A GA Based Approach for Solving Multi Criteria Project Scheduling Problems

Felix Bomsdorf (), Ulrich Derigs () and Elisabeth von Jagwitz ()
Additional contact information
Felix Bomsdorf: University of Cologne, Department of Information Systems and Operations Research (WINFORS)
Ulrich Derigs: University of Cologne, Department of Information Systems and Operations Research (WINFORS)
Elisabeth von Jagwitz: University of Cologne, Department of Information Systems and Operations Research (WINFORS)

Chapter 18 in Operations Research Proceedings 2008, 2009, pp 111-116 from Springer

Abstract: Summary In this paper we present a Genetic Algorithm (GA) for generating eficient solutions for multi criteria resource constrained project scheduling problems where conicting regular as well as non regular performance measures have to be respected (cf. [4]). The GA-scheme is similar to the one developed by [1], i.e. the genes of the genotype do not only represent activities of the phenotype but also information on the decoding scheme and modus. Since a large number of (mostly similar) solutions is usually not of great help for a planner not all efficient solutions are maintained in order to reduce complexity. Thus, we use a “relaxed Pareto operator" which does accept efficient solutions only which differ (substantially) with respect to solution quality (and structure). We have applied this procedure to solve the Movie Shoot Scheduling Problem (MSSP) and we report first results.

Keywords: Schedule Problem; Schedule Scheme; Project Schedule; Project Schedule Problem; Multi Criterion (search for similar items in EconPapers)
Date: 2009
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:sprchp:978-3-642-00142-0_18

Ordering information: This item can be ordered from
http://www.springer.com/9783642001420

DOI: 10.1007/978-3-642-00142-0_18

Access Statistics for this chapter

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