Abstract
Reconfigurable manufacturing system (RMS) is a paradigm that considers many of the issues the economy and society face today. This paper addresses an environmental-oriented multi-objective process plan generation problem in a reconfigurable manufacturing context. Four objective functions are minimized: the total production cost, the total production time, the amount of greenhouse gas emitted by machines, and the hazardous liquid wastes. To solve the problem, three adapted versions of the well-known non-dominated sorting genetic algorithm (NSGA) approach, namely the penalty boundary intersection (PBI) reference point (RP) with the NSGA-II (PBI-R-NSGA-II), NSGA-III with a selection and elimination operator (SE-NSGA-III), and NSGA-III with the similarity coefficient (SC-NSGA-III) are evaluated and compared. Moreover, the impacts of perturbation ratio on SC-NSGA-III's performance as the number of iterations increases is examined. Finally, the efficacity of the three approaches is demonstrated by rich experimental results and analyzes using two metrics, respectively cardinality of the mixed Pareto fronts (CMPF) and max Pareto front error (MPFE).
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