A Hybrid Genetic Algorithm for the One-Dimensional Minimax Bin-Packing Problem with Assignment Constraints

Mariona Vilà () and Jordi Pereira ()
Additional contact information
Mariona Vilà: Universitat Politècnica de Catalunya
Jordi Pereira: Universidad Católica del Norte

A chapter in Computational Management Science, 2016, pp 183-188 from Springer

Abstract: Abstract In this paper, the one-dimensional minimax bin-packing problem with assignment constraints is studied. Among other applications, this problem is used in test-splitting, which consists in assigning several sets of questions into different questionnaires so that every one of these questionnaires contains one question from each one of the original sets. Questions have a weight associated, which typically corresponds to a measure of their difficulty, and the objective is to split the questions among the questionnaires in such a way that the weights are distributed as evenly as possible. We propose a hybrid genetic algorithm for solving this problem, which is then tested on a benchmark set of practically-sized instances. The results show its efficiency in solving large size instances from the literature.

Keywords: Genetic Algorithm; Simulated Annealing; Hybrid Genetic Algorithm; Simulated Annealing Procedure; Assignment Constraint (search for similar items in EconPapers)
Date: 2016
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:lnechp:978-3-319-20430-7_23

Ordering information: This item can be ordered from
http://www.springer.com/9783319204307

DOI: 10.1007/978-3-319-20430-7_23

Access Statistics for this chapter

More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().