A Decomposition Method for the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP)

Mathias Kühn (), Sebastian Dirkmann, Michael Völker and Thorsten Schmidt
Additional contact information
Mathias Kühn: Institut Für Technische Logistik Und Arbeitssysteme, Dresden University of Technology
Sebastian Dirkmann: Institut Für Technische Logistik Und Arbeitssysteme, Dresden University of Technology
Michael Völker: Institut Für Technische Logistik Und Arbeitssysteme, Dresden University of Technology
Thorsten Schmidt: Institut Für Technische Logistik Und Arbeitssysteme, Dresden University of Technology

A chapter in Operations Research Proceedings 2016, 2018, pp 521-526 from Springer

Abstract: Abstract Multi-Mode Resource-Constrained Multi-Project Scheduling Problems (MRCMPSP) with large solution search spaces cannot be optimized in an acceptable computation time. In this paper, we have focused on decomposition strategies for such large scale problems. Based on literature review a time-based decomposition approach was adopted for the present problem. With time-based decomposition approaches a schedule is divided into several time periods. All activities in a time period describe an independent problem, termed as a sub-problem. Due to the independent optimization of these sub-problems project information regarding the relationships among activities in different time periods is not considered. This loss of information has a negative impact on the overall solution quality. We developed a decomposition strategy to improve the interactions between the sub-problems for a better target performance while reducing the computation time. Based on an initial solution the sub-problems are created and sequentially optimized in a concept similar to rolling horizon heuristics. We introduce a transition stage with a constant and a variable component at the end of each partial schedule to improve the interactions among sub-problems and thus taking the volatile nature of the examined problems into account. In comparison, our approach proved to provide significant improvements in runtime and target performance.

Keywords: Decomposition Method; Rolling Horizon Heuristics; Independent Optimization; Scheduled Basis; Proposed Decomposition Procedure (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-55702-1_69

Ordering information: This item can be ordered from
http://www.springer.com/9783319557021

DOI: 10.1007/978-3-319-55702-1_69

Access Statistics for this chapter

More chapters in Operations Research Proceedings from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().