Flexible Job Shop Uncertain Production Scheduling Based On Improved MVO

With the intensification of global competition and the requirements of manufacturing power and sustainable development of manufacturing,the modern manufacturing industry is developing in the direction of intelligence,efficiency,flexibility,and green.Under such circumstances,manufacturing enterprises are facing a more complex and changing market demand and supply chain environment,thus production scheduling is facing more and more uncertainty and variability,and the traditional production scheduling methods can hardly meet the actual needs.Therefore,it is of great practical significance to study the uncertainty production scheduling problem in flexible job shop to solve the uncertainty problem in production scheduling for manufacturing enterprises.Firstly,the study starts from the domestic and international literature on uncertainty production scheduling in flexible job shop and the application of the Multi-Verse Optimization(MVO)algorithm,introduces the common uncertainty factors in flexible job shop scheduling,and elaborates the theoretical and practical significance of flexible job shop production scheduling considering uncertainty.Based on the existing literature and the actual production situation,three uncertainty factors,namely,process time fluctuation,machine failure,and rush order insertion,and the basic solution algorithm of the problem,namely,the MVO algorithm,are identified as the basic research contents.Subsequently,the uncertainty scheduling optimization method using fuzzy process processing time and dynamic optimization of machine failure and rush order is determined by combining the characteristics of the above uncertainty factors.Following that,an uncertain production scheduling model for a flexible job shop is constructed,and an improved multiverse optimization algorithm is designed to solve the model.Secondly,an improved MVO algorithm is designed.First,the population is initialized by the chaotic greedy algorithm to ensure the diversity and quality of the population.Second,the study introduces a self-crossing technique and an insertion-based heuristic algorithm,which simulate the process of exchanging individuals between black/white holes and wormholes,respectively,to improve the global search capability of the algorithm.Third,four neighborhood structures are used for variable neighborhood search,which enhances the local search ability.Fourth,a universe selection mechanism is proposed to facilitate the fast convergence of the algorithm and reduce the possibility of the algorithm falling into the local optimum.Fifth,a leftshift strategy is used in the decoding process,which makes full use of the idle time of the machine and effectively reduces the maximum fuzzy completion cycle.Finally,three groups of 16 cases of different sizes and types were set up to test the performance of the improved MVO algorithm,and the computational results show that the improved MVO algorithm has a significant performance improvement compared with its counterpart.Moreover,the execution time of the improved MVO algorithm is also the smallest in most cases.This fully verifies the effectiveness and superiority of the improved MVO algorithm.Subsequently,the study applies the model and solution algorithm to a fiber optic manufacturing shop scheduling problem in an enterprise,and the case results show that the model and the improved MVO algorithm can effectively solve the flexible job shop uncertainty production scheduling problem compared with the manual way of coping with the interference of uncertainties.In summary,the flexible job shop uncertainty production scheduling model and its solution algorithm have certain feasibility and practical significance in solving the uncertainty production scheduling problem,which can provide some reference and consideration for the uncertainty production scheduling management of enterprises.