Sustainable Multi-objective Process Plan Generation in RMS: Dynamic NSGA-II vs New Dynamic NSGA-II

Sustainability is becoming more acknowledged as a critical issue and a must. Companies can boost their sustainability and responsiveness in today's ever-changing economic environment by implementing the reconfigurable manufacturing system (RMS) concept. The goal of this work is to apply evolutionary techniques to tackle a multi-objective single unit process plans generation in a reconfigurable manufacturing environment (RME) that is environmentally conscious. We adapt and compare two versions of the well-known non-dominated sorting genetic algorithm (NSGA), namely Dynamic-NSGA-II and New Dynamic-NSGA-II. Along with the conventional total manufacturing cost and time, the total amount of hazardous liquid waste and the total amount of greenhouse gases (GHG) generated are minimized, which are considered innovative criteria. First, we model the problem as a non-linear integer multi-objective program (NL-MOIP). Second, numerous instances of the problem are tested and the resulting data examined to establish the efficacy of the three techniques. Furthermore, the TOPSIS technique is applied to assist the decision makers in evaluating and choosing the optimal process plans.