A Single Machine Scheduling with Periodic Maintenance and Uncertain Processing Time

Abstract

A single machine scheduling problem with periodic maintenance is studied in this paper. Due to many uncertainties in reality, the processing time is recognized as an uncertain variable. The aim is to minimize the makespan at a confidence level. An uncertain chance-constrained programming model is developed to delve into the impact of uncertainties on decision variables, and an algorithm for calculating the objective function is proposed. According to the theoretical analysis, a novel method named longest shortest processing time (LSPT) rule is proposed. Subsequently, an improved genetic algorithm (GA-M) combined with LSPT rule is proposed. Numerical experiments are used to verify the feasibility of this model and algorithm.