Nowadays, manufacturing systems should be cost-effective and environmentally harmless to cope with various challenges in today’s competitive markets. This paper aims to solve an environmental-oriented multi-objective reconfigurable manufacturing system design (i.e., sustainable reconfigurable machines and tools selection) in the case of a single-unit process plan generation. A non-linear multi-objective integer program (NL-MOIP) is presented first, where four objectives are minimized respectively, the total production cost, the total production time, the amount of the greenhouse gases emitted by machines, and the hazardous liquid wastes. Second, to solve the problem, we propose four adapted versions of evolutionary approaches, namely two versions of the well-known non-dominated sorting genetic algorithm (NSGA-II and NSGA-III), weighted genetic algorithms (WGA), and random weighted genetic algorithms (RWGA). To show the efficiency of the four approaches, several instances of the problem are experimented, and the obtained results are analyzed using three metrics respectively hypervolume, spacing metric, and cardinality of the mixed Pareto fronts. Moreover, the influences of the probabilities of genetic operators (crossover and mutation) on the convergence of the adapted NSGA-III are analyzed. Finally, the TOPSIS method is used to help the decision-maker ranking and select the best process plans.

如今，制造系统应该具有成本效益且对环境无害，以应对当今竞争激烈的市场中的各种挑战。本文旨在解决在单单元工艺计划生成的情况下面向环境的多目标可重构制造系统设计（即可持续可重构机器和工具的选择）。首先提出非线性多目标整数规划（NL-MOIP），其中四个目标分别最小化，总生产成本、总生产时间、机器排放的温室气体量和有害液体废物. 其次，为了解决这个问题，我们提出了进化方法的四个适应版本，即众所周知的非支配排序遗传算法（NSGA-II 和 NSGA-III）的两个版本，加权遗传算法 (WGA) 和随机加权遗传算法 (RGA)。为了显示四种方法的效率，对问题的几个实例进行了实验，并使用三个度量分别分析了获得的结果，分别是超体积、间距度量和混合帕累托前沿的基数。此外，分析了遗传算子（交叉和变异）的概率对适应后的 NSGA-III 收敛的影响。最后，采用TOPSIS方法帮助决策者排序，选择最佳工艺方案。和混合帕累托前沿的基数。此外，分析了遗传算子（交叉和变异）的概率对适应后的 NSGA-III 收敛的影响。最后，采用TOPSIS方法帮助决策者排序，选择最佳工艺方案。和混合帕累托前沿的基数。此外，分析了遗传算子（交叉和变异）的概率对适应后的 NSGA-III 收敛的影响。最后，采用TOPSIS方法帮助决策者排序，选择最佳工艺方案。