Reconfigurable Manufacturing Systems (RMS) emerged from companies’ needs to increase their responsiveness to an uncertain market, in which customers are increasingly demanding mass-customised products. Companies focused on mass customisation mainly use the modular product design (MPD) strategy to cost-effectively provide a large product variety. Hence, coupling the MPD with the manufacturing in RMS seems to be a good strategy to effectively provide mass-customised products with lower costs. However, few papers have concurrently optimised the modular products’ and RMS’s configurations for that end. Further, very few papers have explored the RMS’s layout configuration. In order to fill these gaps, this paper proposes a Nonlinear Integer Programming model that integrates the configuration of modular products and RMS, driven by individual customer requirements, to minimise manufacturing costs of mass-customised products. An approach combining a Modified Brute-Force Algorithm (MBFA) and a genetic algorithm (GA) is proposed and compared with a CPLEX-based approach for a small-sized problem, proving its ability to find an optimal solution in lower computation time. An illustrative example of modular smartphones confirms the MBFA-GA’s ability to solve medium/large-sized problems in a reasonable amount of time while ensuring an optimal product configuration that meets customer requirements.

The survival of a company nowadays depends on answering to a customer-driven economy, and therefore relies on the performance of its entire network of partners. Competition is no longer among companies nor among supply chains, but rather between networks of companies which form a value network. Thus, the need has arisen to analyse the performance of a network of companies, and include the customer-perceived value in the strategic decision-making process. This paper proposes a framework and a tool to model, simulate and analyse a value network as a decision support system. The method extends the SimulValor approach and language. The discrete event simulation tool relies on a developed value network simulation library. This paper presents a case study in the shoemaking industry to validate the proposed approach.

Currently, manufacturers seek to provide customized and sustainable products, requiring flexible manufacturing systems and advanced production management to cope with customization complexity and improve environmental performance. The reconfigurable manufacturing system (RMS) is expected to provide cost-effective customization in high responsiveness. However, reconfiguration optimization to produce sustainable mass-customized products in RMS is a complex problem requiring multi-criteria decision making. It is related to three problems, process planning, scheduling, and layout optimization, which should be integrated to optimize the RMS performance. This paper aims at integrating the above three problems and developing an effective approach to solving them concurrently. It formulates a multi-objective mathematical model simultaneously optimizing process planning, job-shop scheduling, and open-field layout problem to improve RMS sustainability. The penalty for product tardiness, the total manufacturing cost, the hazardous waste, and the greenhouse gases emissions are minimized. Economic and environmental indicators are defined to modify the Pareto efficiency when searching the Pareto-optimal solutions. Exact Pareto-optimal solutions are obtained by brute-force search and compared with those of the non-environmental indicator model. NSGA-III is adopted to obtain the approximate Pareto-optimal solutions in high effectiveness and efficiency. A small numerical example is applied to validate the mathematical model and resolution methods.

Nowadays, reverse logistics is gaining importance for many companies in different industries. This importance is rooted in the fact that it generates profit and decreases the environmental impacts of products. Even though the decrease of environmental impacts is an indispensable requisite, reverse logistics design is only driven by cost indicators. The main reason behind this high cost is access to environmental information is difficult and is directly linked to data all along the lifecycle of the product. This paper presents a method by which reverse logistics design integrates environmental impacts based on the management of closed-loop product lifecycle. This method is divided into two processes: from beginning of life to end of life and from end of life to beginning of life. The first process integrates product data in order to calculate environmental impacts of the potential reverse logistics networks, whereas the second process selects the most appropriate reverse logistics network before optimising the product based on this particular network. The proposition is illustrated by a case study on a recycled aluminium automotive part.