Workstation job shop scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses a workstation job shop scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: constructing a scheduling model of a workstation job shop; the scheduling model comprises transportation operation and processing operation of different jobs, and the processing operation comprises batch processing operation and discrete processing operation; coding a scheduling problem of a workstation job shop according to the scheduling model to obtain a chromosome sequence; decoding the chromosome sequence; the decoded chromosome sequence is updated, and then the minimum value of the maximum completion time of the processing operation is solved; and scheduling a transportation machine, a batch processing machine and a discrete machine in a work shop of the work station according to the chromosome sequence corresponding to the minimum value of the maximum completion time. According to the method, the minimum value of the maximum completion time of the processing operation can be solved by dynamically updating the chromosome sequence, so that the completion time of the transportation operation, the batch processing operation and the discrete processing operation is minimized, and the scheduling efficiency and the production efficiency are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing technology, and in particular to a workstation job shop scheduling method, device, electronic equipment and storage medium. Background Art

[0002] In the field of biomanufacturing, traditional manual methods have problems such as high technical complexity, high human error rate, low experimental throughput and high biological risks. The above problems lead to low purity and yield of biological substances, hinder standardization and scalability, and become a bottleneck for high-throughput biomanufacturing. The introduction of automated experimental equipment not only reduces human errors and improves production efficiency, but also reduces contamination risks and ensures biosafety, paving the way for addressing the challenges faced by manual methods. The introduction of automated workstations is essential to improving the efficiency and consistency of production processes.

[0003] Scheduling of automated workstations in biomanufacturing systems generally includes transportation, batch processing, and discrete processing, of which transportation and batch processing are relatively critical processes. On the one hand, the transportation of operations between machines requires the participation of transport machines, and the transportation capacity limitations of transport machines and the resulting transportation time consumption need to be considered. On the other hand, processes represented by centrifugation are bottleneck operations in biomanufacturing, and centrifuges can process multiple centrifuge tubes at the same time and have batch processing characteristics. However, current technical solutions are difficult to coordinate the scheduling of workstation operations in workshops with transportation, batch processing, and discrete processing, which affects production efficiency. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a workstation job shop scheduling method, device, electronic device and storage medium to efficiently schedule workstation job shops with transportation, batch processing and discrete processing, thereby improving production efficiency.

[0005] To achieve the above object, an embodiment of the present application provides a workstation job shop scheduling method, which includes the following steps:

[0006] Constructing a scheduling model for a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations;

[0007] Encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence;

[0008] Decoding the chromosome sequence to obtain the decoded chromosome sequence;

[0009] Updating the decoded chromosome sequence to solve the minimum value of the maximum completion time of the processing operation;

[0010] The transport machines, batch processing machines and discrete machines in the workstation job shop are scheduled according to the chromosome sequence corresponding to when the maximum completion time reaches a minimum value.

[0011] In some embodiments, the step of constructing a scheduling model for a workstation job shop comprises the following steps:

[0012] A scheduling model for the workstation job shop is constructed based on the constraints of each of the jobs.

[0013] In some embodiments, the construction of a scheduling model for a workstation job shop based on the constraints of each of the jobs comprises the following steps:

[0014] Determining condition constraints and operation constraints of each of the operations as the constraint conditions;

[0015] The conditional constraints include: each of the operations is independent of each other, all the operations are processed from the buffer at time zero, the transportation time depends on the distance between the two machines involved in the transportation and the number of transportations, and the time of the processing operation is predetermined;

[0016] The operation constraints include: the start time and completion time of the transportation operation, the start time and completion time of the processing operation, the processing time of the batch processing operation, and the allocation batch and operation volume of each of the operations;

[0017] The scheduling model of the workstation job shop is constructed based on the condition constraints and the operation constraints of each of the jobs.

[0018] In some embodiments, encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence includes the following steps:

[0019] Constructing the scheduling problem of the workstation job shop according to the scheduling model;

[0020] All the transport operations and corresponding job indexes of the transport sequence in the scheduling problem are extracted and then encoded into the chromosome sequence.

[0021] In some embodiments, decoding the chromosome sequence to obtain the decoded chromosome sequence comprises the following steps:

[0022] Decoding the chromosome sequence into a plurality of transport operations according to a reverse coding procedure;

[0023] Binding the processing operation to the transport operation that is a direct predecessor of the processing operation;

[0024] batching each of the operations according to adjacent arrivals;

[0025] A virtual job is introduced into each batch after batching to meet the batch processing constraint, and the decoded chromosome sequence is obtained.

[0026] In some embodiments, updating the decoded chromosome sequence comprises the following steps:

[0027] Combining a random generation method and a longest remaining time priority rule to generate a plurality of chromosome sequences as candidate populations;

[0028] Decoding each of the chromosome sequences in the candidate population;

[0029] Selecting a number of the chromosome sequences obtained after decoding the candidate population as the target population by using a binary tournament method and an elite strategy;

[0030] Determining whether the target population meets the conditions for resetting the strategy;

[0031] If the target population meets the conditions of the reset strategy, the target population is reset first, and then a crossover operation, a mutation operation and a local search are performed on the chromosome sequence in the target population to update the chromosome sequence in the target population;

[0032] If the target population does not meet the conditions of the reset strategy, performing a crossover operation, a mutation operation and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population;

[0033] Determine whether a termination condition is met. If the termination condition is not met, return to the step of decoding each chromosome sequence in the candidate population; if the termination condition is met, determine the chromosome sequence in the current target population as the final chromosome sequence.

[0034] In some embodiments, performing a crossover operation, a mutation operation, and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population comprises the following steps:

[0035] In the crossover operation, a single-point crossover operator or a two-point crossover operator is selected in the chromosome sequence of the target population with a first probability, and an improved exchange crossover operator is selected in the chromosome sequence of the target population with a second probability, and then the chromosome sequence of the target population is updated according to the single-point crossover operator, the two-point crossover operator or the improved exchange crossover operator; wherein the second probability is obtained by subtracting the first probability from 1;

[0036] In the mutation operation, one of a swap mutation operator, a shift mutation operator, a substring shift mutation operator or a reversal mutation operator is randomly selected, and then the chromosome sequence of the target population is updated according to the selected mutation operator;

[0037] In the local search, three positions in the chromosome sequence of the target population are randomly selected to generate a full permutation to construct a new chromosome sequence; if the maximum completion time of the chromosome sequence generated by the local search is less than the original chromosome sequence, the original chromosome sequence is replaced; otherwise, the original chromosome sequence is kept unchanged.

[0038] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a workstation job shop scheduling device, the device comprising:

[0039] A model building unit, used to build a scheduling model of a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations;

[0040] A problem encoding unit, used for encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence;

[0041] A sequence decoding unit, used for decoding the chromosome sequence to obtain the decoded chromosome sequence;

[0042] A problem solving unit, used for updating the decoded chromosome sequence and solving the minimum value of the maximum completion time of the processing operation;

[0043] The machine scheduling unit is used to schedule the transport machines, batch processing machines and discrete machines in the workstation operation workshop according to the chromosome sequence corresponding to the maximum completion time taking the minimum value.

[0044] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0045] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0046] The embodiments of the present application include at least the following beneficial effects:

[0047] The present application can construct a scheduling model for a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations; the scheduling problem of the workstation job shop is encoded according to the scheduling model to obtain a chromosome sequence; the chromosome sequence is decoded to obtain a decoded chromosome sequence; the decoded chromosome sequence is updated to solve the minimum value of the maximum completion time of the processing operation; the transportation machines, batch processing machines and discrete machines in the workstation job shop are scheduled according to the chromosome sequence corresponding to the minimum value of the maximum completion time. The present application can solve the minimum value of the maximum completion time of the processing operation by dynamically updating the chromosome sequence decoded from the scheduling problem, thereby minimizing the completion time of the transportation operation, batch processing operation and discrete processing operation, thereby improving the scheduling efficiency and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a workstation job shop scheduling method provided in an embodiment of the present application;

[0050] Figure 2 A flowchart of updating chromosome sequence provided in an embodiment of the present application;

[0051] Figure 3 An exemplary scheduling Gantt chart provided for an embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of a workstation job shop scheduling device provided in an embodiment of the present application;

[0053] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0055] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0056] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0058] The embodiments of the present application provide a workstation job shop scheduling method, device, electronic device and storage medium, and relate to the field of intelligent manufacturing technology. The workstation job shop scheduling method provided in the embodiments of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in the blockchain network; the software can be an application that implements the workstation job shop scheduling method, etc., but is not limited to the above forms.

[0059] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0060] Reference Figure 1 The embodiment of the present application provides a workstation job shop scheduling method, which may include but is not limited to steps S100 to S140, as follows:

[0061] S100: Constructing a scheduling model for a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations.

[0062] Specifically, a variety of different types of machines may be arranged in the workstation job workshop of the present embodiment, and various machines perform corresponding operations. Among them, the machines of the present embodiment may include transport machines, batch processing machines and discrete machines. The transport machines can perform transport operations to transport jobs, the batch processing machines can perform batch processing operations to process multiple jobs simultaneously, and the discrete processing machines can perform discrete processing operations to process one job.

[0063] It can be understood that this embodiment can model the process of scheduling each machine in the workstation job workshop to obtain a scheduling model, wherein the scheduling model can include the transportation operation of the transportation machine, the batch processing operation of the batch processing machine, and the discrete processing operation of the discrete processing machine.

[0064] Further, step S100 may include step S101:

[0065] S101: Constructing a scheduling model of a workstation job shop based on the constraints of each of the jobs.

[0066] Specifically, different jobs may require different constraints, such as processing time, processing conditions, etc. This embodiment can constrain the scheduling of each machine according to the constraints, and then build a scheduling model for the workstation job shop based on the constraints of each job.

[0067] Furthermore, step S101 may include steps S1011 to S1012:

[0068] S1011: Determine the conditional constraints and operational constraints of each of the jobs as the constraint conditions.

[0069] The conditional constraints include: each of the jobs is independent of each other, all the jobs are processed starting from the buffer at time zero, the transportation time depends on the distance between the two machines involved in the transportation and the number of transportations, and the time of the processing operation is predetermined.

[0070] It should be noted that the virtual job in the embodiment of the present application means: in order to more conveniently meet the capacity constraints of the batch processor, a virtual job is introduced in the scheduling process, and the q of the virtual job is 1, but it is not a real product in the manufacturing process.

[0071] Exemplarily, the scheduling model of this embodiment takes minimizing the maximum completion time as the optimization goal, and the objective function is as follows:

[0072]

[0073] Where i is the index of the job, j is the index of the processing operation, n is the number of jobs, and n iis the number of processing operations in the i-th job, max(C P ij ) is the completion time of the processing operation with the longest completion time among all processing operations.

[0074] Specifically, the conditional constraints include:

[0075] Each job is independent of each other;

[0076] All jobs can be processed from the buffer at time zero;

[0077] The transport time depends on the distance between the two machines involved and the number of transport operations;

[0078] Processing times are predetermined and include setup time for each machine.

[0079] The operation constraints include: the start time and the completion time of the transportation operation, the start time and the completion time of the processing operation, the processing time of the batch processing operation, and the allocation batch and the operation volume of each of the jobs.

[0080] Specifically, the operational constraints include:

[0081] It reflects the order constraint of operations in the same job. For two adjacent operation pairs in the same job, the transportation operation of the latter must start after the processing operation of the former is completed.

[0082] Reflects the order constraints in the transport machine. For two adjacent transport operations in the same transport machine, the latter must start after the former is completed.

[0083] This reflects that there is no buffer on discrete machines, that is, when a job arrives at a machine, the previous job on the machine should have started to leave. Considering that there is only one transport machine in the workstation, when a job starts to be transported to a machine, the previous job on the machine should have been transported to other machines.

[0084] This reflects that there is no buffer on the batch processing machine, that is, when a job starts to be transported to the batch processing machine, the previous batch of jobs on the machine should have been transported to other machines;

[0085] It reflects that the completion time of the transport operation is equal to the start time of the transport operation plus the transport time;

[0086] Reflects that the start time of a discrete processing operation on a discrete processing machine is no earlier than the transport completion time of the operation;

[0087] It reflects that on a discrete processing machine, the completion time of a processing operation is equal to the start time of the processing operation plus the processing time;

[0088] It reflects that on the batch processing machine, the start time of the batch operation is no earlier than the transportation completion time of each job in the batch;

[0089] It reflects that the processing time of a batch operation is determined by the operation with the longest processing time in the batch;

[0090] It reflects that on a batch processing machine, the completion time of a processing operation is equal to the start time of the processing operation plus the processing time;

[0091] This reflects that each job is assigned to one and only one batch; This reflects that the amount of work in any batch does not exceed the capacity of the batch processing machine;

[0092] It reflects that each job is always assigned to or not assigned to a batch;

[0093] Where r is O T ij The index of the transport machine in the transport sequence, k is the index of the machine, l is O P ij The index of the kth machine in the processing sequence, b is the index of the batch, O T ijr O P ijkl The direct predecessor of O is also the rth transport operation in the transport sequence. P ijkl is the jth processing operation of the i-th job and the lth processing operation of the k-th machine. klb P is the processing operation of the bth batch, including O P ijkl If X ijklb =1,q i is the amount of the i-th job, m is the number of machines, c is the capacity of the batch processing machine, T T ijr O T ijr The transportation time, T P ijkl O Pijkl The processing time, T klb P O klb P The processing time, S T ijr O T ijr The start time, S P ijkl O P ijkl The start time, S klb P O klb P The start time of C T ijr O T ijr The completion time, C P ijkl O P ijkl The completion time, C klb P O klb P The completion time, X ijklb O klb P Does it contain O P ijkl decision variables.

[0094] S1012: Constructing the scheduling model of the workstation job shop based on the condition constraints and the operation constraints of each of the jobs.

[0095] It can be understood that after determining the above-mentioned condition constraints and operation constraints, the process of scheduling each machine in the workstation job shop is set to establish a scheduling model.

[0096] S110: Encode the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence.

[0097] Specifically, in order to facilitate solving the minimum value of the maximum completion time of the processing operation, this embodiment can encode the scheduling problem, and the encoding result is used as the chromosome sequence.

[0098] Further, step S110 may include steps S111 to S112:

[0099] S111: Constructing the scheduling problem of the workstation job shop according to the scheduling model.

[0100] Specifically, the corresponding scheduling process is determined based on the scheduling model, and then the scheduling problem is established. The scheduling problem includes the operation sequence and processing time of each machine for the job.

[0101] S112: extract all the transport operations and corresponding job indexes of the transport sequence in the scheduling problem and encode them into the chromosome sequence.

[0102] Specifically, this embodiment can use an operation-based representation to encode and decode the scheduling problem, wherein all transportation operations in the transportation sequence can be extracted during encoding, and the job index is retained, thereby obtaining a chromosome sequence that can be used for the evolutionary algorithm.

[0103] This embodiment uses a simple encoding method so that complex scheduling problems can be solved by a variety of meta-heuristic algorithms (such as evolutionary algorithms) without additionally modifying the scheduling problem.

[0104] S120: Decode the chromosome sequence to obtain the decoded chromosome sequence.

[0105] Specifically, to facilitate subsequent calculations, this embodiment may decode the chromosome sequence to obtain a chromosome sequence that is convenient for calculation.

[0106] Further, step S120 may include steps S121 to S124:

[0107] S121: decoding the chromosome sequence into a plurality of transport operations according to a reverse encoding procedure;

[0108] S122: Binding the processing operation to the transport operation that is a direct predecessor of the processing operation;

[0109] S123: batching the operations according to adjacent arrivals;

[0110] S124: Introducing virtual jobs into each batch after batching to meet batch processing constraints, and obtaining the decoded chromosome sequence.

[0111] Specifically, during decoding, this embodiment first interprets the chromosome as a queue of transport operations according to the inverse encoding procedure, and binds the processing operation to the transport operation that is the direct predecessor of the processing operation, and then takes the following two steps to establish a feasible batch: the jobs are batched according to adjacent arrivals, and virtual jobs are introduced into the batches to meet the batch processing constraints. Wherein, adjacent arrival refers to two jobs that arrive consecutively in the same workstation, for example, transport machine 1 transports job 1 to workstation 1, workstation 1 operates on job 1, and then transport machine 2 transports the operated job 1 away from workstation 1, and then transport machine 1 transports job 2 to workstation 1, then job 1 and job 2 belong to adjacent arrival job pairs.

[0112] In addition, conflicts between batch processing machines and discrete transport machines, as well as conflicts in transport sequences, are resolved through flexible decoding.

[0113] The decoding method of this embodiment has the following technical features:

[0114] a. The decoding method establishes feasible batches by grouping jobs based on adjacent arrivals and giving priority to balance. On the one hand, the combination of batch rules and group initialization methods emphasizes the importance of logical and systematic management. On the other hand, the strategic introduction of virtual jobs reflects the value of simplifying problems and maintaining consistency in complex manufacturing systems.

[0115] b. Solve the contradiction between batch processing machines and transportation by adjusting the processing job sequence. Solve the contradiction caused by zero buffer on each machine by introducing additional transportation operations to the common buffer. These solutions highlight the importance of flexible decoding in effectively handling the constraint contradictions of the model.

[0116] It should be noted that the virtual job in the embodiment of the present application means that in order to more conveniently meet the capacity constraints of the batch processor, a virtual job is introduced in the scheduling process, and the q of the virtual job is 1, which only contains one T P =0 operation is not a real product in the manufacturing process.

[0117] S130: updating the decoded chromosome sequence to obtain the minimum value of the maximum completion time of the processing operation.

[0118] It should be noted that the step of updating the decoded chromosome sequence in this embodiment can be repeated multiple times to continuously iterate and update the decoded chromosome sequence. When the updated chromosome sequence meets the preset conditions, the updating is stopped, and then the minimum value of the maximum completion time of the processing operation is solved based on the latest chromosome sequence.

[0119] Furthermore, the step of updating the decoded chromosome sequence in step S130 may include steps S131 to S137:

[0120] S131: Combining a random generation method and a longest remaining time priority rule to generate a plurality of chromosome sequences as candidate populations;

[0121] S132: Decoding each chromosome sequence in the candidate population;

[0122] S133: selecting a number of the chromosome sequences obtained after decoding the candidate population as the target population by using a binary tournament method and an elite strategy;

[0123] S134: Determine whether the target population meets the conditions for resetting the strategy;

[0124] S135: If the target population meets the conditions of the reset strategy, the target population is reset first, and then a crossover operation, a mutation operation and a local search are performed on the chromosome sequence in the target population to update the chromosome sequence in the target population;

[0125] S136: If the target population does not meet the conditions of the reset strategy, performing a crossover operation, a mutation operation, and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population;

[0126] S137: Determine whether a termination condition is met. If the termination condition is not met, return to the step of decoding each chromosome sequence in the candidate population; if the termination condition is met, determine the chromosome sequence in the current target population as the final chromosome sequence.

[0127] Reference Figure 2 This embodiment provides a flowchart for updating chromosome sequences, and this embodiment can be used to solve the workstation job shop scheduling problem with transportation, batch processing and discrete processing.

[0128] Specifically, in this embodiment, the population may be initialized first: according to the updated decoded chromosome sequence and in combination with the random generation method and the longest remaining time priority rule, the diversity and quality of the initial population are ensured.

[0129] Selection and reset strategy: The population is selected through the binary tournament method and the elite strategy, and the reset strategy is introduced at the right time to maintain the diversity of the population. This embodiment combines the selection of the binary tournament method and the elite strategy, and resets at critical moments, which not only maintains the excellent competitiveness of the population, but also maintains the diversity of the population.

[0130] Population update: Update the population through crossover and mutation operations, and further optimize the solution in combination with local search.

[0131] This embodiment updates the chromosome sequence through an adaptive parameter adjustment mechanism, and can dynamically adjust key parameters such as the crossover rate according to feedback from the search process. At the same time, combined with a local search strategy, the search accuracy and convergence speed are further improved.

[0132] Furthermore, the step of performing a crossover operation, a mutation operation, and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population in steps S135 and S136 may include the following steps:

[0133] In the crossover operation, a single-point crossover operator or a two-point crossover operator is selected in the chromosome sequence of the target population with a first probability, and an improved exchange crossover operator is selected in the chromosome sequence of the target population with a second probability, and then the chromosome sequence of the target population is updated according to the single-point crossover operator, the two-point crossover operator or the improved exchange crossover operator; wherein the second probability is obtained by subtracting the first probability from 1;

[0134] In the mutation operation, one of a swap mutation operator, a shift mutation operator, a substring shift mutation operator or a reversal mutation operator is randomly selected, and then the chromosome sequence of the target population is updated according to the selected mutation operator;

[0135] In the local search, three positions in the chromosome sequence of the target population are randomly selected to generate a full permutation to construct a new chromosome sequence; if the maximum completion time of the chromosome sequence generated by the local search is less than the original chromosome sequence, the original chromosome sequence is replaced; otherwise, the original chromosome sequence is kept unchanged.

[0136] Specifically, in the crossover operation, the probability (First probability) selects a single-point crossover operator or a two-point crossover operator, and selects an improved exchange crossover operator with probability 1-P (second probability), where a is the current iteration number and A is the total iteration number:

[0137] The single-point crossover operator is used to select a random point on the chromosome and use this point as the boundary to exchange segments between the two parental chromosomes;

[0138] The two-point crossover operator is used to select two random points on the chromosome and exchange the parent chromosome segments between these two points;

[0139] The improved exchange crossover operator smoothly converts the worse parent into the better parent through a series of exchanges. Specifically, the worse parent is denoted as P1 and the other parent as P2. The elements in P1 and P2 are compared one by one from front to back to see if they are the same. If the u-th position is different, search for the position v where the value of the u-th position of P1 appears in P2, and exchange the values ​​of the u-th position and the v-th position of P1 as the offspring.

[0140] In the crossover operation of the present embodiment, a combined crossover operator is used to explore the solution space by single-point crossover and two-point crossover in the early stage, while targeting promising areas in the iterative process by gradually increasing the use of the improved exchange crossover operator, which is computationally intensive. In addition, the improved exchange crossover operator converts the parent solution P1 to P2 through a series of exchange operations, wherein each exchange involves changing the positions of two elements. This process introduces limited perturbations to the parents, allowing the generated transitional solutions to inherit more features. Therefore, the possibility of finding a solution superior to P1 is higher. The purpose of the modification of the present embodiment is to make the inferior parent advance toward the superior parent and consider all potential offspring, thereby improving the convergence efficiency.

[0141] In the mutation operation, one of the following mutation operators is randomly selected to generate offspring:

[0142] The exchange mutation operator is used to exchange the values ​​of two random positions in the parent chromosome;

[0143] The shift mutation operator is used to move the value of a random position in the parent chromosome to another random position;

[0144] The substring shift mutation operator is used to move a segment from a random position in the parent chromosome to another random position;

[0145] The inversion mutation operator is used to invert genes in a random range in the parent chromosome.

[0146] This embodiment uses a variety of mutation operators in combination to achieve better mutation effect than using a single mutation operator.

[0147] In the local search, three random positions in the chromosome are selected to generate a full permutation to construct a new individual. If the maximum completion time of any new individual generated by the local search is better than the original individual, the original individual is replaced; otherwise, the original individual remains unchanged. The full permutation in this embodiment refers to rearranging the three selected random positions on the chromosome sequence to obtain a new chromosome sequence.

[0148] Compared with the local search based on simulated annealing, the local search of this embodiment avoids accepting worse solutions, thereby reducing the exploration in bad areas and providing more stable improvements during the search process; and does not require parameter adjustment of temperature parameters and cooling rate, making the algorithm easier to implement and tune.

[0149] S140: Scheduling the transport machines, batch processing machines and discrete machines in the workstation operation workshop according to the chromosome sequence corresponding to the time when the maximum completion time reaches the minimum value.

[0150] It can be understood that after the step of updating the chromosome sequence in the embodiment of the present application, a chromosome sequence containing the minimum value of the maximum completion time of the processing operation can be obtained. This embodiment can schedule the transport machines, batch processing machines and discrete machines in the workstation job workshop based on the chromosome sequence to achieve efficient machine scheduling.

[0151] The method proposed in the embodiment of the present application can solve the workstation job shop scheduling problem with transportation, batch processing and discrete processing, can effectively solve related scheduling problems, improve scheduling performance, optimize completion time, and better meet the actual characteristics of automated workstations. In some embodiments, it can effectively shorten production time and significantly improve production efficiency for the specific needs of crude protein purification automated workstations in biomanufacturing systems.

[0152] In summary, some embodiments of the present application have the following beneficial effects:

[0153] 1. The use of simple encoding methods allows complex scheduling problems to be solved by a variety of meta-heuristic algorithms (such as evolutionary algorithms) without additional modifications to the scheduling problem.

[0154] 2. The introduction of virtual operations reflects the value of simplifying problems and maintaining consistency in complex manufacturing systems.

[0155] 3. Timely introduction of reset strategy improves the diversity of chromosome sequence population.

[0156] 4. Perform a crossover operation on the parental chromosome sequences to make the inferior parent move toward the superior parent and consider all potential offspring, thereby improving convergence efficiency.

[0157] 5. The comprehensive use of multiple mutation operators can achieve better mutation effects than using a single mutation operator.

[0158] 6. Generating full permutations through local search to construct new individuals avoids accepting worse solutions, thereby reducing exploration in bad areas, providing more stable improvements during the search process, and is easier to implement and tune.

[0159] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0160] Referring to Table 1 and Table 2, the specific scheme of this embodiment includes transportation time, processing operation information and calculated completion time to verify the effectiveness of the scheme proposed in this application.

[0161] Table 1 Transportation time between machines

[0162] 0 1 2 3 4 5 6 0 0 2.2 2.1 2.3 2.3 2.7 2.5 1 2.2 0 2.2 2.2 2.9 2.3 1.6 2 2.1 2.2 0 2.3 2.4 1.9 1.8 3 2.3 2.2 2.3 0 1.5 1.7 1.8 4 2.3 2.9 2.4 1.5 0 1.8 2.9 5 2.7 2.3 1.9 1.7 1.8 0 2.5 6 2.5 1.6 1.8 1.8 2.9 2.5 0

[0163] The first row and the first column of Table 1 represent the numbers of the various machines, and the remaining data in Table 1 are the transportation times between the machines.

[0164] Table 2 Processing operation information

[0165]

[0166]

[0167] The processing operation was calculated to complete in 123.0 minutes.

[0168] In addition, this embodiment can also provide the following two alternative solutions:

[0169] Alternative solution 1: Use heuristic rules to replace the scheme of updating chromosome sequences in this embodiment for job scheduling, such as first-come-first-served, shortest processing time priority rules, etc. Its advantage is that it is easy to implement, but its disadvantage is that it usually cannot guarantee the optimal solution and is difficult to adapt to complex and changing production environments.

[0170] Alternative method 2: Use the traditional evolutionary algorithm to replace the scheme of updating chromosome sequences in this embodiment for job scheduling. Its advantage is strong adaptability, but its disadvantage is that it requires more iterations to converge to the optimal solution; in the search process, it may concentrate on a certain area too early, resulting in reduced diversity of solutions and difficulty in finding the global optimal solution.

[0171] For the convenience of intuitive understanding, this embodiment also exemplarily provides a scheduling Gantt chart obtained by applying the solution of this embodiment, referring to Figure 3 , where the horizontal axis represents time, the vertical axis represents the transport machine and the processing machine, and the numbers on the blocks represent the index of the job. Figure 3 It can be seen that this embodiment more conveniently meets the capacity constraints of batch processing machines by introducing virtual jobs, reflecting the value of simplifying problems and maintaining consistency in complex manufacturing systems. On the other hand, the contradiction caused by the zero buffer on each machine is solved by introducing additional transportation operations to the public buffer.

[0172] In summary, this embodiment can effectively solve relevant scheduling problems, improve scheduling performance, optimize completion time, and better meet the actual characteristics of automated workstations, which is specifically reflected in the following aspects:

[0173] 1. The introduction of virtual jobs makes it easier to meet the capacity constraints of batch processors.

[0174] 2. Adjust the processing operation sequence to solve the contradiction between batch processing machines and transportation.

[0175] 3. Introducing additional transport operations to the common buffer solves the contradiction caused by zero buffer on each machine.

[0176] Reference Figure 4 The embodiment of the present application further provides a workstation job shop scheduling device, which can implement the above-mentioned workstation job shop scheduling method, and the device includes:

[0177] A model building unit, used to build a scheduling model of a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations;

[0178] A problem encoding unit, used for encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence;

[0179] A sequence decoding unit, used for decoding the chromosome sequence to obtain the decoded chromosome sequence;

[0180] A problem solving unit, used for updating the decoded chromosome sequence and solving the minimum value of the maximum completion time of the processing operation;

[0181] The machine scheduling unit is used to schedule the transport machines, batch processing machines and discrete machines in the workstation operation workshop according to the chromosome sequence corresponding to the maximum completion time taking the minimum value.

[0182] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0183] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above workstation job shop scheduling method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0184] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0185] See also Figure 5 , Figure 5 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0186] The processor 501 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0187] The memory 502 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 502, and the processor 501 calls and executes the workstation job shop scheduling method of the embodiment of this application;

[0188] Input / output interface 503, used to implement information input and output;

[0189] Communication interface 504, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0190] A bus 505 that transmits information between the various components of the device (e.g., the processor 501, the memory 502, the input / output interface 503, and the communication interface 504);

[0191] The processor 501 , the memory 502 , the input / output interface 503 and the communication interface 504 are connected to each other in communication within the device via a bus 505 .

[0192] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned workstation job shop scheduling method is implemented.

[0193] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0194] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0196] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0197] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0199] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0200] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0201] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0202] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0203] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0204] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0205] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A workstation job shop scheduling method, characterized in that: The method comprises the following steps: Constructing a scheduling model for a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations; Encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence; Decoding the chromosome sequence to obtain the decoded chromosome sequence; Updating the decoded chromosome sequence to solve the minimum value of the maximum completion time of the processing operation; The transport machines, batch processing machines and discrete machines in the workstation job shop are scheduled according to the chromosome sequence corresponding to when the maximum completion time reaches a minimum value.

2. The workstation job shop scheduling method according to claim 1, characterized in that: The construction of the scheduling model of the workstation job shop comprises the following steps: A scheduling model for the workstation job shop is constructed based on the constraints of each of the jobs.

3. The workstation job shop scheduling method according to claim 2, characterized in that: The scheduling model of the workstation job shop is constructed based on the constraint conditions of each job, comprising the following steps: Determining condition constraints and operation constraints of each of the operations as the constraint conditions; The conditional constraints include: each of the operations is independent of each other, all the operations are processed from the buffer at time zero, the transportation time depends on the distance between the two machines involved in the transportation and the number of transportations, and the time of the processing operation is predetermined; The operation constraints include: the start time and completion time of the transportation operation, the start time and completion time of the processing operation, the processing time of the batch processing operation, and the allocation batch and operation volume of each of the operations; The scheduling model of the workstation job shop is constructed based on the condition constraints and the operation constraints of each of the jobs.

4. The workstation job shop scheduling method according to claim 1, characterized in that: The step of encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence comprises the following steps: Constructing the scheduling problem of the workstation job shop according to the scheduling model; All the transport operations and corresponding job indexes of the transport sequence in the scheduling problem are extracted and then encoded into the chromosome sequence.

5. The workstation job shop scheduling method according to claim 4, characterized in that: Decoding the chromosome sequence to obtain the decoded chromosome sequence comprises the following steps: Decoding the chromosome sequence into a plurality of transport operations according to a reverse coding procedure; Binding the processing operation to the transport operation that is a direct predecessor of the processing operation; batching each of the operations according to adjacent arrivals; A virtual job is introduced into each batch after batching to meet the batch processing constraint, and the decoded chromosome sequence is obtained.

6. The workstation job shop scheduling method according to claim 1, characterized in that: The updating of the decoded chromosome sequence comprises the following steps: Combining a random generation method and a longest remaining time priority rule to generate a plurality of chromosome sequences as candidate populations; Decoding each of the chromosome sequences in the candidate population; Selecting a number of chromosome sequences as target populations from the chromosome sequences obtained after decoding the candidate populations by using a binary tournament method and an elite strategy; Determining whether the target population meets the conditions for resetting the strategy; If the target population meets the conditions of the reset strategy, the target population is reset first, and then a crossover operation, a mutation operation and a local search are performed on the chromosome sequence in the target population to update the chromosome sequence in the target population; If the target population does not meet the conditions of the reset strategy, performing a crossover operation, a mutation operation and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population; Determine whether a termination condition is met. If the termination condition is not met, return to the step of decoding each chromosome sequence in the candidate population; if the termination condition is met, determine the chromosome sequence in the current target population as the final chromosome sequence.

7. The workstation job shop scheduling method according to claim 6, characterized in that: The performing a crossover operation, a mutation operation, and a local search on the chromosome sequence in the target population to update the chromosome sequence in the target population comprises the following steps: In the crossover operation, a single-point crossover operator or a two-point crossover operator is selected in the chromosome sequence of the target population with a first probability, and an improved exchange crossover operator is selected in the chromosome sequence of the target population with a second probability, and then the chromosome sequence of the target population is updated according to the single-point crossover operator, the two-point crossover operator or the improved exchange crossover operator; wherein the second probability is obtained by subtracting the first probability from 1; In the mutation operation, one of a swap mutation operator, a shift mutation operator, a substring shift mutation operator or a reversal mutation operator is randomly selected, and then the chromosome sequence of the target population is updated according to the selected mutation operator; In the local search, three positions in the chromosome sequence of the target population are randomly selected to generate a full permutation to construct a new chromosome sequence; if the maximum completion time of the chromosome sequence generated by the local search is less than the original chromosome sequence, the original chromosome sequence is replaced; otherwise, the original chromosome sequence is kept unchanged.

8. Workstation job shop scheduling device, characterized in that: The device comprises: A model building unit, used to build a scheduling model of a workstation job shop; wherein the scheduling model includes transportation operations and processing operations of different jobs, and the processing operations include batch processing operations and discrete processing operations; A problem encoding unit, used for encoding the scheduling problem of the workstation job shop according to the scheduling model to obtain a chromosome sequence; A sequence decoding unit, used for decoding the chromosome sequence to obtain the decoded chromosome sequence; A problem solving unit, used for updating the decoded chromosome sequence and solving the minimum value of the maximum completion time of the processing operation; The machine scheduling unit is used to schedule the transport machines, batch processing machines and discrete machines in the workstation operation workshop according to the chromosome sequence corresponding to the maximum completion time taking the minimum value.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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