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Modelling maintenance activities could be a complex and non-linear system which consists of different parameters. This article presents an integrated fuzzy simulation-fuzzy data envelopment analysis (FSFDEA) to cope with a special case of 'maintenance activity planning' problem. First, the maintenance activities are simulated by means of Awesim ® . Due to the ambiguity associated with the time between failures, fuzzy sets theory is incorporated into the simulation network. Different distribution functions for production, failure and maintenance times are estimated using historical data. Then, outputs are computed by carrying out the simulation for different scenarios which are combinations of periodic maintenances. Several outputs including machines and operators' availability, reliability, efficiency and queue length are computed. Data envelopment analysis (DEA) method is used to select the preferred policy for this multi-criteria problem. In order to handle fuzzy outputs of the simulation, fuzzy DEA model is applied. To show the applicability of the proposed FSFDEA algorithm for planning maintenance activities, the data for a series production line is used and different scenarios are investigated. The proposed approach of this study would help managers to identify the preferred strategy considering and investigating various parameters and scenarios.

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This research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective. Highlights A scheduling problem with multiple unavailability is proposed and formulated. A knowledge-based variable neighborhood search algorithm is proposed. Extensive computational tests confirm the good capability of the proposed model and algorithm.