Single-Machine Scheduling with Learning Effects and Maintenance: A Methodological Note on Some Polynomial-Time Solvable Cases

This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the a...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-6
Main Authors: Chen, Jie-Ning, Lin, S.-W., Lu, Chung-Cheng, Ying, K.-C.
Format: Article
Language:English
Subjects:
Completion time
Computers
Employment
Industrial engineering
Information science
Job shops
Maintenance
Manufacturing
Mathematical problems
Neural networks
Operations research
Polynomials
Production scheduling
Scheduling
Studies
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work addresses four single-machine scheduling problems (SMSPs) with learning effects and variable maintenance activity. The processing times of the jobs are simultaneously determined by a decreasing function of their corresponding scheduled positions and the sum of the processing times of the already processed jobs. Maintenance activity must start before a deadline and its duration increases with the starting time of the maintenance activity. This work proposes a polynomial-time algorithm for optimally solving two SMSPs to minimize the total completion time and the total tardiness with a common due date.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/7532174