Integrated preventive maintenance and flow shop scheduling under uncertainty

Abstract

This paper is concerned with stochastic scheduling of production and maintenance activities in a permutation flow shop setting. We present a two-stage stochastic mixed-integer program (SMIP) that adapts the conventional permutation flow shop scheduling problem for incorporating multiple preventive maintenance activities with various meter-based intervals. The model handles uncertainties in both processing times and the duration of maintenance activities. The concept of combining maintenance activities in scheduling problems is introduced and formulated, along with other practical considerations. The objective is to minimize the total expected cost associated with lateness penalties and maintenance resources. We use simulation–optimization (SO) for solving large-scale instances of the problem, and for validating the SMIP model. Through extensive computational experiments, we show that the SO method is superior in terms of efficiency and effectiveness and evaluate its sensitivity to the input data. Finally, a case study in earth-moving operations is presented, followed by managerial implications.