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

Kuo Ching Ying, Chung-Cheng Lu, Shih Wei Lin*, Jie Ning Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number7532174
JournalMathematical Problems in Engineering
Volume2017
DOIs
StatePublished - 1 Jan 2017

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