Reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm, which can easily adjust its capacity and functionality for rapid responsiveness to sudden changes in the market. The core component of RMS is called reconfigurable machine tool (RMT), which has a modular structure. The RMTs can be reconfigured into many configurations. This ability allows RMS to manufacture many types of products with high quantities. In this paper, the scheduling of products in a multi-product line is fulfilled based on three criteria: profit over cost, due date, and reconfiguration responsiveness index. The latter is the combination of reconfiguration time and reconfiguration reliability of machines. An integrated approach of maximum deviation method (MDM) and multi-criteria decision-making (MCDM) approach called technique for order preference by similarity to ideal solution (TOPSIS) is proposed as a solution approach for getting the optimal scheduling of the products to be manufactured in RMS. Weights of criteria have been calculated using MDM and ranking of products is obtained using TOPSIS. A numerical example is presented to illustrate the scheduling of products in RMS.

Multi-objective optimization evolutionary algorithms (MOEAs) are considered as the most suitable heuristic methods for solving multi-objective optimization problems (MOPs). These MOEAs aim to search for a uniformly distributed, near-optimal and near-complete Pareto front for a given MOP. However, MOEAs fail to achieve their aim completely because of their fixed population size. To overcome this limit, an evolutionary approach of multi-objective optimization was proposed; the dynamic multi-objective evolutionary algorithms (DMOEAs). This paper deals with improving the user requirements (i.e., getting a set of optimal solutions in minimum computational time). Although, DMOEA has the distinction of dynamic population size, being an evolutionary algorithm means that it will certainly be characterized by long execution time. One of the main reasons for adapting parallel evolutionary algorithms (PEAs) is to obtain efficient results with an execution time much lower than the one of their sequential counterparts in order to tackle more complex problems. Thus, we propose a parallel version of DMOEA (i.e., PDMOEA). As experimental results, the proposed PDMOEA enhances DMOEA in terms of three criteria: improving the objective space, minimization of computational time and converging to the desired population size.