Simultaneous control of production, repair/replacement and preventive maintenance of deteriorating manufacturing systems

Abstract

AbstratThis paper presents a method to find the optimal production, repair/replacement and preventive maintenance policies for a degraded manufacturing system. The system is subject to random machine failures and repairs. The status of the system is considered to degrade with repair activities. When a failure occurs, the machine is either repaired or replaced. A replacement action renews the machine while a repair action brings it to a degraded operational state for which the next repair time increases as the number of repairs increases. A preventive maintenance action is considered to improve the reliability of the machine and therefore the disruptions caused by the machine failures are reduced. The decision variables are the production rate, the preventive maintenance rate and the repair/replacement switching policy upon machine failure. The objective of the study is to find the decision variables that minimize an overall cost, including repair, replacement, preventive maintenance, inventory holding and backlog costs over an infinite planning horizon. The proposed model is based on a semiMarkov decision process and the stochastic dynamic programming method is used to obtain the optimality conditions. A numerical example is given to illustrate the proposed model. A sensitivity analysis is considered to confirm the structure of the control policy and to illustrate the usefulness on the proposed approach.

Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives

“…The Kushner's technique is useful because it converts the continuous minimization problem to a discrete-time, discrete-state decision process, for further details about this technique Dehayem-Nodem et al [21] can be consulted. Without loss of generality, we study the case of a manufacturing system with three defective rates depending on its productivity.…”

Optimal maintenance of a series production system with two multi-component subsystems and an intermediate buffer

“…Ribeiro et al [17] proposed a mixed integer linear programming model to jointly optimise the maintenance strategy and the buffer size. Dehayem Nodem et al [6] simultaneously optimised the production and maintenance of a system with a production unit and a buffer-inventory. Murino et al [16] applied three thresholds (i.e.…”

Stochastic optimal control of manufacturing systems under production-dependent failure rates

“…To solve the HJB equations, we used a numerical method based on the Kushner (1992) approach, such as in Gharbi et al (2011). By approximating ( , ) …”

“…This basic result has been extended in several ways over the years, with most such extensions relating to the Markovian case (see Tan and Gershwin (2004), Dong-Ping (2009), etc.). Only a few papers have examined semi-Markov processes (see Hu and Xiang (1995); Dehayem et al (2011); Kazaz and Sloan (2013) etc.). In the Markovian case, however, a frequent assumption is that the underlying Markov process is homogeneous.…”