To make realistic maintenance decisions, it is important that maintenance managers make their preventive replacement decisions based on observations of the condition of their equipment. This study addresses a condition-and age-based replacement decision problem using the complete history of measured condition observations to minimize long-run average cost, maximize long-run average availability, or both. A stochastic filtering process (SFP) is used to estimate the residual lifetime distribution conditional on the history of observed condition information. A long-run average cost model and a long-run average availability model are analysed in order to determine the theorems necessary for calculating the optimum replacement time. To minimize the cost and maximize availability, a multiobjective decision frontier is proposed that will help maintenance managers deal with trade-offs between the two objectives. Finally, numerical examples are presented for each scenario to show the effectiveness of the methods proposed.
Real world scheduling problems are complex in nature. They require satisfying multiple objectives. To get a realistic schedule, consideration of machine reliability and availability is very important to allocate job in machine. This research aims to develop two fuzzy inference systems (FIS) for hybrid flow shop problem. First FIS is used to get priority of each job considering multiple objectives of processing time, due date and cost over time. Second FIS is used to get machine reliability and availability based priority using the information of mean time to failure (MTTF) & mean time to repair (MTTR) of each individual machine at each stage. To distribute the workload depending on their reliability and availability based priority of each machine, maximum utilization target is determined. An algorithm has been developed for grouping, sequencing & allocating the jobs to the machines at every stage in such a way that total percentage of over utilization will minimum. Based on this algorithm, a computing tool has been developed and, explained with a three stage hybrid flow shop scheduling problem.
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