Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III

Abstract

ABSTRACT The highly competitive and volatile market puts companies in a tough position. While cost and time efficiency are important to stay competitive, environmental awareness is more and more critical. The reconfigurable manufacturing system (RMS) paradigm is suggested to cope with these new challenges. In addition to its six fundamental characteristics, it is seen as an enabler for Industry 4.0. This article investigates the multi-objective process planning problem in an environmentally conscious manner in a reconfigurable manufacturing environment. Four criteria are minimised: total production cost, total production time, total amount of greenhouse gas produced by machines, and total quantity of hazardous liquid wastes. To address the problem, modified versions of the non-dominated sorting genetic algorithm (NSGA) method, namely new dynamic NSGA-II (NewD-NSGA-II) and New NSGA-III, are developed and evaluated. Rich experimental results are presented and analysed using three metrics to demonstrate the efficacy of the proposed approaches: inverted generational distance (IGD), diversity measure (DM), and cardinality of the mixed Pareto fronts (CMPF). The effects of the similarity coefficient on the convergence of the NewD-NSGA-II and New NSGA-III are investigated, and the TOPSIS technique is used to assist the decision-maker in evaluating and selecting the best process plans.