Quality and production control in multiple-product unreliable manufacturing system A. Hajji*., A. Gharbi**.,S.Dellagi*** *Department of Operations and Decision Systems & CIRRELT, Laval University, CANADA (e-mail: adnene.hajji@osd.ulaval.ca). **Department of Automated Production Engineering, École de Technologie Supérieure, CANADA (e-mail: ali.gharbi@etsmtl.ca) ***Laboratory of Industrial Engineering and Production of Metz , Université Paul Verlaine de Metz, France (e-mail: dellagi@univ-metz.fr) Abstract: This paper seeks to address the joint production and quality specification control problem in multiple-product unreliable manufacturing systems. In this context, the decision maker should make the best compromise between production and quality policy to adopt so as to maximize the long term average per unit time profit. This compromise is required given that the policies governing the production and quality decision making can be conflicting. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, one should consider the unreliability of the manufacturing system. To hedge against future capacity shortages caused by machine failures, the decision maker should adopt an adequate production policy in order to meet customer demand and minimize the incurred total cost. The paper tackle the problem in a dynamic stochastic context with a combined approach, based on optimal control theory, simulation modeling and response surface methodology. The obtained results are promising and show that the profit under the considered joint policy is improved up to 7% compared with dissociated production and quality strategies. Keywords: Quality specification, production control, hedging policy, simulation, experimental design. 1. INTRODUCTION Although the production control decisions taken to improve manufacturing system productivity have a direct impact on the quality and the market value of products, joint production and quality decision making problems, especially in a dynamic stochastic environment, remain relatively unexplored. In a deterministic context several researchers have studied the interaction between manufacturing activities (production & maintenance) and quality control. Integrated models were proposed and the benefits of integrated decision making were established. In particular, the reader is referred to Ben Daya and Rahim (2000), Ben Daya (2002) and Lee and Unnikrishan (1998). To the best of our knowledge, in a dynamic stochastic context, only a limited number of papers have thus far considered the intersection between quality control and production control issues. As we see it, the most comprehensive contribution has been a series of works by Kim and Gershwin (2005 and 2008), who introduce dynamic models of machines including in addition to operational and failure states, various defective quality states. They also incorporated the effects of quality states in the approximate analytical assessment of buffer sizing / quality related decisions in transfer lines. An important feature of the Kim- Gershwin models is that they correspond to a fluid view of the production process, although quality attributes remain attached to discrete parts. A systematic taxonomy of quality /quantity issues is also presented in Gershwin and Schick (2007). By contrast with Kim and Gershwin, Colledani and Tolio (2005, 2006) tackle simultaneous quality and production issues within an entirely discrete framework: they consider a production system with manufacturing and inspection machines. Machines are unreliable and can fail in different modes. In addition, the behaviour of the machines can be monitored by control charts which are used to generate information about their state. This latter work led us, in Hajji et al. (2011), to consider the problem with a more complete view of the interdependence of production and quality. Hence, an unreliable system producing a mixture of good and defective items, where the design of the product specifications and the production control strategies involving an economic decision making process was considered. In the context of a failure prone manufacturing system, our objective here is to address the multiple-product issue. To ILQG DQ ‡RSWLPDO· FRPELQHG SURGXFWLRQ TXDOLW\ VSHFLILFDWLRQ and production control strategy within a (parameterized) class of policies combining multiple hedging inventory levels and the engineering specification limits for each product. For more details on the multiple hedging point structure control strategy the reader is referred to Sharifnia (1988), Kenne et al. (2003), Zanoni et al. (2006) and Gharbi et al. (2008) where the performance of such control strategy in a dynamic stochastic context was observed and analysed. Optimality is defined here as a long term average per unit time measure of quality and quantity dependent revenue from sales, minus Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012 978-3-902661-98-2/12/$20.00 © 2012 IFAC 981 10.3182/20120523-3-RO-2023.00399