Byung Soo KimYucel Yilmaz Ozturkoglu2025-10-06201314333015, 026837680268-37681433-301510.1007/s00170-012-4553-xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84888644597&doi=10.1007%2Fs00170-012-4553-x&partnerID=40&md5=6e4f1f642065e6edfac4385c84b3b482https://gcris.yasar.edu.tr/handle/123456789/10093In this paper we study a single machine scheduling problem with deteriorating processing time of jobs and multiple preventive maintenances which reset deteriorated processing time to the original processing time. In this situation we consider three kinds of problems whose performance measures are makespan total completion time and total weighted completion time. First we formulate integer programming formulations and using the formulations one can find optimal solutions for small problems. Since these problems are known to be NP-hard and the size of real problem is very large we propose a number of heuristics and design genetic algorithms for the problems. Finally we conduct some computational experiments to evaluate the performance of the proposed algorithms. © 2012 Springer-Verlag London. © 2013 Elsevier B.V. All rights reserved.EnglishDeterioration, Discrete Optimization, Genetic Algorithms, Preventive Maintenance, Scheduling, Computational Experiment, Discrete Optimization, Integer Programming Formulations, Optimal Solutions, Performance Measure, Single Machine Scheduling Problems, Total Completion Time, Total Weighted Completion Time, Deterioration, Integer Programming, Preventive Maintenance, Scheduling, Scheduling Algorithms, Genetic AlgorithmsComputational experiment, Discrete optimization, Integer programming formulations, Optimal solutions, Performance measure, Single machine scheduling problems, Total completion time, Total weighted completion time, Deterioration, Integer programming, Preventive maintenance, Scheduling, Scheduling algorithms, Genetic algorithmsArticle