34 results about "Machine scheduling" patented technology

The invention belongs to the field of workshop scheduling, and particularly discloses a hybrid flow workshop dynamic scheduling method and system considering machine predictive maintenance, and the method comprises the steps: taking the minimization of total completion time, maintenance cost and processing cost as the target, building a hybrid flow workshop scheduling problem as a multi-target joint optimization model, and carrying out the optimization of the multi-target joint optimization model; setting intelligent agents with the same number as the processing stages, and constructing a Markov decision process; each agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on a Markov decision process, training an intelligent agent, maintaining a workshop at an operation and maintenance point, and respectively calling a workpiece scheduling network and a machine scheduling network at a scheduling point to select workpieces and machines; and after training is completed, workshop dynamic scheduling is realized based on the trained intelligent agent. According to the method, the problem of hybrid flow shop dynamic scheduling considering machine predictive maintenance is effectively solved through workshop scheduling integrating machine operation and maintenance, and the method has good dynamics and adaptivity.

The invention provides a heterogeneous multi-agent autonomous collaborative optimization method for production and maintenance integrated scheduling of a mixed flow manufacturing workshop. The heterogeneous multi-agent autonomous collaborative optimization method comprises the steps of limited buffer region constraint, feature screening, heterogeneous multi-agent and the like. The method is optimized on the basis of a TD3 algorithm, and comprises the following steps: firstly, constructing a heterogeneous multi-agent according to the characteristics of each processing site; secondly, in order to improve effective characterization of feature extraction, a feature screening mechanism based on NSGAIII is designed; and finally, in combination with the actual scheduling condition of the mixed-flow manufacturing workshop and aiming at the actual situation that the performance of the scheduling rule of the first-selected workpiece and the second-selected machine is poor, a composite scheduling rule based on the first-selected machine and the second-selected workpiece is adopted. According to the method provided by the invention, the effectiveness and superiority of the NSGAII-MATD3 algorithm are respectively demonstrated by comparing a traditional single scheduling rule, a feature-free screening MATD3 algorithm and five advanced deep reinforcement learning algorithms.

The invention discloses a green robust independent parallel locomotive scheduling method with uncertain processing time. The method comprises the following steps: acquiring a to-be-scheduled parameter set; constructing an irrelevant parallel machine scheduling model taking worst scene completion time WC and scene average energy consumption MTEC as double targets based on the parameters; a scene-driven double-population discrete artificial bee colony algorithm is adopted for solving, and the method comprises the steps of population initialization, employed bee global search based on ternary championics and two-point crossing, division into two sub-populations according to MTEC, MN local search based on a mean value scene, WN local search based on a worst scene, LN observation bee self-adaptive neighborhood search based on Q-learning and scout bee disturbance. And finally, outputting a robust scheduling solution set with both robustness and low-carbon property according to a Pareto criterion. And a plurality of scheduling schemes considering robustness and energy consumption optimization are provided for decision makers.

The invention discloses an aviation composite material laying workshop man-machine scheduling method and system considering a collaborative learning effect, and the method comprises the steps: taking the minimum maximum completion time, the minimum maximum team task load, and the maximum worker proficiency degree improvement total amount as objective functions; a dual-resource constraint flexible job shop scheduling model considering a worker collaborative learning effect is constructed, and the scheduling model is solved through a multi-target whale optimization algorithm driven by domain knowledge; according to the method, the individual learning effect and the collaborative learning effect of workers are considered, a corresponding skill proficiency improvement model is constructed, and an optimal scheduling scheme is solved by designing a hybrid initialization method, a neighborhood search operator, an adaptive leader whale division strategy and an adaptive similarity threshold strategy and predation and search operators based on domain knowledge. And a scheduling scheme of worker and equipment resources can be generated more accurately, so that the processing efficiency is improved, and a resource allocation mechanism is optimized.

The invention discloses an intelligent production scheduling method for a copper coil pipe coiling and drawing process. The intelligent production scheduling method comprises the following steps: step 1, converting a monthly order into a daily production order; 2, translating order specifications to obtain an order set; and step 3, scheduling production by using an intelligent algorithm, carrying out frame material division, batch distribution and machine scheduling, and arranging dynamic shutdown to reduce the power cost. According to the intelligent production scheduling method for the copper coil pipe coil drawing process, frame material process similarity clustering and multi-target optimization scheduling are introduced, automatic production scheduling of the copper coil pipe coil drawing process is achieved on the basis of order data, process parameters and equipment information, and therefore the production efficiency of the copper coil pipe coil drawing process is improved while it is guaranteed that an order production task is completed. The working efficiency of the disc drawing equipment is improved, and energy conservation and cost reduction are achieved while production scheduling is optimized.

The invention provides an uncorrelated parallel machine scheduling method, system and device, a medium and a product. The method comprises the steps of obtaining a main problem and a sub-problem corresponding to a scheduling task; obtaining a binary representation model of the main question; the scheduling quantum computer analyzes the binary representation model of the main problem to obtain a plurality of optional scheduling strategies; analyzing the sub-problem based on the plurality of selectable scheduling strategies to obtain a plurality of candidate sub-problem feedback information; screening the plurality of candidate sub-problem feedback information to obtain target sub-problem feedback information, and performing a next iterative analysis process based on the target sub-problem feedback information to obtain a plurality of new selectable scheduling strategies; and obtaining a target scheduling strategy until an iteration ending condition is met. Through the quantum and classical hybrid computing system, the quality and generation efficiency of the scheduling strategy are improved.

This invention discloses a flexible scheduling method, system, and application for UPMS (Upright Manufacturing System) workshops based on an improved differential evolution algorithm. The method includes: establishing an independent parallel machine scheduling model with sequentially dependent mold-changing time as the objective of minimizing the maximum completion time; using real-valued vector encoding and decoding it into a scheduling scheme through the maximum position value rule; performing mutation and crossover operations of the differential evolution algorithm on the generated test vectors, with optimizations including load balancing optimization based on destruction and reconstruction and mold-changing optimization based on sequential smoothing; finally, updating the population and outputting the optimal scheduling scheme. This invention significantly reduces sequentially dependent mold-changing time, achieves dual optimization of load balancing and mold-changing cost, and improves solution accuracy and efficiency, making it particularly suitable for discrete manufacturing workshops such as textile printing and dyeing, and injection molding.

The invention provides a semiconductor manufacturing equipment scheduling method and device, electronic equipment and a storage medium, and relates to the technical field of semiconductors. The method comprises the following steps: acquiring a machine scheduling request; determining a next process and a plurality of available candidate machines meeting the requirements of the next process according to the wafer batch identifier and the current process identifier; according to the machine identifier of the current process, multi-dimensional scheduling information of each available candidate machine is obtained, and the multi-dimensional scheduling information at least comprises distance information, state information and load information; and according to the multi-dimensional scheduling information of each candidate machine, determining a target machine in the plurality of available candidate machines, and executing the next process through the target machine. According to the method, the scheduling score of each candidate machine is comprehensively evaluated by fusing three key dimensions including distance, state and load, accurate modeling and quick response to a complex dynamic environment in a semiconductor production line are realized, the production cycle is remarkably shortened, and the equipment utilization rate is improved.

The application belongs to the technical field of intelligent manufacturing and production scheduling optimization, and particularly relates to a buffer workpiece group batch transfer and order allocation collaborative optimization method and system. The method firstly performs real-time group batch clustering on the to-be-transferred workpieces in the buffer based on attribute correlation to generate a transfer batch, and issues a transfer instruction under a double-threshold control pulse trigger mechanism. Then, a deep reinforcement learning model is constructed. At each pulse trigger time, a global state vector is constructed based on the transfer batches of all issued transfer instructions and the order types in the system, an order allocation decision is output based on the global state vector, and finally a batch-transfer equipment-path combination instruction is generated based on the order allocation decision. The application realizes global optimization of production efficiency and energy efficiency through collaborative optimization of machining scheduling and material transfer scheduling and deep integration of dynamic response of orders.

The application provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling.The method comprises the following steps: encoding and analyzing workpieces and machines based on machine indexes; in the case of parallel machine scheduling, initializing algorithm parameters based on the hybrid particle swarm algorithm, initializing a population by a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimization of the maximum completion time as an objective, executing a variable neighborhood descent local search strategy, and updating the local optimum and the global optimum; and in the case that an iteration index exceeds an iteration threshold, determining the end of the algorithm and outputting a target result to represent the allocation and scheduling information of the workpieces and machines.The application considers fuzzy processing time, learning effect and deterioration effect, and considers the scheduling problems of single machines and non-single machines in a fuzzy environment based on actual production conditions, which is helpful for iron and steel plants to formulate production strategies and reasonably arrange the allocation and scheduling of workpieces and machines.

The invention provides a serial batch processing scheduling method based on a hybrid particle swarm algorithm in a fuzzy environment, and relates to the field of production scheduling, and the method comprises the steps: carrying out the coding analysis of a workpiece and a machine based on a machine index; under the condition that the scheduling type is parallel machine scheduling, based on a hybrid particle swarm algorithm, initializing algorithm parameters, initializing a population through a heuristic algorithm, analyzing fitness, and updating the speed and position of particles; determining the minimum maximum completion time as a target, executing a variable neighborhood descent local search strategy, and updating local optimum and global optimum; and under the condition that the iteration index exceeds an iteration threshold value, determining that the algorithm is ended, and outputting a target result to represent distribution scheduling information of the workpiece and the machine. According to the method, fuzzy processing time, a learning effect and a deterioration effect are considered, single-machine and non-single-machine scheduling problems in a fuzzy environment are considered based on actual production conditions, and an iron and steel plant is helped to formulate a production strategy and reasonably arrange distribution scheduling of workpieces and machines.

The invention provides an additive manufacturing SLM single-machine scheduling method considering part nesting. The method comprises the steps that production data are acquired; constructing an integrated scheduling optimization model: taking predefined nested combination adopted by the parts, construction direction selection of the parts, operations formed by grouping the parts and sorting of all the operations as decision variables of collaborative optimization, and aiming at minimizing the unit volume production cost of all the operation parts, constructing the integrated scheduling optimization model; the constraints that the total projection area does not exceed the area of the processing platform and the completion time does not exceed the delivery time are met; solving the integrated scheduling optimization model by adopting an improved sparrow search algorithm, namely coding a processing sequence of associated parts through three reforms, constructing direction selection and operation grouping, measuring the distance between individuals in an algorithm population by adopting a Hamming distance, dynamically adjusting step length control parameters, and keeping the influence of the optimal solution of the current population on the next generation; the output scheduling scheme comprises the construction direction of each part, the work to which the part belongs, the adopted nested combination and the machining sequence of all the work.

The invention belongs to the technical field of computer software testing, and particularly relates to a Linux kernel vulnerability mining method based on diversity guidance. According to the method, firstly, collected PoCs are expressed by using a customized abstract syntax tree, clustering is carried out on the PoCs based on a Louvain community discovery algorithm, an initial diversity seed bank is constructed, and seeds are divided into a plurality of communities with different functions; in order to quantify seed diversity, a community prevalence rate index (CPR) is introduced; designing a double-layer multi-arm tiger machine scheduling framework based on the CPR, wherein the framework is used for efficiently allocating variable resources between communities and in the communities; a CPR-guided seed variation strategy is adopted to preferentially carry out rapid variation and expansion on high-diversity seeds, so that the coverage speed and efficiency of vulnerability triggering are improved. Experimental results show that compared with a current most advanced kernel fuzzy test tool, the method has the advantages that the code coverage rate is averagely increased by 17.4%, and the vulnerability discovery number is averagely increased by 9.1 times.

The present application relates to the technical field of wool textile machine table scheduling, and particularly relates to a production scheduling and machine table scheduling system for a wool textile workshop, comprising a data acquisition module, a data processing and analysis module, a production scheduling planning module, a machine table scheduling module, a monitoring and feedback module, a conveying unit, a user interface module and a process knowledge base module; the data acquisition module is responsible for acquiring various types of production data such as order information, equipment state information, process parameter information and production progress information of the wool textile workshop, and the data acquisition mode is realized through a combination of multiple modes such as sensors, equipment interfaces and manual input; the data processing and analysis module is used for cleaning, arranging and storing the collected production data; it is convenient to fully utilize the workshop equipment resources, avoid equipment idling or overloading, reduce the waiting time and downtime in the production process, thereby improving the overall production efficiency of the workshop, timely adjusting the production scheduling plan and the machine table scheduling scheme, and enhancing the production flexibility of the workshop.

An apparatus and method for scheduling a set of jobs for a plurality of machines is provided. A method for scheduling a set of jobs for a plurality of machines, wherein each job is defined by at least one feature characterizing a processing time of the job, wherein if any machine is idle, a job is selected from the set of jobs to be executed by the machine and scheduled for the machine, wherein the job is selected as follows: a graph neural network receives as input the set of jobs and a current state of at least the idle machine, the graph neural network outputs a reward for each job if started on the machine, the state is input into the graph neural network, and the job for the idle machine is selected depending on the graph neural network output.

The invention provides a refueling machine scheduling method, device, equipment, medium and program product, and relates to the field of automation, the method comprises the steps of obtaining data collected by hardware, and generating a first processing result based on the data collected by the hardware, the data comprising refueling gun operation data of a refueling machine, data of a vehicle entering a gas station and gas station environment data, the first processing result comprises an estimated oil product type and an estimated refueling amount corresponding to each queuing vehicle in the gas station; acquiring a second processing result according to the first processing result and historical data, wherein the second processing result comprises an oiling machine distribution priority list; and optimizing the second processing result based on the first processing result to obtain an optimization result, wherein the optimization result is used for scheduling the refueling machine.

This invention discloses a visualized flexible scheduling method and system for machining production, relating to the field of production scheduling technology. The method includes: obtaining machining machine groups and the machining coordinate system of each machining machine group based on the process machines of all core processes; obtaining the first span machine of schedulable parts and all process machines based on simulated machining; and performing flexible scheduling using a multi-machine scheduling method. This invention addresses the problem in existing flexible scheduling methods for machining production where, when there are many types of machining machines in the workshop and multiple processing routes for parts, the numerous possible equipment combinations in case of machine failure lead to poor timeliness or local optima after rescheduling the machining machines, thus preventing the most efficient flexible scheduling of parts.

This invention belongs to the technical field of computer system management, and relates to a multi-agent dynamic orchestration and secure scheduling system based on model-based software. The system includes: an intent parsing and risk labeling module, which outputs intent operation sequences; a security context token generation module, which creates security context tokens containing permission locks and operation licenses; a state machine scheduling and execution module, which schedules agents to execute operation steps with a risk level lower than a preset security threshold; a gating verification scheduling module, which outputs verification reports; and a binding execution authorization module, which receives the verification reports, updates the status of the security context tokens to authorized or terminates the current task flow and triggers a security mechanism. This invention solves the problem of lacking security constraint mechanisms for high-risk operations, lacking permission control and operation boundary limitation measures, and making it difficult to prevent security incidents caused by permission abuse.

The invention provides a unit scheduling method, device and equipment and a storage medium, relates to the technical field of data processing, and is used for improving the flight scheduling efficiency. The method comprises the following steps: acquiring a function interface array of a flight sub-rule; the function interface array comprises a plurality of function interfaces, and one function interface corresponds to one flight sub-rule. Under the condition that a first function interface in the function interface array is called, obtaining a calling value corresponding to the first function interface; the first function interface is any function interface in the plurality of function interfaces. Under the condition that the calling value is the first numerical value, obtaining a first adaptation result corresponding to the first function interface; the first numerical value is a numerical value obtained after the first function interface is called, and the adaptation result is a first adaptation result obtained by performing adaptation on the employee information of the flight crew by the first function interface based on a flight sub-rule corresponding to the first function interface. And arranging flights for the flight crew based on the first adaptation result.

The invention provides a parallel machine scheduling optimization method and device, equipment and a storage medium, and relates to the technical field of equipment scheduling, and the method comprises the steps: determining a target sample from a sample set based on the sample set for a parallel machine and a response value corresponding to each sample in the sample set; wherein response values corresponding to samples in a first sample subset contained in the sample set are determined through an order expected processing time calculation model, response values corresponding to samples in a second sample subset contained in the sample set are determined through an order expected processing time simulation model, and each sample corresponds to a parallel machine scheduling decision; determining a collection value corresponding to the target sample through the collection function; on the basis of the collection value, a new sample is obtained by optimizing a collection function; adding the new sample into the sample set, and returning to execute the step of determining the target sample from the sample set until a preset termination condition is met; and determining a scheduling optimization decision of the parallel machine according to the response values corresponding to the samples in the sample set.

The application discloses a multi-machine scheduling method and device for track inspection robots based on a genetic algorithm and reinforcement learning, equipment and a medium, relates to the field of intelligent operation and maintenance of rail transit trains, and comprises the following steps: constructing a fitness evaluation function of a track inspection robot; initializing a population of the track inspection robot through a genetic algorithm to obtain an initial population, generating a child population based on the initial population, and screening the child population based on the fitness evaluation function to obtain a target population; constructing a reward function based on the iteration progress, Hamming distance, fitness value of the target population, and an action set corresponding to the target population through a reinforcement learning framework, and constructing a Q value function by using the reward function, a state set corresponding to the target population, and the action set, so as to generate a scheduling strategy for the track inspection robot by using the Q value function, and to schedule multiple track inspection robots by using the scheduling strategy. The application can improve the work execution efficiency of multiple track inspection robots in a complex environment.

The present disclosure provides a task processing method and device, equipment and a storage medium, relates to the technical field of autonomous driving, in particular to the technical field of high-definition map, and can be used in the production scene of electronic map. The specific implementation scheme is as follows: determining resource requirement information of a to-be-processed task, for example, a high-definition map data processing task; selecting a target physical machine from candidate physical machines according to the resource requirement information, resource remaining information reported by the candidate physical machines and the number of tasks in processing; and controlling the target physical machine to execute the to-be-processed task. The rationality of physical machine scheduling can be improved in the process of executing a task, thereby ensuring the rational use of physical machine resources.

The invention discloses a resource-constrained parallel machine transformer neighborhood search method considering differentiated order levels, which comprises the following steps: dividing orders into privileged orders, periodic orders and monthly orders based on an order hierarchical strategy, and receiving resource constraints; generating an initial scheduling scheme as an initial solution by adopting a heuristic rule; constructing a neighborhood structure set comprising a plurality of neighborhood structures of different dimensions; adopting multi-neighborhood collaborative search, taking the initial solution as a starting point, sequentially applying the neighborhood structures in the neighborhood structure set to disturb the current solution, generating a new solution, and performing constraint verification on the new solution; evaluating the quality of the new solution based on a fitness function, and determining whether to accept the new solution according to a simulated annealing criterion; and outputting a global optimal solution as a final scheduling scheme. On the basis of generating a scheduling scheme by using a heuristic rule, a multi-dimensional neighborhood action is designed aiming at the defects of the existing heuristic rule, and a variable neighborhood search framework is adopted to further optimize the scheduling scheme.

The application discloses a kind of set up and release of collaborative optimization system of port loader scheduling, it is related to port scheduling technical field, including the following steps: step 1, data acquisition: data set is obtained by MySQL database;Step 2, data processing;Step 3, establish set up and release of collaborative optimization model of port loader scheduling, divided into two parts of constraint condition and the construction of objective function;In the present application, intelligent scheduling, efficient collaboration: according to the complex field conditions of gas transport set up and release, establish set up and release of collaborative optimization mathematical model of port loader scheduling under matching scene, the optimal loader configuration and controllable release vehicle can be accurately calculated, to ensure the efficient use of resources, through the system not only can significantly improve the efficiency of field operation, but also can promote the collaboration and smoothness of overall operation process.

The invention discloses a weeding machine scheduling method for soil mechanical compaction subduction, and belongs to the technical field of intelligent scheduling. The invention aims to solve the technical problem that the existing weeding machine scheduling method does not consider the soil compaction degree during operation path planning, so that the weeding machine repeatedly grinds the same piece of soil, the air permeability of the soil is reduced, and the development of crops is restricted. Obtaining single-plant crop data on a target land parcel by identifying target farmland image data; dividing the target plot into a plurality of operation blocks based on the single-plant crop data, and constructing a block basic data set; constructing a weeding machine scheduling model based on the block basic data set; and solving the weeding machine scheduling model by using a firefly heuristic algorithm to obtain a weeding machine scheduling scheme. The weeding machine scheduling method is mainly used for generating the weeding machine scheduling method considering the soil compactness and the total scheduling cost at the same time.

The embodiment of the invention provides a physical machine scheduling method and device, a storage medium and electronic equipment, and relates to the technical field of cloud computing. The method comprises the following steps: receiving a scheduling request sent by a target object, wherein the scheduling request comprises a target object identifier of the target object; obtaining a reference label identifier, wherein the reference label identifier comprises at least one label identifier which is inquired from an object label relation table and corresponds to the target object identifier; querying at least one reference physical machine identifier corresponding to the reference tag identifier from a physical machine tag relationship table, and determining a target physical machine according to a physical machine corresponding to each reference physical machine identifier; and scheduling the target physical machine to execute the target task, such as creating a cloud server. According to the corresponding relation among the target object, the label and the physical machine, the target physical machine can be determined for the target object in time, and the efficiency of the physical machine scheduling process is improved.

The invention relates to the technical field of agricultural intelligent scheduling, in particular to a multi-machine cooperative scheduling method for coping with a dynamic interference condition based on an HQNSGA-II algorithm. The method mainly comprises the following steps: firstly, constructing an electronic topological map by utilizing a topological graph method; then designing a corresponding rescheduling strategy based on the dynamic interference type, and executing rescheduling; and finally, performing task allocation and job optimization by adopting an HQNSGA-II algorithm. The invention aims to solve the problem that in the existing agricultural machinery scheduling technology, multi-machine cooperation is difficult to deal with dynamic interference, a hybrid algorithm HQNSGA-II is constructed, falling into a local optimal solution is avoided, and the global search capability is enhanced. Corresponding rescheduling strategies are designed for different dynamic interference types, resource allocation and operation implementation are effectively optimized, the method is more in line with actual agricultural scenes, and high efficiency and stability of agricultural production are ensured.

The application belongs to the field of workshop scheduling, and particularly discloses a mixed flow shop dynamic scheduling method and system considering machine predictive maintenance, which comprises the following steps: taking minimizing total completion time, maintenance cost and processing cost as the target, establishing a mixed flow shop scheduling problem as a multi-objective joint optimization model, setting the same number of intelligent agents as the number of processing stages, and constructing a Markov decision process; each intelligent agent has an independent scheduling network, including a workpiece scheduling network and a machine scheduling network; based on the Markov decision process, the intelligent agents are trained, the workshop is maintained at the operation and maintenance point, and the workpiece and machine selection are respectively performed by calling the workpiece scheduling network and the machine scheduling network at the scheduling point; after the training is completed, the trained intelligent agents are used to realize the dynamic scheduling of the workshop. The application effectively overcomes the mixed flow shop dynamic scheduling problem considering machine predictive maintenance by integrating the workshop scheduling of machine operation and maintenance, and has good dynamic and adaptability.

The invention discloses a multi-target fast response scheduling method and system for a wafer photoetching area, and relates to the technical field of flexible job shop non-equivalent parallel machine scheduling, and the method comprises the steps: collecting the historical production data of the photoetching area, carrying out the standardization processing, generating a training set and a verification set, constructing a self-adaptive scheduling intelligent agent, and defining a scheduling environment model. A sliding window feature extraction mechanism is adopted to dynamically adjust multi-target weights; intelligent agent network parameters are updated through a parallel evaluation framework; intelligent agent model performance is verified by using a verification data set; a scheduling strategy passing verification is deployed to a real-time scheduling system; and a dynamic decision is triggered by continuously monitoring a wafer arrival event and an equipment state. According to the invention, the intelligent, dynamic and multi-objective optimization of wafer photoetching area scheduling is realized, the production efficiency, the resource utilization rate and the punctual delivery rate are obviously improved, and the adaptability of the system to dynamic change is enhanced.

The application discloses a generalized job shop scheduling method based on improved NSGA-III. The method steps of the application comprise the following steps: constructing a mathematical model; determining constraint conditions of the mathematical model; setting coding and decoding based on the improved NSGA-III, then coding processes and machines; carrying out population initialization by using a mixed selection mechanism and a reference point optimization strategy; generating a new generation of population by performing chromosome crossover and variation on the population; performing non-dominated sorting on the new generation of population, then optimizing the offspring based on the reference point optimization strategy; judging whether the optimized offspring individuals meet a termination condition, and if not, returning to the step of generating the new generation of population by performing crossover and variation; and outputting related process and machine scheduling Gantt charts after decoding the process coding and the machine coding according to the decoding mode based on the improved NSGA-III. The application can efficiently schedule high-dimensional multi-objective generalized job shops under forced same-machine operation conditions.