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.

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.

In this work, we develop methods to assess the risk of profit–loss resulting from the choice of a computational method for solving a joint production and maintenance-planning problem. In fact, the optimal objective function is calculated via the use of algorithms and optimization methods. The use of these methods can have an impact on an event that can disrupt the optimal production and maintenance plan. To achieve our goals, we start with calculating the manufacturing system’s joint production and maintenance plans over a finite horizon using different methods. In the second part of the work, we propose analytical models to quantify the risk of profit–loss resulting from product returns and the integration of an imperfect maintenance policy. Numerical examples are conducted by adopting the different algorithms used. This study provides insights into the most efficient computational methods for the encountered problems. This research proposes new approaches to help and guide managers in the analysis and evaluation of their decisions.