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      <dc:title>Maintenance costs and makespan minimization for assembly permutation flow shop scheduling by considering preventive and corrective maintenance</dc:title>
      <dc:creator>Zhang, Zikai</dc:creator>
      <dc:creator>Tang, Qiuhua</dc:creator>
      <dc:creator>Chica Serrano, Manuel</dc:creator>
      <dc:subject>Assembly permutation</dc:subject>
      <dc:subject>Bi-objective flow shop</dc:subject>
      <dc:subject>Preventive maintenance</dc:subject>
      <dc:description>This work is supported by the National Natural Science Foundation of China (No. 51875421). M. Chica is jointly supported by the Spanish Ministry of Science, Andalusian Government, the National Agency for Research Funding AEI, and ERDF under grants EXASOCO (PGC2018-101216-B-I00), SIMARK (P18-TP-4475) and RYC-2016-19800.</dc:description>
      <dc:description>The joint optimization of production scheduling and maintenance planning has a significant influence on production continuity and machine reliability. However, limited research considers preventive maintenance (PM) and corrective maintenance (CM) in assembly permutation flow shop scheduling. This paper addresses the bi-objective joint optimization of both PM and CM costs in assembly permutation flow shop scheduling. We also propose a new mixed integer linear programming model for the minimization of the makespan and maintenance costs. Two lemmas are inferred to relax the expected number of failures and CM cost to make the model linear. A restarted iterated Pareto greedy (RIPG) algorithm is applied to solve the problem by including a new evaluation of the solutions, based on a PM strategy. The RIPG algorithm makes use of novel bi-objective-oriented greedy and referenced local search phases to find non-dominated solutions. Three types of experiments are conducted to evaluate the proposed MILP model and the performance of the RIPG algorithm. In the first experiment, the MILP model is solved with an epsilon-constraint method, showing the effectiveness of the MILP model in small-scale instances. In the remaining two experiments, the RIPG algorithm shows its superiority for all the instances with respect to four well-known multi-objective metaheuristics.</dc:description>
      <dc:date>2025-06-24T11:28:06Z</dc:date>
      <dc:date>2025-06-24T11:28:06Z</dc:date>
      <dc:date>2021-04-13</dc:date>
      <dc:type>journal article</dc:type>
      <dc:identifier>Zhang, Zikai et al. Journal of Manufacturing Systems 59 549-564. https://doi.org/10.1016/j.jmsy.2021.03.020</dc:identifier>
      <dc:identifier>https://hdl.handle.net/10481/104821</dc:identifier>
      <dc:identifier>10.1016/j.jmsy.2021.03.020</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>open access</dc:rights>
      <dc:rights>Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License</dc:rights>
      <dc:publisher>Elsevier</dc:publisher>
   </ow:Publication>
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