This paper deals with the optimization of a manufacturing–remanufacturing–transport–warehousing closed-loop supply chain, which is composed of two machines for manufacturing and remanufacturing, manufacturing stock, purchasing warehouse, transport vehicle and recovery inventory. The proposed system takes into account the return of used end-of-life products from the market. Manufactured and re-manufactured products are stored in the manufacturing stock. The used end-of-life products are stored in the recovery inventory for remanufacturing. The vehicle transports products from the manufacturing stock to the purchasing warehouse. The objective of this work is to simultaneously evaluate the optimal capacities of manufacturing stock, purchasing warehouse and the vehicle, as well as the optimal value of returned used end-of-life products. Those four decision variables minimize the total cost function. A discrete flow model, which is supposed to be the most realistic, is used to describe the system. An optimization program, based on a genetic algorithm, is developed to find the decision variables. Numerical results are presented to study the influence of transportation time, unit remanufacturing cost and configuration of the manufacturing/remanufacturing machines on the decision variables.

Due to environmental legislation pressure and the competition between manufacturing firms, a high number of production firms are obliged to collect and remanufacture used products. As a result, firm leaders and academic researchers are devoted to developing and managing new sustainable supply chains. Most of the published works in the literature assume that new and remanufactured products are of the same quality, and that all of the returned-used products are remanufacturable. However, in practice, new products are perceived as being of higher quality than remanufactured ones, and the remanufacturing depends on the quality of the returned-used products. This paper aims to bridge this gap in the literature by providing an optimal design for a manufacturing/remanufacturing system that differentiates between new and remanufactured products and sorts the used products into three quality levels. The objective is to determine the optimal storage capacities and production decisions regarding new and remanufactured products while considering carbon emissions. A model is developed to consider the above issues and determine the total profit. An evolutionary algorithm is developed to find the optimal values regarding store capacities and the remanufacturing periods of new and remanufactured products that maximize the total profit. Numerical results are provided to study the impact of the quantity and quality of returned-used products on the optimal values of store capacities, the remanufacturing periods of new and remanufactured products, and carbon emissions.

With an increase of environmental pressure on economic activities, reverse flow is increasingly important. It seeks to save resources, eliminate waste, and improve productivity. This paper investigates the optimization of the disassembly, remanufacturing and assembly system, taking into account assembly-disassembly system degradation. An analytical model is developed to consider disassembly, remanufacturing of used/end-of-life product and assembly of the finished product. The finished product is composed of remanufactured and new components. A maintenance policy is sequentially integrated to reduce the system unavailability. The aim of this study is to help decision-makers, under certain conditions, choose the most cost-effective process for them to satisfy the customer as well as to adapt to the potential risk that can perturb the disassembly-assembly system. A heuristic is developed to determine the optimal ordered date of the used end-of-life product as well as the optimum release dates of new external components. The results reveal that considering some remanufacturing and purchase components costs, the proposed model is more economical in comparison with a model without remanufactured parts. Numerical results are provided to illustrate the impact of the variation of the ordering cost and quality of the used end-of-life product on the system profitability. Finally, the risk due to system repair periods is discussed, which has an impact on managerial decision-making.

A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.