Opposition-based Multiple Objective Differential Evolution (OMODE) for optimizing work shift schedules

Abstract

Abstract Work shift schedules are often utilized in construction projects to meet project deadlines. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project benefit. This paper develops a novel optimization algorithm, the Opposition-based Multiple Objective Differential Evolution (OMODE), to solve the time–cost-utilization work shift tradeoff (TCUT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. Two numerical case studies of construction projects demonstrate the ability of OMODE generated non-dominated solutions to assist project managers to select an appropriate plan to optimize TCUT, an operation that is otherwise difficult and time-consuming. Comparisons with the non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), and multiple objective differential evolution (MODE) verify the efficiency and effectiveness of the proposed algorithm.