Reconfigurable manufacturing systems (RMS) are considered the future of manufacturing, being able to overcome both dedicated (DMS) and flexible manufacturing systems (FMS). In fact, they provide significant cost and time reductions in the launch of new products, and in the integration of new manufacturing processes into existing systems. The goals of RMS design are the extension of the production variety, the adaption to rapid changes in the market demand, and the minimization of the investment costs. Despite the interest of many authors, the debate on RMS is still open due to the lack of practical applications. This work is a review of the state-of-the-art on the design of cellular RMS, compared to DMS, by means of optimization. The problem addressed belongs to the NP-Hard family of combinatorial problem. The focus is on non-exact meta-heuristic and artificial intelligence methods, since these have been proven to be effective and robust in solving complex manufacturing design problems. A wide investigation on the most recurrent techniques in DMS and RMS literature is performed at first. A critical analysis over these techniques is given in the end

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The selection of conceptual design alternatives is crucial in product development. This is due both to the fact that an iterative process is required to solve the problem and that communication among design team members should be optimized. In addition, several design constraints need to be respected. Although the literature offers several alternative selection methods, to date, only very few are currently being used in industry. A comparison of the various approaches would improve the knowledge transfer between design research and practice, helping practitioners to approach these decision support tools more effectively. This paper proposes a structured comparison of two decision support methods, namely the Fuzzy-Analytic Hierarchy Process and Pugh’s Controlled Convergence. From the literature debate regarding selection methods, four relevant criteria are identified: computational effort, suitability for the early design stages, suitability for group decision making, and ease of application. Finally a sensitivity analysis is proposed to test the robustness of each method. An industrial case study is described regarding an innovative and low-cost solution to increase the duration of heel tips in women’s shoes. The selection of conceptual design alternatives of the heel tip presents complex challenges because of the extremely difficult geometric constraints and demanding design criteria

This paper presents a global optimization method able to find gear profile modifications that minimize vibrations. A non linear dynamic model is used to study the vibrational behavior; the dynamic model is validated using data available in literature. The optimization method takes into account the influence of torque levels both on the static and the dynamic response. Therefore, two different objective functions are considered; the first one is based on static analysis and the second one is based on the dynamic behavior of a lumped mass system. The procedure can find the optimal profile modification that reduce the vibrations over a wide range of operating conditions. In order to reduce the computational cost, a Random-Simplex optimization algorithm is developed; the optimum reliability is also estimated using a Monte Carlo simulation. The approach shows good performances both for the computational efficiency and the reliability of results.

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