This article addresses the problem of joint optimization of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup; production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities.To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behavior and to optimize the control policy parameters which minimize the total cost incurred. An in-depth study of the effects of the system parameters variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.Keywords: Production / setup control, preventive maintenance, inflexible and unreliable manufacturing system, simulation modeling, optimization, response surface methodology. IntroductionIn the context of unreliable manufacturing system, one of the most effective strategies is the hedging point policy (HPP) concept (Akella and Kumar, 1986 Accepted in International Journal of Production Research, Vol. 53, no 15, 2015 2 appropriate safety stock while the machine is operational to respond to customer demands when the machine is down. For failure and repair times described by homogeneous Markov processes, the optimality of the HPP is proved in the case of one-machine one-product type manufacturing system (M 1 P 1 ) with constant demand rate. Integrating preventive maintenance (PM) in the production planning has also attracted many researchers since machine breakdowns may disturb production process and cause delay in schedules. The PM function, in the overall manufacturing sector, is a priority and is central to the concerns of manufacturers. Its role is crucial and is manifested in the failures prevention and the maintaining of production tools in service. Several integrated models that coordinate PM planning decisions with the production scheduling were proposed (Cassady and Kutanoglu, 2005;Wang and Liu, 2013). The main objective was to minimize the total expected weighted completion time of jobs. Other studies argued the interests to integrate PM and planning for production as PM helps maintain the production tool in service and improve the system performances (Wang, 2002). Wee and Widyadana (2013) explain that PM can result in savings due to an increase of the effective service life of the system. Reineke et al.(1999) addressed the problem of determining the appropri...

This article addresses the problem of joint optimization of production and subcontracting of unreliable production systems. The production system considered presents a common problem in the pharmaceutical industry. It is composed of multiple production facilities with different capacities, each of which is capable of producing two different classes of medications (brand name and generic). The resort to subcontracting is double: first, it involves the quantity of products received on a regular basis in order to compensate for insufficient production capacity in existing facilities, second, when needed, urgent orders are also launched in order to reduce the risk of shortages caused by breakdowns of manufacturing facilities. Failure, repair and urgent delivery times may be represented by any probability distributions.The objective is to propose a general control policy for the system under consideration, and to obtain, in the case of two facilities, optimal control parameters that minimize the total incurred cost for a specific level of the customer service provided. Given the complexity of the problem considered, an experimental optimization approach is chosen in order to determine the optimal control parameters. This approach includes experimental design, analysis of variance, response surface methodology and simulation modeling. It allows the accurate representation of the dynamic and stochastic behaviours of the production system and the assessment of optimal control parameters. Other control parameters which represent the subcontracting are introduced and three joint production / subcontracting control policies (general, urgent, regular) This policy offers not only cost savings, but is also easier to manage, as compared to that proposed by Dror et al. [1]. Numerical examples and a sensitivity analysis are also performed to illustrate the robustness of the proposed control policy and the solution approach.

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