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

Abstract

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...

A Mathematical Model: A Flexible Manufacturing System, Prone to Error, Making Two Products Each with Stochastic Demand Schedules

“…A numerical approach [58] is used to solve the HJB equations for the system under study. It is described in details in Appendix 2.…”

“…Since HJB differential equations cannot be solved analytically, a finite-difference (discretization) method [58] was used to obtain approximate numerical solutions. The resulting value functions are the solutions of the optimality conditions.…”

“…It is known as the hedging-point policy (HPP), as described previously [32] [57] [58]. The following sections describe the optimization of i z using a simulation approach and DOE combined with a GA.…”

“…For solving optimal control problems, DOE, statistical analysis, and response surface methodology (RSM) are often used, in combination with simulation, to model the behavior of the studied system and the interactions that have an impact on its performance [20] [55] [58]. At the end, system performance is expressed through linear regression.…”

Production and setup policy optimization for hybrid manufacturing–remanufacturing systems

“…Application oriented approach for computing setup and production polices was proposed in (Gharbi et al, 2006) were operational level optimization of production and setup policy (Modified Hedging Point Policy -MHPP) was proposed. Systems with setups, preventive maintenance and customer satisfaction criteria were recently studied in (Assid et al, 2015) using extension of MHPP policy.…”