100 results about "Job shop scheduling problem" patented technology

The invention discloses a flexible job-shop scheduling multi-objective method. The specific implementation procedures of the flexible job-shop scheduling multi-objective method are that: establishing a model for a multi-objective flexible job shop; optimizing the model by using an ant colony algorithm, and evaluating the result to check whether the scheme is the optimal scheme; and improving a pheromone updating rule according to the optimal scheme, and increasing convergence rate of the algorithm to obtain a Pareto optimal solution of a multi-objective flexible job shop scheduling problem. Compared with the prior art, the flexible job-shop scheduling multi-objective method effectively reduces workshop production cost, shortens the processing time, improves qualified rate of products, is high in practicability, and is easy to popularize.

The invention discloses an equipment preventive maintenance and flexible job shop scheduling integrated optimization method. The method is characterized by specifically comprising: first of all, according to an operation sequence, an equipment maintenance period, and a constraint condition of uninterrupted production process of each operation and non-conflict production process and equipment maintenance process of a flexible job shop scheduling problem, establishing an integrated optimization model of a flexible job shop scheduling and equipment maintenance plan, which takes cost and efficiency into consideration; secondary, optimizing multiple targets by use of a hybrid multi-objective chemical-reaction optimization method, the multiple targets comprising maximum completion time, total production cost and total equipment preventive maintenance cost; and finally, obtaining an optimization solving result, such that a flexible job shop scheduling plan can be obtained. The method can realize the goals of reducing the maximum completion time, the production cost and the equipment maintenance cost and can obtain an optimal flexible job shop scheduling scheme.

The invention discloses a method for solving flexible job shop scheduling based on an improved whale algorithm. The method comprises the following steps: 1) establishing a mathematical model of a flexible job shop scheduling problem; 2) setting algorithm parameters and generating an initial population; 3) obtaining a current optimal scheduling solution; 4) judging whether the current number of iterations is greater than the maximum number of iterations; if yes, outputting a scheduling solution; if not, judging whether the counter value of the current optimal individual is not smaller than a preset value or not; if yes, carrying out variable neighborhood search operation, and updating a scheduling solution; if not, converting the scheduling solution into a whale individual position vector,and retaining the whale individual corresponding to the scheduling solution; and 5) updating whale individual position information by adopting an improved whale algorithm, converting the whale individual position vector into a scheduling solution to complete population updating, adding 1 to the number of iterations, and returning to the step 3). According to the method disclosed by the invention,all optimal solutions of flexible job shop scheduling can be well solved, and the solving speed and precision are improved.

The invention provides a multi-objective flexible job shop scheduling method based on a discrete firefly algorithm. The method comprises the steps that a mathematical model is established for a multi-objective flexible job shop scheduling problem; a segment coding method is used to code a firefly, and a machine selection part and a process sorting part are divided; the discrete firefly algorithm is used to optimize the model to acquire a Pareto optimal solution set; and a solution corresponding to the actual need is selected from the Pareto optimal solution set, and decoding is carried out to output machine selection position information and process sorting position information. Compared with the existing method, the multi-objective flexible job shop scheduling problem optimizing method has the advantages that the global optimization ability of the algorithm is improved; the overall processing time is shortened; the job shop production cost is reduced; and the method meets actual production needs.

The invention relates to the technical field of job shop scheduling, in particular to an improved culture gene algorithm for solving a multi-objective flexible job shop scheduling problem. The algorithm comprises the following steps of performing process-based encoding; generating an initialized population; performing local search by a hill-climbing method; calculating fitness; judging whether an optimization criterion is met or not (if yes, generating an optimal individual and ending the algorithm, otherwise, executing the next step); performing selection; performing SPX crossover; performing mutation; performing local search by the hill-climbing method; generating a new-generation population; calculating fitness; and circulating the process. The algorithm is improved as follows: the local search is performed by utilizing the hill-climbing method, so that local optimum can be escaped for obtaining a better solution, and the calculation time can be shortened; and in addition, the crossover and mutation modes of the algorithm are improved, the SPX crossover method is adopted, and one of two methods of insertion mutation and replacement mutation is randomly selected for mutating individuals in the population by an equal probability Pm during mutation.

The invention belongs to the crossing field of a computer application technology and production manufacturing. A natural computing technology is used to optimize a multi-target flexible job shop scheduling problem. A problem that a cooperative hybrid artificial fish swarm algorithm is used to solve multi-target flexible job shop scheduling is provided. The method is characterized in that a foraging behavior with a distribution estimation attribute and an artificial fish attraction behavior are designed to improve an artificial fish swarm model; a cooperation idea is introduced into the model; through multiple population cooperation of the fish swarm, global searching is performed and is cooperated with a simulation annealing algorithm so as to enhance an algorithm local searching capability; aiming at a multi-target problem, an improved epsilon-Pareto dominant strategy is designed to evaluate an individual applicable degree value. The method in the invention has the following advantages that problems of slow later-period convergence, a poor local optimizing ability and the like, which exist in the artificial fish swarm algorithm during a searching process, can be overcome; through cooperative optimization, a pareto solution set with good quality and dispersibility is obtained.

The invention relates to a flexible job-shop scheduling optimization method, which applies the Metropolis criterion and the sinusoidal adaptive step length to a firefly algorithm so as to optimize andsolve a discrete problem. On the basis of building a mathematical model, an initial solution population of a discrete combination problem is randomly generated, then local search in an individual domain is performed according to the Metropolis criterion in simulated annealing to generate a new individual, the internal energy difference between the new individual and the original individual is calculated, the new individual is accepted according to a certain probability, and global search is performed on each generation by using the discrete firefly algorithm with the sinusoidal adaptive steplength until an optimal solution is searched. The method can better search an optimal solution of the FJSP (Flexible Job-Shop Scheduling Problem) in the global space and has better search precision, search efficiency and stability, thereby having important significance and significant engineering practical application values for solving discrete problems such as job-shop scheduling.

The invention discloses a method for solving flexible job shop scheduling based on a hybrid whale swarm algorithm, and the method comprises the steps: firstly defining the coding mode of flexible jobshop scheduling as two-stage random key coding, and then carrying out the mapping conversion through employing a conversion mechanism; Defining the shortest total processing time for solving the fitness function as an optimization target; Secondly, initializing parameters and whale population in the flexible job shop scheduling problem by adopting a whale group algorithm, wherein initialization isdivided into a sorting scheme of a random generation process and a genetic variation mode of an improved genetic algorithm to generate a better machine distribution scheme corresponding to the sorting scheme of the process, and then a better initial population is generated; Calculating the fitness value of each scheduling scheme, and finding and reserving the best scheduling solution; And finally, outputting the optimal scheduling solution and the corresponding fitness function value to obtain a solved optimal scheduling scheme, and solving the problems of low solution precision and low convergence rate in the existing flexible job shop scheduling problem.

The invention presents a job shop scheduling method based on an improved fruit fly optimization algorithm. The method comprises the following steps: establishing a mathematical model of a job shop according to the characteristics of the job shop, and constructing the constraint conditions for the processing order of different working procedures of each work piece and the constraint conditions forthe processing order of the working procedures of different work pieces on each machine; and establishing a job shop scheduling objective function based on minimum maximum completion time, forming individual fruit flies through a coding method based on working procedures, enabling the fruit fly colony to quickly find the minimum value of a taste concentration determination function through a classification olfactory random search method based on adaptive step size, and obtaining an optimal solution of job shop scheduling, namely, an optimal scheme of job shop scheduling. The algorithm is simple to implement, and requires only two parameters. Moreover, the algorithm has strong global optimization ability, and can be used to solve the job shop scheduling problem.

The invention relates to a control method for solving a flexible job shop scheduling problem based on a genetic algorithm, which is divided into six parts such as encoding and decoding, initial population generation, crossover, mutation, fitness calculation and selection. The control method is characterized in that a segmented encoding method is adopted, chromosome encoding is divided into a machine selection part and a procedure selection part, and chromosomes are decoded according to a certain mode so as to acquire corresponding manufacturing procedures and corresponding manufacturing machines; an initial population is generated by adopting a mode of combining various search modes; and fitness calculation aims to solve a problem of how to solve the execution time of certain legal scheduling and judge the quality of the scheduling. The control method provided by the invention not only has great advantages in solving quality, but also has the same excellent performance in improving the solving speed and processing a large-scale flexible job shop scheduling problem.

The invention provides a self-crossover genetic algorithm for solving a flexible job-shop scheduling problem. The algorithm relates to the field of job-shop scheduling, and particularly relates to the field of flexible job-shop scheduling. The existing genetic algorithms are mostly amphilepsis, the coding mode is complex, crossover and variation are caused to be complex, and a non-feasible solution is easy to acquire. The invention provides monolepsis-based self-crossover whose coding, crossover and variation are performed on a uniparental chromosome. The coding uniparental chromosome is divided into a working procedure portion and an equipment portion, wherein the working procedure portion is coded based on the workpiece number, and the equipment portion represents selected equipment by using the probability. Self-crossover is performed on the working procedure portion, and the equipment portion also performs the same crossover transform along with the working procedure portion. Two types of variation operators are adopted, exchange type variation is adopted for the working procedure portion, and insertion type variation is adopted for the equipment portion. The self-crossover genetic algorithm provided by the invention has the characteristics of high practicability and wide application range.

The invention provides a genetic algorithm by employing guided local search for a multi-objective optimization problem. The algorithm is used for the field of flexible job-shop scheduling. A flexible job-shop scheduling problem belongs to an NP-Hard problem, optimization of multiple objectives often needs to be faced in real production, and the objectives are in interaction and conflict. The genetic algorithm aims at solving the problems of too fast convergence, insufficient population diversity and over-high calculation cost of enumerating all neighborhood solutions caused by continuous cross breeding of close relatives in a genetic operation in the genetic algorithm in the prior art. According to the algorithm, a procedure of calculating a crossover rate and a mutation rate before genetic crossover and mutation and a procedure of searching a movable process and a feasible position by using the guided local search are designed for these problems; and through introduction of the two procedures, the calculation cost is reduced while algorithm premature is avoided. The algorithm is high in practicability and can be well used for actual job-shop scheduling.

The invention provides a hybrid particle swarm tabu search algorithm for solving a job-shop scheduling problem. Compared with other meta-heuristic algorithms, the algorithm has the characteristic of ''elite memory'' according to a PSO and has the characteristic of fast convergence, the PSO is taken as an initial solution source of TSAB tabu search, and an encoding and decoding mechanism for mapping a particle swarm continuous solution space into a discrete space of the job-shop scheduling problem is designed. A real number solution of the PSO is converted into an integer solution of the tabu search algorithm through a real integer encoding method and the integer solution of the tabu search algorithm is converted into the real number solution of the PSO through a real integer decoding method after one-time iteration; and a chance of accurate search is made in a potential space to own more exploration in a global search space. An improved PSO with a balancing strategy is provided, and a balance operator beta is introduced. The performance of the algorithm is greatly strengthened through these improvements and the actual job-shop scheduling condition is combined. The algorithm is high in practicability and good in usability.

The invention provides a genetic algorithm using an improved coding method to solve a distributed flexible job shop scheduling problem. The algorithm is suitable for the field of flexible job shop scheduling. The distributed flexible job shop scheduling problem refers to production activities carried out in several factories and manufacturing units, contains the information of flexible job shop scheduling problems, and contains the selection of suitable factories and flexible manufacturing units. The allocation of specific workpieces to different factories produces different production scheduling, which affects a supply chain. The existing technology has little research on the problem. The invention proposes the genetic algorithm to solve the problem. A traditional genetic algorithm is easy to fall into local optimum and leads to premature convergence, and the coding method produces infeasible solution and other problems. According to the invention, the improved coding method based on probability is provided; the crossover of a tabu table is introduced; the problems are avoided based on the variation of neighborhood search; and the genetic algorithm takes into account the actual production situation, and has the characteristics of high practicability and the like.

The invention provides an operation workshop scheduling key outside cooperation process identification method based on relation chains. The method comprises the steps of first obtaining initial scheduling according to the scheduling problem; then identifying candidate key outside cooperation processes according to processing rules of the relation chains; then re-scheduling the processes influenced after outside cooperation; and finally performing multi-attribute decision making according to the conditions of the production site, and outputting an optimal key outside cooperation process and a corresponding production scheduling scheme. By applying the method, a process set restraining effective throughput of the whole scheduling scheme can be found out rapidly in a targeted mode, and blind and passive searching for the outside cooperation processes is avoided. To solve the scheduling problem for an operation workshop with m machines and n workpieces, tests need doing for m*n times in an exhaustive method. According to the operation workshop scheduling key outside cooperation process identification method, the minimum transfer times is m, the maximum transfer times is m*n-1. Through the tests done for 100 times, the obtained average transfer times is about (m*n-1) / 3.

The invention discloses a flexible job-shop dynamic scheduling method based on variable weight scheduling range, and the method belongs to the technical field of workshop scheduling field and comprises the following steps: 1) using the improved genetic algorithm to initialize the data for a static flexible job shop scheduling scheme and implementing the scheme; 2) determining the type of dynamic events when dynamic events occur; and 3) implementing the dynamic scheduling method for the variable rescheduling range to regenerate a new scheduling scheme, and implementing the dynamic scheduling scheme. After the dynamic events occur, only the workpieces within the range are rescheduled, and the size of the range is determined by the workpieces directly influenced by the dynamic events. According to the invention, through the establishment of a flexible job-shop scheduling model featuring the goal of minimizing the work completion time, a cluster initialization and selection method and a dynamic scheduling strategy based on variable weight scheduling range are proposed, which solves the scheduling problem of a dynamic flexible job-shop and increases the quality of weight scheduling solution.

The invention discloses a method for solving the flexible job-shop scheduling problem with an improved GA based on a polychromatic set hierarchical structure. According to the method, an original process-machine tool contour matrix is split into matrixes of relation of process-benchmark, benchmark-equipment model, equipment model-asset number by establishing equipment benchmarks and setting process constraint, equipment constraint, machine tool constraint and unique constraint, and the data size of a constraint model is effectively reduced; furthermore, by optimizing chromosome lengths reasonably and setting blending operation of batch benchmarks, the time and space complexity of chromosomes is effectively reduced, and then the solving speed and practicality of the algorithm can be greatly improved.

The invention provides a hybrid genetic algorithm for solving a multi-objective flexible job-shop scheduling problem. The algorithm takes three objectives of the completion time, the production cost and the equipment utilization rate in flexible job-shop scheduling into consideration, and aims at the circumstances that algorithms at present are difficult in decoding and long in computation time because of possession of a complicated coding method and that the genetic algorithm is faced with problems of global near optimum and insufficient search capability in local search. In allusion to the problems, the invention provides a new matrix chromosome coding method which almost does not need to perform decoding. In addition, the algorithm provided by the invention combines the global search capability and local search of the genetic algorithm, the search capability of the algorithm is enhanced, and a feasible solution is found more easily. The hybrid algorithm is high in practicability, and can be well applied to flexible job-shop scheduling.

The invention provides a flexible job shop scheduling method and system. The method comprises steps that S1, the initial population S is generated based on basic parameters of a flexible job shop scheduling problem FJSP, and the initial population S is taken as a parent population P; S2, the parent population P is selected, crossed and mutated to obtain a temporary progeny population T; S3, basedon the temporary progeny population T, the parent population P is subjected to niche pre-selection operation to obtain a progeny population C; S4, a fitness value of each individual in the progeny population C is calculated, and the individual with the highest fitness value in the progeny population C is taken as the optimal solution of the FJSP; and S5, based on the optimal solution, a job shop corresponding to the FJSP is scheduled. The method is advantaged in that the solution search space can be made to maintain population diversity, the obtained solution can be guaranteed to converge to the global optimum, and thereby the job shop scheduling effect can be improved.

The invention proposes an improved cuckoo search algorithm for solving a scheduling problem of a workshop, and the algorithm makes the following improvements on the problems that a conventional cuckoo algorithm is slow in search speed, is low in calculation precision and is not strong in search activity: 1, arranging initial positions of nests according to a rising trend, so the algorithm is simple and ordered and reduces the iteration search time; 2, solving JSP through a coding rule based on a working procedure, and attributing workpiece parameters, so the algorithm is simple and clear, is high in practicality and also improves the search capability; 3, solving a cuckoo search step through a method based on a mean value, thereby reducing the search time of the algorithm, and improving the precision of the algorithm in solving the JSP.

The invention discloses a flexible job shop scheduling method based on an improved genetic algorithm. The method can solve the problem that an existing genetic algorithm is insufficient in usability in a discrete flexible job shop scheduling problem. The traditional genetic algorithm has a good global search capability, but the local search capability is insufficient, the premature convergence iseasy, the optimal solution set is difficult to find, the Powell search has a strong local search capability, but the defect that the Powell search is liable to be trapped in the local optimization exists. According to the genetic algorithm scheme combined with the Powell search method, the excellent global search capability of the genetic algorithm can be fully utilized, meanwhile, the local search capability of the whole algorithm is enhanced through the Powell search method, early maturing of the algorithm is avoided, and the quality of a scheduling scheme is improved. In consideration of the particularity of a chromosome coding scheme of a flexible job shop scheduling genetic algorithm, a traditional Powell search method is improved to avoid generation of an infeasible solution, so thatthe robustness and the search efficiency of the algorithm are improved.

The invention provides a single-piece job-shop scheduling method based on multi-Agent deep reinforcement learning, aiming at the characteristics that the single-piece job-shop scheduling problem is complex in constraint and various in solution space types, and the traditional mathematical programming algorithm and meta-heuristic algorithm cannot meet the quick solution of the large-scale job-shopscheduling problem. The method comprises the following steps: firstly, designing a communication mechanism among multiple Agents, and carrying out reinforcement learning modeling on a single job shopscheduling problem by adopting a multi-Agent method; secondly, constructing a deep neural network to extract a workshop state, and designing an operation workshop action selection mechanism on the basis of the deep neural network to realize interaction between a workshop processing workpiece and a workshop environment; thirdly, designing a reward function to evaluate the whole scheduling decision,and updating the scheduling decision by using a PolicyGraphic algorithm to obtain a more excellent scheduling result; and finally, performing performance evaluation and verification on the algorithmperformance by using the standard data set. The job shop scheduling problem can be solved, and the method system of the job shop scheduling problem is enriched.

The invention provides a hybrid fruit fly algorithm based on double-objective job shop scheduling. The method comprises the following steps: a mathematical model is built; the constraint conditions for the processing order of different working procedures of each work piece are built; the constraint conditions for the processing order of the working procedures of different work pieces on each machine are built; an objective function is built; fruit fly individual and fruit fly population initialization are carried out; a new fruit fly population is obtained, and a global collaboration mechanismis carried out; the new obtained fruit fly population is evaluated and iterative optimization is carried out; and if termination conditions are met, non-inferior sets obtained through multiple timesof operation are combined and screened to obtain a pareto solution set. Only two parameters need to be set, the algorithm is simple to realize, the complexity of job shop scheduling is reduced, and the job shop scheduling efficiency is enhanced; besides, the global optimization ability is strong, and the job shop scheduling problem can be effectively solved; and the hybrid fruit fly algorithm based on double-objective job shop scheduling has the advantages of few set parameters, strong convergence and strong robustness and the like.

The invention discloses a method for solving dynamic workshop scheduling based on an improved genetic algorithm of a polychromatic set. By adopting the method, a method for solving the dynamic workshop operation scheduling by combining a genetic algorithm and a polychromatic set theory is realized. The improved algorithm combining the genetic algorithm and the polychromatic set theory is applied to the dynamic flexible workshop operation scheduling in order to provide an appropriate algorithm for the dynamic flexible operation workshop scheduling problem, so that the operation time is shortest, the dynamic re-scheduling problem in two cases such as the damage of machine tool equipment and insertion of an emergency key can be solved, the machining time and the machining cost can be reduced,and the dynamic change of a workshop scheduling environment can be handled; and according to the improved genetic algorithm, the dynamic re-scheduling can be carried out in the case of changing a contour matrix and not changing a scheduler program, the solving speed is high, the solving precision is high.

The invention discloses an immune clone selection job shop scheduling method based on scheduling coding, mainly aiming at overcoming the disadvantages that the quality is poor and the efficiency is low in the prior art when a job shop scheduling problem is solved. The method comprises the following steps: operating an input machine, operation and a constraint condition by utilizing a GT (Guo Tao) algorithm to generate a scheduling matrix; carrying out direct coding to the scheduling matrix to be used as antibody population; calculating the affinity of the antibody population and dividing the antibody population into a memory unit and a free unit; calculating the clone scale of each antibody; carrying out clone variation to the antibody population by using a clone operator based on neighborhood search according to the clone scale to obtain the clone population; carrying out clone selection to the clone population to obtain new antibody population, and carrying out updating and death to the memory unit and free unit; outputting the optimal antibody in the antibody population and mapping the optimal antibody as the scheduling sequence of the machine and the operation. The immune clone selection job shop scheduling method has the advantages of good quality and high efficiency, and can be used for solving the problem of job shop scheduling.

The invention provides an improved cuckoo search algorithm for solving a job-shop scheduling problem. A calculation way of a foreign bird egg in the cuckoo search algorithm is determined, so that the algorithm is accurate and efficient, and the algorithm is more applicable to an actual production environment; on the aspect of an application of the cuckoo search algorithm in solving the job-shop scheduling problem, a calculation way of calculating an initial position of a bird nest is determined, so that the algorithm result is more accurate, and meanwhile, the calculation method is simple, convenient and efficient; and in addition, one of three assumed conditions of the cuckoo search algorithm is broken through: the number of bird nests is unchanged. On the basis of considering a condition of increasing number of the bird nests, a condition of decreasing of the number of the bird nests is also taken into consideration, so that the algorithm is wider in application range; and a calculation method for estimating order completion time is determined.

The invention relates to a fuzzy job-shop scheduling method based on self-adaption inheritance and the clonal selection algorithm. The method includes the steps of determining the coding scheme of the fuzzy job-shop scheduling problem, generating the initial population N at random, defining a solution space, defining and calculating the adaptability function of an individual, conducting clone proliferation operation on the individual according to the size of the adaptability value near each generation of optimal solution, independently conducting self-adaption cross and mutation operation on individuals obtained through reproduction, conducting clonal selection operation on the individuals obtained through cross and mutation to generate the new population N*, ending the circulation if the ending condition is met, and returning to continue the next step if the ending condition is not met. By means of the method, resources can be more reasonably distributed, the completion time of job-shop scheduling fuzziness is shortened, the efficiency of job-shop fuzzy scheduling is improved, and the requirement of actual production scheduling is better met.

The invention provides a layered optimization algorithm for solving multi-technical-route workshop scheduling, and relates to the field of multi-technical-route workshop scheduling. According to characteristics of multi-technical-route workshop scheduling, a layered optimization model based on a target cascade method is provided, problems of multi-technical-route workshop scheduling are divided into a technical plan layer, a unit plan layer and a workpiece scheduling layer according to the model, the technical plan layer selects an optimal technical route for all workpieces, the unit plan layer clusters manufacturing units for all machines, and the technical scheduling layer schedules workpieces in all the manufacturing units. Via coordinated optimization of different layers and clustering of the manufacturing units, an improved genetic algorithm is combined to search for a globally optimal solution, and the layered optimization algorithm can realize coordinated scheduling among different units of large workshops, and is characterized by flexible technical routes, low complexity and high efficiency.

The invention discloses a flexible factory work scheduling method based on MapReduce parallelization in a cloud computing environment. The method comprises the following steps of: receiving a remotely submitted flexible work shop scheduling problem, allocating computing resource according to a computing task and task requirements through a cloud computing elastic mode, wherein the flexible work shop scheduling problem comprises the computing task, and the task requirements of computing time and computing precision for the computing task; according to the computing resource allocated in the first step, modeling for the flexible work shop scheduling problem submitted by a user and coding the computing task, then, solving with a MapReduce parallelization genetic algorithm, and finally providing a scheduling result. In the method provided by the invention, a MapReduce model is used, thus, requirements of the user on time and precision can be satisfied, algorithm solving time can be reduced effectively, and solution quality can be improved.

The invention discloses a scheduling method based on a discrete invasive weed algorithm, and the algorithm comprises the steps: giving an initial solution of a random number to all weeds, and carryingout the coding and decoding of an initial random sequence in a mode of procedure coding; in each iteration process, enabling each weed to calculate the number of seeds to be generated by the weed; inthe spatial diffusion operation of the algorithm, enabling the seeds to generate a standard deviation value for controlling the diffusion distance according to the fitness value of the parent plant,and enabling the seeds to be randomly diffused around the parent in a normal distribution mode according to the standard deviation value; carrying out local optimization on newly generated seeds, using a VNS thought to change a sequence of a workpiece, and improving the local search capability of the algorithm; and when the total number of the weeds reaches a set maximum value, removing poorer individuals by an algorithm, and only retaining the maximum number of the weeds. The invention provides a scheduling method based on an advanced novel evolutionary algorithm, and the scheduling method can more intelligently solve the job shop scheduling problem in the manufacturing industry.