Solving multi-objective Modified Distributed Parallel Machine and Assembly Scheduling Problem (MDPMASP) with eligibility constraints using metaheuristics

Abstract

ABSTRACT We present a new generalization and metaheuristics for the distributed parallel machine and assembly scheduling problem (DPMASP), namely MDPMASP with eligibility constraints. There is a set of unrelated factories or production lines. Each has a series of non-identical parallel machines with varying processing speeds. Then, it was disposed of as a single assembly machine in a series. Each product is assembled from a set of components (jobs). Each item necessitates multiple unidentical jobs. The objectives are to minimize mean flow time and the number of tardy jobs. We suggest four basic heuristics and three metaheuristics to tackle the problem. The Taguchi approach is used to discuss and calibrate various metaheuristic parameters. Algorithms are compared using the four performance measures. The computational results show that the proposed algorithms can solve moderate-sized problems efficiently and near-optimal solutions in most cases. Moreover, based on the four performance measures, MGA is the best method, followed by MSA, MPSO, SH2, SH4, SH1 and SH3.