstract

The invention discloses a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization system and cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization equipment, which relate to the field of inter-enterprise collaborative scheduling, and are used for firstly acquiring a product-level manufacturing task set in a cloud manufacturing environment; generating a real number sequence of a product-level manufacturing task order; acquiring a total virtual manufacturing resource set in a cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set; establishing objective functions of the total cost of production and logistics activities of all manufacturing subtasks, the maximum finishing time of product-level manufacturing tasks and the over-period duration of the product-level manufacturing tasks according to the processing time of the manufacturing subtasks, the unit cost data of the manufacturing subtasks, the logistics transfer time and the logistics transfer cost; and determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function. The invention can realize the selection of manufacturing resources and the scheduling optimization of manufacturing tasks, and generate an integrated inter-enterprise collaborative scheduling optimization scheme.

Description

Cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, system and equipment
Technical Field
The invention relates to the technical field of inter-enterprise collaborative scheduling, in particular to a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, system and equipment.
Background
From the era when ERP was originally widely used in manufacturing, to the day when networked manufacturing, manufacturing grids, and cloud manufacturing were proposed, the role of informatization in manufacturing has gradually increased. Cloud manufacturing mode can bring great benefit to manufacturing enterprises covered by the cloud manufacturing mode and production units of all levels. Unlike traditional manufacturing modes, cloud manufacturing is a service-oriented manufacturing mode. Multiple users may submit product-level manufacturing requirements (tasks) for processing by the cloud manufacturing platform to further enable service discovery, matching, selection, and combining. And finally, the execution and delivery of a plurality of manufacturing tasks are efficiently completed by distributing resources to submitted tasks or distributing submitted tasks to resources through scheduling. Compared with the traditional manufacturing mode, the cloud manufacturing not only needs to consider the execution sequence of the working procedures on equipment, but also needs to consider the selection of manufacturing resources governed by the cloud, the processing sequence of batch working procedure-level manufacturing tasks on the manufacturing resources, and the logistics transfer time and cost distributed among the manufacturing resources in different geographic positions.
In recent decades, a large number of optimization algorithms, such as integer programming accurate solution methods, multi-objective optimization algorithms and reinforcement learning models, have been proposed for inter-enterprise collaborative scheduling of cloud manufacturing, but there are many shortcomings in actual production scheduling. For example, integer programming accurate solution methods have extremely low solution efficiency for large-scale collaborative scheduling scenarios; the pareto criterion of the multi-objective optimization algorithm cannot give a deterministic solution, but a pareto front solution set; although reinforcement learning has a fast solution efficiency, the optimization effect is unstable, and it is often difficult to obtain an optimal solution. Therefore, it is difficult to be effectively applied to actual production scenes.
Disclosure of Invention
The invention aims to provide a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization system and cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization equipment, which can realize selection of manufacturing resources and scheduling optimization of manufacturing tasks and generate an integrated inter-enterprise collaborative scheduling optimization scheme.
In order to achieve the above object, the present invention provides the following solutions: a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method comprises the following steps: acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks.
Acquiring a total virtual manufacturing resource set in a cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services.
The processing time of the manufacturing subtasks, the unit cost data of the manufacturing subtasks, the logistics transit time and the logistics transit cost establish objective functions of the total cost of the production and logistics activities of all the manufacturing subtasks, the maximum finishing time of the manufacturing subtasks and the overtime time of the manufacturing subtasks based on the real sequence of the product level manufacturing task sequence and the real sequence selected by the manufacturing resources.
And determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function.
Optionally, the determining the production plan according to the objective function by adopting a hybrid coding artificial bee colony algorithm further comprises: the real sequence of the product level manufacturing task order and the real sequence of manufacturing resource selections are converted to a schedule.
Determining an index of the product-level manufacturing task in an objective function according to a time table corresponding to the product-level manufacturing task; the index comprises: maximum completion time of the product level manufacturing task, total cost of all manufacturing subtask production and logistics activities, and expiration time of the product level manufacturing task.
Optionally, the converting the real number sequence of the product-level manufacturing task order and the real number sequence of the manufacturing resource selection into the schedule specifically includes: step 1, judging real number sequence of product level manufacturing task sequenceWhether it is an empty set; if->Not empty set, let task number +.>Representing selection->Middle first task code->The method comprises the steps of carrying out a first treatment on the surface of the Task code from product level>Selecting manufacturing subtasks to operate, and enabling ∈ ->Then the first manufacturing sub-task is selected>Starting to execute the step 2; if it isEmpty, go to step 5.
Step 2, judging the current manufacturing subtaskWhether or not to meet->Number of all manufacturing subtasks involvedThe method comprises the steps of carrying out a first treatment on the surface of the If it meets->Turning to step 3; otherwise, go to step 4.
Step 3, obtaining a manufacturing subtaskReal number sequence selected in manufacturing resource->Is the position of (2)Selecting the code j by the manufacturing resource on the computer; if->For the first manufacturing sub-task->End time of updating mapping resource +.>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, get the previous manufacturing subtask +.>At->Is the position of (2)The resource on the resource selection resource codes h, and the end time of updating the mapping resource is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Update->、And +.>The method comprises the steps of carrying out a first treatment on the surface of the Subsequently let->Continuing to step 2; wherein (1)>For making resource R j Complete manufacturing subtask->Treatment time of->For making resource R j Complete manufacturing subtask->Is a unit cost of (2); TC (TC) h，j For making resource R h To manufacturing resource R j Is the logistics transportation cost, TT h，j For making resource R h To manufacturing resource R j Is a stream transfer time of (a); />Representing the number of all manufacturing subtasks for the task code q.
Step 4, fromDelete product level manufacturing task code->Turning to step 1.
Step 5, terminating the decoding flow and outputting the time map of each manufacturing sub-taskProduction cost of corresponding manufacturing resources->And the logistic transfer cost of the corresponding manufacturing resource +.>。
Optionally, the objective functionThe method comprises the following steps: />。
Wherein,is a target priority factor; q (Q) T The variable is an integer variable and represents the maximum finishing time of the product-level manufacturing task; q (Q) C Is an integer variable representing the total cost of all manufacturing subtask production and logistics activities; />Is an integer variable representing the length of the product level manufacturing task over time.
Optionally, the hybrid coded artificial bee colony algorithm employs a multi-point insertion strategy, a regional perturbation strategy and a local search strategy during the employment and following bee phases.
An inter-enterprise collaborative scheduling optimization system for cloud manufacturing, comprising: the product-level manufacturing task sequence coding module is used for acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks.
The manufacturing resource selection coding module is used for acquiring a total virtual manufacturing resource set in the cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services.
The objective function building module is used for building objective functions of total cost of production and logistics activities, maximum finishing time of the product-level manufacturing task and over-period duration of the product-level manufacturing task under a real number sequence based on the order of the product-level manufacturing tasks and a real number sequence selected by manufacturing resources.
And the production plan determining module is used for determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function.
An inter-enterprise collaborative scheduling optimization device for cloud manufacturing, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the described inter-enterprise collaborative scheduling optimization method for cloud manufacturing.
Optionally, the memory is a computer readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method, the system and the equipment for optimizing the inter-enterprise collaborative scheduling for cloud manufacturing provided by the invention acquire a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; acquiring a total virtual manufacturing resource set in a cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; the method is characterized in that a double-coding structure of manufacturing task sequence and manufacturing resource selection is established, and according to data such as processing time, cost, delivery period, logistics transit time and cost of manufacturing resources and the like of the manufacturing tasks, a mixed coding artificial bee colony algorithm is adopted to realize the selection of the manufacturing resources and the scheduling optimization of the manufacturing tasks, so that an integrated inter-enterprise collaborative scheduling optimization scheme is generated.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for optimizing cooperative scheduling among enterprises facing cloud manufacturing.
Fig. 2 is a schematic diagram of a framework of a collaborative scheduling optimization method between enterprises facing cloud manufacturing.
Fig. 3 is a flowchart of a hybrid coding artificial bee colony algorithm.
Fig. 4 is a schematic diagram of a multi-point insertion strategy, wherein part (a) of fig. 4 is a real sequence of a product level manufacturing task sequence, and part (b) of fig. 4 is a real sequence of manufacturing resource selection.
Fig. 5 is a schematic diagram of a regional perturbation strategy, wherein part (a) of fig. 5 is a real sequence of product level manufacturing task sequences, and part (b) of fig. 5 is a real sequence of manufacturing resource selections.
Fig. 6 is a schematic diagram of a local search strategy, where part (a) of fig. 6 is a real sequence of product-level manufacturing task sequences and part (b) of fig. 6 is a real sequence of manufacturing resource selections.
Fig. 7 is a gand diagram at target priority factor combination 1.
Fig. 8 is a gand plot under target priority factor combination 2.
Fig. 9 is a gand plot under target priority factor combination 3.
Fig. 10 is a schematic diagram of a time convergence curve of the hybrid coding artificial bee colony algorithm.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method, a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization system and cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization equipment, which can realize selection of manufacturing resources and scheduling optimization of manufacturing tasks and generate an integrated inter-enterprise collaborative scheduling optimization scheme.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1 and fig. 2, the inter-enterprise collaborative scheduling optimization method for cloud manufacturing provided by the invention includes: s101, acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks.
S102, acquiring a total virtual manufacturing resource set in a cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services.
And S103, establishing objective functions of total cost of production and logistics activities of all manufacturing subtasks, maximum finishing time of the manufacturing subtasks and over-period duration of the manufacturing subtasks based on the real sequence of the product level manufacturing task sequence and the real sequence selected by the manufacturing resources according to the processing time of the manufacturing subtasks, the unit cost data of the manufacturing subtasks, the logistics transit time and the logistics transit cost.
The decoding mechanism is as follows: (1) The real sequence of the product level manufacturing task order and the real sequence of manufacturing resource selections are converted to a schedule.
(2) Determining an index of the product-level manufacturing task in an objective function according to a time table corresponding to the product-level manufacturing task; the index comprises: maximum completion time of the product level manufacturing task, total cost of all manufacturing subtask production and logistics activities, and expiration time of the product level manufacturing task.
The decoding mechanism comprises the following specific steps: step 1, judging real number sequence of product level manufacturing task sequenceWhether it is an empty set; if->Not empty set, let task number +.>Representing selection->Middle first task code->The method comprises the steps of carrying out a first treatment on the surface of the Task code from product level>Selecting manufacturing subtasks to operate, and enabling ∈ ->Then the first manufacturing sub-task is selected>Starting to execute the step 2; if->Empty, go to step 5.
Step 2, judging the current manufacturing subtaskWhether or not to meet->Number of all manufacturing subtasks involvedThe method comprises the steps of carrying out a first treatment on the surface of the If it meets->Turning to step 3; otherwise, go to step 4.
Step 3, obtaining a manufacturing subtaskReal number sequence selected in manufacturing resource->Is the position of (2)Selecting the code j by the manufacturing resource on the computer; if->For the first manufacturing sub-task->End time of updating mapping resource +.>The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, get the previous manufacturing subtask +.>At->Is the position of (2)The resource on the resource selection resource codes h, and the end time of updating the mapping resource is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Update->、And +.>The method comprises the steps of carrying out a first treatment on the surface of the Subsequently let->Continuing to step 2; wherein (1)>For making resource R j Complete manufacturing subtask->Treatment time of->For making resource R j Complete manufacturing subtask->Is a unit cost of (2); TC (TC) h，j For making resource R h To manufacturing resource R j Is the logistics transportation cost, TT h，j For making resource R h To manufacturing resource R j Is a stream transfer time of (a); />Representing the number of all manufacturing subtasks for the task code q.
Step 4, fromDelete product level manufacturing task code->Turning to step 1.
Step 5, terminating the decoding flow and outputting the time map of each manufacturing sub-taskProduction cost of corresponding manufacturing resources->And the logistic transfer cost of the corresponding manufacturing resource +.>。
The matrices ET, tkc and tkw are obtained by the decoding mechanisms of steps 1-5, thereby calculating the correlation with the objective function、/>And->The index is shown in the formula (1) to formula (3): /> （1）。
（2）。
（3）。
The objective functionThe method comprises the following steps:
（4）。
wherein,is a target priority factor; q (Q) T The variable is an integer variable and represents the maximum finishing time of the product-level manufacturing task; q (Q) C Is an integer variable representing the total cost of all manufacturing subtask production and logistics activities; />Is an integer variable representing the length of the product level manufacturing task over time.
S104, determining a production plan by adopting a hybrid coding artificial bee colony algorithm (Hybird Encoding Based Artificial Bee Colony Algorithm, HE-ABC) according to the objective function.
The artificial bee colony algorithm (Artificial Bee Colony Algorithm, ABC) is a meta-heuristic algorithm inspired by the foraging behavior of bees, and the algorithm is first proposed by Karaboga et al in 2005 for the problem of combination optimization. Compared with other meta-heuristic algorithms, the algorithm has the advantages of fast convergence and difficult sinking into local optimum. The ABC algorithm flow can be summarized as: employment of Bees (sampled Bees), following Bees (onaloker Bees) and detection Bees (Scout Bees) stages. The employment bees are responsible for searching for honey sources and sharing collected information in the honeycomb, the follow bees collect the honey sources according to the information provided by the employment bees, and the investigation bees search for new honey sources aiming at the discarded honey sources. The ABC algorithm treats a solution (individual) as a honey source and the corresponding fitness value as the honey source pollen count. Any one honey source (individual) is represented by a real number code. In order to effectively improve the algorithm performance, the three stages respectively execute different optimization strategies in one iteration of the ABC algorithm. The goal of employing the bee stage is to maximize the quality of all individuals within the population, the following bee stage focuses on improving some of the excellent individual quality, and the investigation bee stage is responsible for eliminating the worse individuals and regenerating them. The algorithm can obtain a global optimal solution through multiple iterations.
As shown in fig. 3, the flow of the hybrid coding artificial bee colony algorithm is as follows: s1, selecting a binary tournament. Two different individuals are randomly selected in succession from the population, and the best (higher fitness) individual among them is then taken as the output of the binary tournament selection.
S2, adopting a bee stage, and improving the overall solution quality of the population. And selecting the sequentially selected individuals as the ontology solution and selecting the binary tournament individuals as the neighborhood solution. The quality of each solution is improved by combining the methods of multi-point insertion, regional disturbance and local search through the methods of exchanging, adjusting and recombining codes.
S3, following the bee stage, improving the overall solution quality of the population. The neighborhood search and the local search optimization method are combined, two excellent individuals are combined to search for a better individual, and therefore the optimality and the execution efficiency of the algorithm are improved to the greatest extent in one iteration.
In S2 and S3, the adopted multipoint insertion strategy, the area disturbance strategy and the local search strategy comprise the following specific steps:
the key steps of the multi-point insertion strategy are shown in fig. 4.
For example, the ontology solution is individualNeighborhood solution is individual->。/>For from individuals->Extracting and inserting individual->Is a coded number of (a) codes. For example, individual->Is (1,5,4,6,2,3) its neighborhood individuals->Is (3,2,4,1,5,6). Random selection->Middle->（/>=2) coding and preserving of positions, yielding the intermediate variable +.>=（/>，/>，/>). Subsequently, let in>The other codes are->The sequence of the inner is filled in->Generating a new coding sequence to update +.>. Similarly, resource coding also randomly extracts +.>Encoding of different positions of (a) to generate +.>. Except for->In addition to the selected position->The other codes are filled in according to the position>Generating a new resource coding sequence to update +.>。
The (two) multi-point disturbance strategy can avoid the local optimization of the algorithm, and aims at codingAnd->There are different operational flows.
For coding sequencesLet->、/> （/> ≠ />） /> />Representing two randomly generated integers +.>The sequence can be expressed as +.>= （/>,/>, . . . , /> , . . . , /> ,. . . , />). Introducing a random probability value->. If->The random rearrangement is located->And (3) withA subsequence therebetween; otherwise, random rearrangement->To->Between +.>To->A subsequence in between.
For coding sequencesRegeneration->、/>（/>≠/>）/> Two random integers. Thus (S)>The sequence can be expressed as +.>= （/>, />, . . . , />, . . . , />, . . . , />). The random probability value is +.>. If->In->And->The subsequences representing the resource selection will be randomly regenerated; otherwise, random regeneration ++>To->Sum of the twoTo->A subsequence of the same.
FIG. 5 shows the position as、/>Coding tasks->Disturbance flow of (2) and position +.>、Coding of resources->Is a disturbance flow of (1).
(III) the local search strategy relies on weak enumeration methods to promote the quality of selected individuals as much as possible. The key is to search the resource occupation of all manufacturing subtasks in one product-level manufacturing task and distribute the related manufacturing subtasks to the same manufacturing resource as much as possible so as to reduce the logistics time and cost. Assuming the selected individual isThen the local search strategy is directed to +.>And (3) withThe method comprises the following steps of:
(1) ReplicationAnd->Is->And->. Select->The first code of (2).
(2) Correspond selected product level manufacturing task code toMiddle phaseResource selection of subtask locations should be made. The largest shared resource number of all manufacturing subtasks is determined. Will->The related position of (3) is adjusted to the maximum shared resource number.
(3) Judging whether the current position isIf yes, go to (6); otherwise, will->The coding of the current position and the adjacent position are exchanged in sequence, and the process goes to (4).
(4) Based on after exchangeAnd ∈10 after resource occupation adjustment>Calculate target value +.>=/>（/>，/>). If the current exchange results in a decrease of the target value (fitness value), the rotation is terminated and the coding sequence +.>Turning to (5); otherwise, go to (3).
(5) Updating coding sequences = />And +.>=/>. Turning to (1).
(6) Terminating the local search flow and outputting the search resultAnd->。
Fig. 6 is a typical example of the local search flow. Wherein the local search flow is from an initial state to= (3, 2, 1, 4,6, 5) and +.>= (3, 3, 8, 6, 7, 7, 9, 5, 4), causing a decrease in the objective function value (fitness value), updated to +.>And->. Then, starting from the first code 3, the last position is sequentially shifted until the objective function value (fitness value) does not decrease. Finally, the local search flow is terminated, and the corresponding search sequence is output as +.>= (3, 2, 1, 4,6, 5) and= （3, 3, 8, 6, 7, 7, 9, 5, 4）。
step 6: and in the bee detection stage, the algorithm is prevented from being trapped in local optimum. Suppose that the stack of individuals k is not optimizedRecord the number of generations asIf iterative recording->Greater than a limit value->The individual is randomly reconstructed as a new individual and the iteration number of the new individual k is recorded +.>Is also reset to 0.
Step 7: until the iterative times of the algorithm reach the termination conditionThe algorithm stops and outputs the historical optimal solution.
The following description is made by way of specific examples, employing data checking that three enterprises cover multiple manufacturing resources and that both enterprise-level and plant-level process routes are well known. Table 1-Table 4 list key data for an instance of an inter-enterprise system production scheduling problem. The time units in each table are hours and the cost units are units. In Table 1, each product level manufacturing task includes a plurality of manufacturing sub-tasks. The manufacturing sub-tasks correspond to the process steps of a single manufacturing service that may be provided by a plurality of specific manufacturing resources governed by a cloud manufacturing platform. This results in different processing times and unit costs of the manufacturing resources for the manufacturing sub-tasks, with each product level manufacturing task having a given lead time. Table 2 lists manufacturing resources that enterprises may offer at different geographic locations.
Table 3 and table 4 list the cost of the mutual logistics transfer and the transfer time between enterprises at different geographical locations. In addition, tables 3 and 4 are typical asymmetric matrices, because round trip time and cost are not exactly equivalent for two places due to road, vehicle, etc. The data in tables 3 and 4 may be converted into standard flow TT, TC matrices between manufacturing resources, which are not described here.
And solving by adopting a hybrid coding artificial bee colony algorithm, and taking three groups of target priority factors to represent different importance degrees of three targets. The calculation experiments are respectively ranked as to the degree of no importance and degree of importance of each targetThe importance degree is ordered as +.>Is solved for by combining the three target priority factors.
Table 5 lists the target priority factors and target values for different combinations. Table 6 sets forth the numerical results of the solutions for each manufacturing subtask. Wherein, the combination 1 represents the same importance as the three targets, and the combination 2 represents the importance degree as followsCombination 3 indicates a degree of importance of +.>. The Gantt chart of the results of each target combination is shown in fig. 7-9.
As can be seen from the data in columns 8-11 of FIGS. 7-9 and Table 6, the key product level manufacturing tasks that affect the scheduling results for different target priority combinations are T4 and T3. In the combination 2, the delay time of T4 is 0, and the execution cost of the two manufacturing subtasks is 2800 and 1500 respectively; in combination 3, the latency of T4 is 60, and the execution costs of the two manufacturing subtasks are 2700 and 1200, respectively. The latency goal for combination 2 is the highest in importance, so the latency for T4 is the shortest. The cost goal of combination 3 is most important and the cost of T4 is least, resulting in an increase in delay time.
Specific parameter interpretations are shown in tables 7-9.
In the aspect of inter-enterprise collaborative scheduling under the cloud manufacturing environment, the current inter-enterprise collaborative scheduling is mainly based on an integer programming mathematical method or a multi-objective optimization algorithm (such as NSGA-II) and the like to realize scheduling optimization, so that an effective scheduling scheme is obtained. When the integer programming mathematical method is used as an optimization means, as the number of manufacturing tasks in a production scene increases, the number of variables in a corresponding mathematical model increases sharply, so that the solving efficiency decreases remarkably. The multi-objective optimization method such as NSGA-II utilizes the pareto front edge to obtain a pareto solution set, but for a cloud manufacturing platform with extremely high automation degree, an effective mode is not available for automatically selecting a solution with higher satisfaction degree from solution sets. In some cases, the satisfaction degree of the solution centralized scheduling scheme still depends on manual judgment. According to the invention, by establishing the artificial bee colony algorithm of the hybrid code and integrating iterative optimization of a plurality of optimization operators, the global optimal solution can be effectively searched, so that the optimization performance is improved on the premise of ensuring the calculation efficiency.
As shown in fig. 10, in the aspect of large-scale solution, the hybrid coding artificial bee colony algorithm can quickly converge to a very small optimal target value in the first 100 iterations. The reason is that in the local search strategy, a plurality of manufacturing subtasks under one main task can be distributed to the same manufacturing resource as far as possible aiming at the resource selection code, so that the corresponding logistics transfer cost and transfer time can be eliminated, and the optimization target value can be rapidly reduced. Therefore, there is a tendency that the fitness value rapidly decreases in fig. 10.
Corresponding to the method, the invention also provides a cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization system, which comprises the following steps:
the product-level manufacturing task sequence coding module is used for acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks;
the manufacturing resource selection coding module is used for acquiring a total virtual manufacturing resource set in the cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services;
the objective function building module is used for building objective functions of total cost of production and logistics activities, maximum finishing time of the product-level manufacturing task and over-period duration of the product-level manufacturing task of all manufacturing sub-tasks under a real number sequence based on the order of the product-level manufacturing tasks and a real number sequence selected by manufacturing resources;
and the production plan determining module is used for determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function.
In order to execute the method corresponding to the embodiment to achieve the corresponding functions and technical effects, the invention also provides inter-enterprise collaborative scheduling optimization equipment facing cloud manufacturing, which comprises the following steps: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the described inter-enterprise collaborative scheduling optimization method for cloud manufacturing.
The memory is a computer-readable storage medium.
Based on the above description, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned computer storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (8)

1. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method is characterized by comprising the following steps of:
acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks;
acquiring a total virtual manufacturing resource set in a cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services;
establishing objective functions of total production and logistics activity cost, maximum finishing time of the product-level manufacturing task and overtime time of the product-level manufacturing task of all manufacturing subtasks under a real sequence based on the order of the product-level manufacturing tasks and a real sequence selected by manufacturing resources according to the processing time of the manufacturing subtasks, the unit cost data of the manufacturing subtasks, the logistics transit time and the logistics transit cost;
and determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function.
2. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method according to claim 1, wherein determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to an objective function, further comprises:
converting the real sequence of the product-level manufacturing task order and the real sequence of manufacturing resource selection into a schedule;
determining an index of the product-level manufacturing task in an objective function according to a time table corresponding to the product-level manufacturing task; the index comprises: maximum completion time of the product level manufacturing task, total cost of all manufacturing subtask production and logistics activities, and expiration time of the product level manufacturing task.
3. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method according to claim 2, wherein the converting the real number sequence of the product-level manufacturing task order and the real number sequence of the manufacturing resource selection into a schedule specifically comprises:
step 1, judging real number sequence of product level manufacturing task sequenceWhether it is an empty set; if->Not empty set, let task number +.>Representing selection->Middle first task code->The method comprises the steps of carrying out a first treatment on the surface of the Task code from product level>Selecting manufacturing subtasks to operate, and enabling ∈ ->Then the first manufacturing sub-task is selected>Starting to execute the step 2; if->If the air is empty, the process goes to the step 5;
step 2, judging the current manufacturing subtaskWhether or not to meet->The number of all manufacturing subtasks involved +.>The method comprises the steps of carrying out a first treatment on the surface of the If it meets->Turning to step 3; otherwise, turning to step 4;
step 3, obtaining a manufacturing subtaskReal number sequence selected in manufacturing resource->Position->Selecting the code j by the manufacturing resource on the computer; if->For the first manufacturing sub-task->Updating end time of mapped resourceThe method comprises the steps of carrying out a first treatment on the surface of the Otherwise, get the previous manufacturing subtask +.>At->Position->The resource on the resource selection resource codes h, and the end time of updating the mapping resource is as followsThe method comprises the steps of carrying out a first treatment on the surface of the Update->、And +.>The method comprises the steps of carrying out a first treatment on the surface of the Subsequently let->Continuing to step 2; wherein (1)>For making resource R j Complete manufacturing subtask->Treatment time of->For making resource R j Complete manufacturing subtask->Is a unit cost of (2); TC (TC) h，j For making resource R h To manufacturing resource R j Is the logistics transportation cost, TT h，j For making resource R h To manufacturing resource R j Is a stream transfer time of (a); />Representing the number of all manufacturing subtasks for the task code q;
step 4, fromDelete product level manufacturing task code->Turning to step 1;
step 5, terminating the decoding flow and outputting the time map of each manufacturing sub-taskProduction cost of corresponding manufacturing resources->And the logistic transfer cost of the corresponding manufacturing resource +.>。
4. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method according to claim 3, wherein the objective functionThe method comprises the following steps:
；
wherein,is a target priority factor; q (Q) T The variable is an integer variable and represents the maximum finishing time of the product-level manufacturing task; q (Q) C Is an integer variable representing the total cost of all manufacturing subtask production and logistics activities; />Is an integer variable representing the length of the product level manufacturing task over time.
5. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization method according to claim 3, wherein the hybrid coded artificial bee colony algorithm employs a multi-point insertion strategy, a regional perturbation strategy and a local search strategy in an employment bee stage and a following bee stage.
6. An inter-enterprise collaborative scheduling optimization system for cloud manufacturing, comprising:
the product-level manufacturing task sequence coding module is used for acquiring a product-level manufacturing task set in a cloud manufacturing environment; and generating a real sequence of product-level manufacturing task orders; the product-level manufacturing task set in the cloud manufacturing environment comprises a plurality of product-level tasks; each product level task includes a plurality of manufacturing sub-tasks;
the manufacturing resource selection coding module is used for acquiring a total virtual manufacturing resource set in the cloud manufacturing environment; determining a manufacturing resource set corresponding to each manufacturing subtask; generating a real number sequence of manufacturing resource selection according to the manufacturing resource set corresponding to each manufacturing subtask; each manufacturing resource is configured to provide one or more manufacturing services;
the objective function building module is used for building objective functions of total cost of production and logistics activities, maximum finishing time of the product-level manufacturing task and over-period duration of the product-level manufacturing task of all manufacturing sub-tasks under a real number sequence based on the order of the product-level manufacturing tasks and a real number sequence selected by manufacturing resources;
and the production plan determining module is used for determining a production plan by adopting a hybrid coding artificial bee colony algorithm according to the objective function.
7. Inter-enterprise collaborative scheduling optimization equipment facing cloud manufacturing is characterized by comprising: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement an inter-enterprise co-scheduling optimization method for cloud manufacturing as claimed in any one of claims 1-5.
8. The cloud manufacturing-oriented inter-enterprise collaborative scheduling optimization apparatus according to claim 7, wherein the memory is a computer readable storage medium.
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