Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65474
Title: Parallel-machine scheduling of deteriorating jobs with potential machine disruptions
Authors: Yin, Y
Wang, Y
Cheng, TCE 
Liu, W
Li, J
Keywords: Deteriorating jobs
Disruptive environment
Fully polynomial-time approximation scheme
Scheduling
Issue Date: 2017
Publisher: Pergamon Press
Source: Omega, 2017, v. 69, p. 17-28 How to cite?
Journal: Omega 
Abstract: We consider parallel-machine scheduling of deteriorating jobs in a disruptive environment in which some of the machines will become unavailable due to potential disruptions. This means that a disruption to some of the machines may occur at a particular time, which will last for a period of time with a certain probability. If a job is disrupted during processing by a disrupted machine and it does not need (needs) to re-start after the machine becomes available again, it is called the resumable (non-resumable) case. By deteriorating jobs, we mean that the actual processing time of a job grows when it is scheduled for processing later because the machine efficiency deteriorates over time due to machine usage and aging. However, a repaired machine will return to its original state of efficiency. We consider two cases, namely performing maintenance immediately on the disrupted machine when a disruption occurs and not performing machine maintenance. In each case, the objective is to determine the optimal schedule to minimize the expected total completion time of the jobs in both non-resumable and resumable cases. We determine the computational complexity status of various cases of the problem, and provide pseudo-polynomial-time solution algorithms and fully polynomial-time approximation schemes for them, if viable.
URI: http://hdl.handle.net/10397/65474
ISSN: 0305-0483
EISSN: 1873-5274
DOI: 10.1016/j.omega.2016.07.006
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

12
Last Week
2
Last month
Checked on Oct 16, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.