An Improved Immune Algorithms for Solving Flexible Manufacturing System Distributed Production Scheduling Problem Subjects to Machine Maintenance

Abstract

Competitiveness and rapid expansion of flexible manufacturing system (FMS) as one of the industrial alternatives has attracted many practitioners’ and academicians’ interest. Recent globalization events have further encouraged FMS development into distributed, self-reliant units of production center. The flexible manufacturing system in distributed system (FMSDS) considers multi-factory environments, where jobs are processed by a system of FMSs. FMSDS problems deal with the allocation of jobs to factories, independent assignment of job operation to the machines, and operations sequencing on the machine. Additionally, in many previous studies, impact of maintenance as one of the core parts of production scheduling has been neglected. This significantly affects the overall performance of the production scheduling. As such, maintenance has been considered in this paper as part of the production scheduling. The objective of this paper is to minimize the global makespan over all the factories. This paper proposes an Improved Immune Algorithm (IIA) to solve the FMSDS problem. Antibody encoding adoption explicitly represents the information of factory, job, and maintenance, whilst a greedy decoding procedure exploits flexibility and determines the job routing. Rather than s traditional mutation operator, an improvised mutation operator is used to improve the solutions by refining the most promising individuals of each generation. The proposed approach has been compared with other algorithms and obtained satisfactory results, where the algorithm performance has been tested with several parameter tunings.