A simulation based optimization approach to combine two hedging control policies in a transported degrading failure-prone manufacturing system

Abstract

This paper deals with joint production and corrective maintenance problem of a transported material network failure-prone manufacturing system along which two aspects are supposed. First, each non-identical machine are subject to degradation with failure phenomena. When a failure occurs, system is either repaired or replaced with new one, repairing activity not only degrades machine operating state, but also increases with the next repair time. Second, optimality production control policy called Modified Hedging Point Policy (MHPP) and Modified Hedging Corridor Policy (MHCP) are applied for given network machine. The aim of this article is to find decision variables so as to minimize incurred cost function, including repair costs, stock related costs (i.e., holding costs, backlog and shortage) and setup cost, over an finite-horizon time. Simulation experimental approach with meta-heuristic algorithm is applied to obtain near-optimum decision variables.

Impact of Unreliable Subcontracting on Production and Maintenance Planning Considering Quality Decline

“…The proposed model defined appropriate countermeasures by considering the level of inventory, the evolution of defectives, and the dynamics of failures and repairs, and a continuous-time stochastic process was used to model the underlying dynamics of the production system. This research aimed to extend previous results of the conventional system presented in Assid et al [13] and mainly in the studies of Rivera-Gómez et al [17][18][19][20][21][22][23][24][25][26][27][28], which investigated the effective coordination of production and subcontracting policies in the case of progressive quality decline. The mathematical complexity and the stochastic behavior of the proposed production system imposed several challenges, which were overcome with the use of a methodology based on several techniques, such as a semi-Markov process, stochastic optimal control, and simulationbased optimization.…”

“…The influence of quality control or subcontracting has not been addressed in deterioration models. The same observation applied to the studies that adopted a simulation approach, such as Hosseini and Tan [25], Guiras et al [26], Abdolmaleki et al [27], etc. Therefore, based on this discussion, we concluded that there is a current need to develop an integrated model that considers the key functions of production, subcontracting, quality, and maintenance in a stochastic and quality deteriorating context.…”

“…They also analyzed the integration of imperfect maintenance with the consideration of returned products. Abdolmaleki et al [27] used a simulation model to manage the production and maintenance strategies of a transported material network system. They considered that the machines were subject to deterioration with failures and their machine increased its deterioration level with each repair conducted.…”

“…In this table, the rows classify the studies presented in the literature review in five main areas, and the columns present the key factors investigated in these studies. [4] Rivera-Gómez et al [5] Hajej and Rezg [6] (b) Production-quality-maintenance strategies Bouslah et al [7] Fakher et al [8] Cheng et al [9] Abubakar et al [10] Ait-El-Cadi et al [11] Hajej et al [12] (c) Production and subcontracting planning Assid et al [13] Ben-Salem et al [14] Haoues et al [15] Ayed et al [16] Rivera-Gómez et al [17] Kammoun et al [18] (d) Deteriorating systems Boudhar et al [19] Martinod et al [20] Ouaret et al [21] Polotski et al [22] Dellagi et al [23] Magnanini and Tolio [24] (e) Simulation approach Hosseini and Tan [25] Guiras et al [26] Abdolmaleki et al [27] Rivera-Gómez et al [28] Assid et al [29] Ait-El-Cadi et al [30] (f) Additional issues Fathollahi-Fard et al [31] Jian et al [32] Villalonga et al [33] Chen et al [34] Fathollahi-Fard et al [35] Gholizadeh et al [36] Villalonga et al [37] The proposed model…”