Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell

International Journal of Production Research

Hajji

2015

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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...

Section: Problem Definitionmentioning

confidence: 99%

“…In order to reduce the risk of shortages and the PM cost, the execution of PM actions on the production machine may depend on its age or on the utilization time from the last maintenance action (Berthaut et al, 2011). As the improvements made in the IMHCP, PM decisions could take into account the level of buffer stocks that would reduce the risk of shortages.…”

mentioning

confidence: 99%

Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems

Assid

International Journal of Production Research

Hajji

2015

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“…To solve the problem and optimize the policies parameters, an experimental approach integrating simulation, design of experiment (DOE) and RSM is adopted as in Berthaut et al (2011). The main steps of the experimental resolution approach are:…”

Section: 1resolution Approach Stepsmentioning

confidence: 99%

“…Hajji et al (2011) and developed the Multiple HPP (MHPP) for multiple state systems. Berthaut et al (2011), Rezg et al (2005) and Ayed et al (2012), among others, considered a joint implementation of a corrective and preventive maintenance strategy and production rate control. For systems producing multiple part types, Bai and Elhafsi (1997) developed Hedging Corridor Policy (HCP), adapted to this context.…”

Section: Introductionmentioning

confidence: 99%

An Environmental Hedging Point Policy to control production rate and emissions in unreliable manufacturing systems

Ben-Salem

International Journal of Production Research

Hajji

2014

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Abstract:This paper proposes a new hedging point policy which integrates environmental concerns into the optimal control of unreliable manufacturing systems. The considered system is composed of a production facility subjects to random failures and producing a product family intended for a given market with stable demand. The manufacturing facility's operations cause harmful emissions to the environment, and may incur sanctions in the form of an environmental tax imposed by the relevant authorities. Given the significant compromise that must take place between inventory, backlog and taxes costs, the main objective of this paper is to propose a feedback adaptive control policy which provides a better control of the production rate and the emissions generated. Under the hedging point policies (HPP) category, a new structure called the Environmental Hedging Point Policy (EHPP) is proposed. To illustrate the effectiveness of the proposal, an experimental approach based on simulation modelling, variance analysis and response surface methodology (RSM) is applied. The results show a significant gain in terms of incurred costs compared to those incurred when the system is governed by a classical HPP. An improved version of EHPP is also proposed for systems with high emission rates. Several sensitivity analyses are conducted to illustrate the robustness and effectiveness of the proposed policies.

“…These mathematical models combine BRP or ARP with production/inventory control policies to respond to demand during maintenance activities (Ki Ling and Hausman (1997), Salameh and Ghattas (2001), Tadashi et al (2001), Chelbi and Ait-Kadi (2004), Kenné et al (2007), Gharbi et al (2007), , Rezg et al (2008), Berthaut et al (2011)). …”

Section: Introductionmentioning

confidence: 99%

Joint optimal production control/preventive maintenance policy for imperfect process manufacturing cell

Dhouib

International Journal of Production Economics

Aziza

2012

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Production, maintenance, and quality are often modeled as separate problems, despite the strong link that exists between these primary components of any manufacturing system. This paper deals with an integrated approach for the joint optimization of production-inventory control and preventive maintenance policy for a manufacturing cell comprising an imperfect process where the 'in-control' period have a general deterioration distribution. The production-inventory control policy based on the hedging point policy consists of building and maintaining a security stock of finished products in order to respond to demand and to avoid shortages during restoration actions. Restoration actions are planned when the system switches to the 'out-of-control' state and starts producing non-conforming items. The manufacturing cell is also subject to an age-based preventive maintenance policy in order to reduce the shift rate to the 'out-of-control' state. The main objective of this paper is to determine the joint optimal policy that minimizes the overall cost, which is comprised of setup, maintenance, inventory holding and shortage costs, as well as the cost incurred by producing non-conforming items. A mathematical model is proposed, and the expression of the overall incurred cost is derived and used as a basis for the optimal determination of the joint production-inventory control and preventive maintenance policy. These issues are illustrated using numerical examples. It is found that performing preventive maintenance will yield reductions in the overall incurred cost. Sensitivity analyses are also carried out in order to illustrate the robustness of the proposed methodology.