![]() ![]() The most reported approach to simulation in scheduling studies involving maintenance activities is discrete event simulation (Alrabghi and Tiwari 2015). More simplicity and higher flexibility have made simulation models superior to analytical and mathematical models in the maintenance optimization problems (Alrabghi and Tiwari 2015). The optimization of the maintenance plans by using simulation is gaining an increasing attention in recent years (Alrabghi and Tiwari 2015). They designed a metaheuristic approach in order to find the Pareto optimal solutions (Zhang et al. They considered the makespan criteria, the expected costs of performing preventive maintenance, and the expected costs of stochastic failures at the same time. ![]() tackled the problem of scheduling maintenance activities and jobs in a nonidentical parallel-machine environment. They assumed that the start time of the maintenance activity must be within a given interval (Mosheiov et al. designed approximation algorithms for two-machine flow shop and open shop scheduling problems in which a flexible maintenance activity must be performed on one of the machines. They assumed that the time between each pair of consecutive maintenance activities cannot be longer than a pre-specified threshold (Wang et al. addressed a machine scheduling problem that the processing time of jobs increases since machine speed degrades and the maintenance activities change the machine back to its normal rate. They assumed that there is a maximum allowed interval between any pair of consecutive PMs (Cui and Lu 2017). Cui and Lu addressed the joint single-machine and flexible maintenance scheduling problem with the makespan as criterion. They proposed a heuristic and a branch and bound algorithm to minimize the maximum tardiness. Sbihi and Varnier ( 2008) addressed a machine scheduling problem in which the maximum allowed continuous working time between two maintenance activities is predefined. They proposed a mixed continuous-discrete genetic algorithm in order to minimize the total weighted expected completion times. Their problem includes positioning maintenance activities in predefined FIs. ( 2009) tackled a machine scheduling problem regarding random failures. They proved that with makespan criterion, the classical list scheduling algorithm is the best possible approximation algorithm. Xu and Yin ( 2011) considered the online version of the single-machine scheduling problem with given FIs. ![]() Chen also presented some mathematical programming formulations for single- and parallel-machine cases with a single flexible maintenance on each machine and total tardiness as criterion (Chen 2006a). In these two studies, it was shown that the problems of scheduling jobs and maintenance activities within FIs are strongly NP-hard and some efficient heuristics were developed. Further, the average reduction of the total costs gained from the flexibility of maintenance intervals on a wide range of parameters is reported.Ĭhen addressed the single-machine scheduling problems with given FIs and the mean flow time (Chen 2006b) and makespan (Chen 2008) as criteria. Numerical studies are used to compare the performance of these algorithms. ![]() Two mixed continuous-discrete variations of the ant colony optimization algorithm and the particle swarm optimization algorithm are developed as the solution approaches. The objective is the minimization of the estimated total costs of the corrective and preventive maintenance, the undesirability of the flexibility (i.e., uncertainty) in maintenance timing, and the tardiness and long due date costs of jobs. In a single-machine production environment, this paper proposes a simulation–optimization approach which establishes periodic flexible maintenance plans by determining the time between the maintenance intervals and the flexibility (i.e., length) of each interval. On the contrary, the maintenance departments tend to know the timing of the long term maintenance plans as certain as possible. From the production point of view, the flexibility of the maintenance intervals enhances the manufacturing efficiency. Preventive maintenance is the essential part of many maintenance plans. ![]()
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