Publication Type
Journal Article
Version
submittedVersion
Publication Date
3-2007
Abstract
In this paper, we study the multi-machine scheduling problem with earliness and tardiness penalties and sequence dependent setup times. This problem can be decomposed into two subproblems—sequencing and timetabling. Sequencing focuses on assigning each job to a fixed machine and determine the job sequence on each machine. We call such assignment a semi-schedule. Timetabling focuses on finding an executable schedule from the semi-schedule via idle-time insertion. Sequencing is strongly NP-hard in general. Although timetabling is polynomial-time solvable, it can become a computational bottleneck if the procedure is executed many times within a larger framework. This paper makes two contributions. We first propose a quantum improvement to the computational efficiency of the timetabling algorithm. We then apply it within a squeaky wheel optimization framework to solve the sequencing and overall problem. Finally, we demonstrate the strength of our proposed algorithms by experiments.
Keywords
Earliness-tardiness, Meta-heuristics, Scheduling, Squeaky wheel
Discipline
Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Publication
Annals of Operations Research
Volume
159
Issue
1
First Page
83
Last Page
95
ISSN
0254-5330
Identifier
10.1007/s10479-007-0284-z
Publisher
Springer Verlag
Citation
FENG, Guang and LAU, Hoong Chuin.
Efficient algorithms for machine scheduling problems with earliness and tardiness penalties. (2007). Annals of Operations Research. 159, (1), 83-95.
Available at: https://ink.library.smu.edu.sg/sis_research/1195
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1007/s10479-007-0284-z
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons