Scheduling jobs and maintenance activities subject to job-dependent machine deteriorations

• Published in: Journal of scheduling Vol. 20; no. 2; pp. 183 - 197
• Main Authors: Grigoriu, Liliana, Briskorn, Dirk
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2017; Springer Nature B.V
• Subjects: Algorithms; Approximation; Artificial Intelligence; Automobile industry; Breakdowns; Business and Management; Calculus of Variations and Optimal Control; Optimization; Deterioration; Heuristic; Job shops; Maintenance; Maintenance management; Mathematical analysis; Operations Research/Decision Theory; Optimization; Parallel processing; Production scheduling; Scheduling; Similarity; Studies; Suppliers; Supply Chain Management; Parallel machines; Approximation algorithms; Makespan; Maintenance; Minimum subset sum; Heuristics
• ISSN: 1094-6136; 1099-1425
• DOI: 10.1007/s10951-016-0502-0

This paper considers machine scheduling that integrates machine deterioration caused by jobs and, consequently, maintenance activities. The maintenance state of the machine is represented by a maintenance level which drops by a certain, possibly job-dependent amount while jobs are processed. A maintenance level of less than zero is associated with the machine’s breakdown and is therefore forbidden. Hence, maintenance activities that raise the maintenance level may become necessary and have to be scheduled additionally. We consider the objective to minimize the makespan throughout the paper. For the single machine case, we provide a linear-time approximation algorithm with worst-case a bound of 5/4, and comment on how an FPTAS from previous literature can be employed to apply to our problem. Due to problem similarity, these results also apply to the minimum subset sum problem, and the 5/4 linear-time approximation algorithm is an improvement over the 5/4 quadratic-time approximation algorithm of Güntzer and Jungnickel. For the general problem with multiple machines, we provide approximability results, two fast heuristics, an approximation algorithm with an instance-dependent approximation factor and a computational study evaluating the heuristics.