Biobjective Scheduling for Joint Parallel Machines with Sequence-Dependent Setup by Taking Pareto-Based Approach

Abstract

Modern factories have been moving toward just-in-time manufacturing paradigm. Optimal resource scheduling is therefore essential to minimize manufacturing cost and product delivery delay. This paper therefore focuses on scheduling multiple unrelated parallel machines, via Pareto approach. With the proposed strategy, additional realistic concerns are addressed. Particularly, contingencies regarding product dependencies as well as machine capacity and its eligibility are also considered. Provided a jobs list, each with a distinct resource work hour capacity, this novel scheduling is aimed at minimizing manufacturing costs, while maintaining the balance of machine utilization. To this end, different computational intelligence algorithms, i.e., adaptive nearest neighbour search and modified tabu search, are employed in turn and then benchmarked and validated against combinatorial mathematical baseline, on both small and large problem sets. The experiments reported herein were made on MATLAB™ software. The resultant manufacturing plans obtained by these algorithms are thoroughly assessed and discussed.