Genetic algorithms for total weighted expected tardiness integrated preventive maintenance planning and production scheduling for a single machine

Abstract

PurposeThis paper seeks to improve solution procedures for solving a larger version of the integrated preventive maintenance planning and production scheduling model with a total weighted expected tardiness objective function introduced in a 2003 paper by Cassady and Kutanoglu using a genetic algorithm heuristic procedure.Design/methodology/approachIn this paper, heuristics based on genetic algorithms are developed to solve the integrated model.FindingsThe performance of the proposed genetic algorithm heuristics are evaluated using multiple instances of several problem sizes. The results indicate that the proposed genetic algorithms can effectively be used to solve the integrated problem.Practical implicationsThe heuristics presented in this paper significantly improve the ability of the decision‐maker to consider larger instances of the integrated model. One may ask, “how significant is that improvement?” The answer depends on the specific industrial context under consideration and the definition of a “job”.Originality/valueTypically, production scheduling and preventive maintenance planning is planned and executed independently in spite of the inter‐dependent relationship between them. However, the 2003 paper by Cassady and Kutanoglu demonstrates the benefit of using the integrated model to solve these two problems simultaneously. However, their solution procedure is limited to small problems (6‐jobs or less). Therefore, this study intends to improve the solution procedure to solve larger instances of the problem.