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

Content Provider	Springer Nature Link
Author	Grigoriu, Liliana Briskorn, Dirk
Copyright Year	2016
Abstract	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.
Starting Page	183
Ending Page	197
Page Count	15
File Format	PDF
ISSN	10946136
Journal	Journal of Scheduling
Volume Number	20
Issue Number	2
e-ISSN	10991425
Language	English
Publisher	Springer US
Publisher Date	2016-11-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Parallel machines Maintenance Makespan Approximation algorithms Heuristics Minimum subset sum Operation Research/Decision Theory Calculus of Variations and Optimal Control; Optimization Optimization Artificial Intelligence (incl. Robotics) Supply Chain Management
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Management Science and Operations Research Engineering Software

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