Finding efficient solutions of the multicriteria assignment problem

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F22%3A10250018" target="_blank" >RIV/61989100:27510/22:10250018 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/B9780128237991000085#" target="_blank" >https://www.sciencedirect.com/science/article/pii/B9780128237991000085#</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/B978-0-12-823799-1.00008-5" target="_blank" >10.1016/B978-0-12-823799-1.00008-5</a>

Result language
angličtina
Original language name
Finding efficient solutions of the multicriteria assignment problem
Original language description
The assignment problem (AP) is one of the well-known and most studied combinatorial optimization problems. The single objective AP is an integer programming problem that can be solved with efficient algorithms such as the Hungarian or the successive shortest paths methods. On the other hand, finding and classifying all efficient assignments for a Multicriteria AP (MCAP) remains a controversial issue in Multicriteria Decision Making (MCDM) problems. In this chapter, we tackle the issue by using data envelopment analysis (DEA) models. Importantly, we focus on identifying the efficiency status of assignments using a two-phase algorithm. In phase I, a mixed-integer linear programming (MILP) based on the Free disposable Hull (FDH) model is used to determine minimal complete set (MCS) of efficient assignments. In Phase II, the DEA-BCC model is used to classify efficient assignments as supported or nonsupported. A numerical example is provided to illustrate the presented approach. (C) 2022 Elsevier Inc. All rights reserved.
Czech name
—
Czech description
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Project
<a href="/en/project/GA19-13946S" target="_blank" >GA19-13946S: Performance evaluation in the presence of unclassified factors</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Multi-Objective Combinatorial Optimization Problems and Solution Methods
ISBN
978-0-12-823799-1
Number of pages of the result
19
Pages from-to
193-211
Number of pages of the book
290
Publisher name
Academic Press
Place of publication
San Diego
UT code for WoS chapter
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