Multi-objective missile boat scheduling problem using an integrated approach of NSGA-II, MOEAD, and data envelopment analysis

Abstract

During peacetime, the missile boat squadron possesses rapidly deployable capacities for crisis response. The squadron conducts maritime reconnaissance and patrolling, safeguarding maritime safety and engaging in humanitarian assistance and disaster relief in the surrounding waters of Taiwan and the Asia Pacific region. To help the Navy efficiently schedule missile boats, this study models the problem as a multi-objective optimization and seeks to develop an integrated approach to optimizing the scheduling problem of a missile boat squadron. We applied a second-generation nondominated sorting genetic algorithm (NSGAII) and a multi-objective evolutionary algorithm based on decomposition (MOEAD) to search the approximated Pareto-optimal set for the problem efficiently. Then, a data envelopment analysis was used to identify super-efficiency solutions among these and rank them. An appropriate solution was then determined based on the relative efficiency instead of the decision-maker preferences. The results were substantiated using 20 datasets and reflected that the NSGAII can provide super-efficient solutions on the input-oriented model, and MOEAD demonstrated precedence in forming super-efficiency solutions in the output-oriented model.