As manufacturing converts raw materials into products, environmental wastes and emissions are simultaneously generated from the consumption of materials and energy during the manufacturing processes. Then, sustainable manufacturing is defined as the creation of manufactured products using processes that minimize negative environmental impacts, conserve energy and natural resources and that are safe on employees, communities and consumers. Such an approach requires a compromise between ecological and economic aspects to meet the pillars of sustainable development.This paper presents the implementation of particle swarm tool in order to solve multi-objective optimization for sustainable manufacturing. Hence, this study might serve as part of a global approach to model sustainable manufacturing. The main objective of this approach is to develop operations that allow production with respect of ecological, economic and technological constraints. We developed a case study on the cutting conditions during turning at the end of our study.

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This paper presents a new algorithm belonging to the class of swarm intelligence methods, called the adaptive simplified PSO (ASPSO)-based algorithm, for solving reliability-redundancy allocation problems. In this constrained nonlinear mixed-integer problem, both the number of redundant components and their reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The proposed ASPSO operates with a new updating model to adjust the position of particles, without dealing with velocity. In addition, a randomization technique, based on the dispersion of particle bests through the search space, is used to speed up the convergence of the proposed approach and prevent it from being trapped within the local optimum. Moreover, to control the balance between exploration and exploitation, during the search process, two adaptive functions are utilized. The simulation results of four different benchmarks for the reliabilityredundancy allocation problem are reported and compared. Accordingly, the solutions given by the new presented approach are all superior to those best known solutions provided by several methods in the literature.

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