Bi Objective Assignment Problem

The Journal of the Operational Research Society


The Editorial Policy of the Journal of the Operational Research Society is: The Journal is a peer-refereed journal published 12 times a year on behalf of the Operational Research Society. It is the aim of the Journal to publish papers, including those from non-members of the Society, which are relevant to practitioners, researchers, teachers, students and consumers of operational research, and which cover the theory, practice, history or methodology of operational research. However, since operational research is primarily an applied science, it is a major objective of the Journal to attract and publish accounts of good, practical case studies. Consequently, papers illustrating applications of OR to real problems are especially welcome.

Coverage: 1978-2014 (Vol. 29, No. 1 - Vol. 65, No. 12)

Moving Wall: 3 years (What is the moving wall?)

The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication.
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For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available.

Terms Related to the Moving Wall
Fixed walls: Journals with no new volumes being added to the archive.
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Complete: Journals that are no longer published or that have been combined with another title.

ISSN: 01605682

EISSN: 14769360

Subjects: Business & Economics, Business

Collections: Arts & Sciences IV Collection, Business & Economics Collection, Business I Collection, JSTOR Essential Collection

In this paper, we present several algorithms for the bi-objective assignment problem. The algorithms are based on the two phase method, which is a general technique to solve multi-objective combinatorial optimisation (MOCO) problems.

We give a description of the original two phase method for the bi-objective assignment problem, including an implementation of the variable fixing strategy of the original method. We propose several enhancements for the second phase, i.e., improved upper bounds and a combination of the two phase method with a population based heuristic using path relinking to improve computational performance. Finally, we describe a new technique for the second phase with a ranking approach, which outperforms all other tested algorithms.

All of the algorithms have been tested on instances of varying size and range of objective function coefficients. We discuss the results obtained and explain our observations based on the distribution of objective function values.


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