Dual-Resource Flexible Job Shop Based On Jayaalgorithm Scheduling Optimization Research

With the development of industrial interconnection,China’s manufacturing industry is transforming intelligent production.To comprehensively improve their industrial manufacturing level,the manufacturing enterprises must adjust their production systems.However,under the trend of an aging population and rising labor costs,China’s manufacturing enterprises face a series of problems such as insufficient production capacity,growing production costs,and increasing customer personalized needs.Therefore,overall optimization is urgently needed to improve enterprise production capacity and resources configuration capability.Flexible job-shop scheduling is an important technology to solve this series of problems.It reconstructs enterprises’ manufacturing and organization modes by comprehensively connecting people,machines,objects,and systems to realize the rational allocation of manufacturing resources.This paper focuses on the Flexible job-shop Scheduling Problem with dual resource constraints.It considers using a swarm intelligence algorithm and a critical path-based local search strategy to solve this problem which uses to guide the production and processing of the enterprise.This paper first classifies and summarizes the research of scheduling problems.Then,we establish the mixed-integer programming model of the flexible job-shop scheduling problem with the dual resource constraint.We proposed an improved Jaya algorithm for solving the single-objective FJSP,single-objective DRCFJSP,and multi-objective DRCFJSP,respectively.The main contribution of this research is as follows.(1)Aiming at the single-objective FJSP problem,the paper constructs a mathematical model with the maximum completion time as the optimization decision-making objective,and uses the improved Jaya algorithm and the local search strategy based on the critical path to solve it.The algorithm encodes based on the two-dimensional vectors of the process and equipment and uses the complete decoding method for decoding.That reduces the idle time of the equipment and improves the quality of the solution.In detail,the population is initialized based on the equipment load.And the standard Jaya algorithm is discretized because it cannot be directly used to solve combinational problems.Then,two iterative candidate solution set is used to ensure the diversity of the population.To avoid the algorithm falling into local optimum,a neighborhood search strategy based on critical path information is introduced to improve the algorithm’s performance.The robustness and effectiveness of the improved Jaya algorithm are verified through the calculation of standard benchmarks and compared with other algorithms and scheduling rules.(2)To solve the single-objective DRCFJSP problem,we consider the influence of workers’ proficiency on workshop production and improve the Jaya algorithm according to the characteristics of the problem.The main improvements can be described as follows.A 3D vector coding scheme is presented to initialize the population with integrated feature information of workers,equipment,and workpieces.Then,we design an updating strategy of the algorithm based on the characteristics of the research problem,introduce a local search strategy,and set reasonable acceptance criteria.To verify the superiority of the improved Jaya algorithm,numerous computational experiments are carried out via designed extended instances and compared with other state-of-the-art meta-heuristic algorithms.(3)An improved Jaya algorithm is designed to solve the multi-objective DRCFJSP problem for minimizing the Makespan,key equipment load,and the total equipment load objectives.In detail,a Pareto fast non-dominated sorting strategy and a calculation mechanism of crowding degree distance are introduced to improve the quality of the solution.Then,the crossover and mutation operations are performed based on the dynamically adjusted crossover and mutation probability to increase the diversity of the population.The validity and effectiveness of the improved Jaya algorithm for solving multi-objective optimization problems are verified through the designed extended instances.This paper builds a heuristic solution framework for the flexible job shop scheduling problem under worker resource constraints.This research closely focuses on production resource elements and enterprise production goals,and provides a solution for the multi-objective flexible job shop scheduling problem under resource constraints.At the same time,the research of this framework can help modern manufacturing enterprises realize intelligent production scheduling mode,and provide a theoretical basis for the construction of intelligent manufacturing systems.