Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/47905
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2019-10-28T07:27:49Z | - |
dc.date.available | 2019-10-28T07:27:49Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Zammit, I. (2019). Heuristic and meta-heuristic approaches to cockpit crew scheduling (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/47905 | - |
dc.description | B.SC.SOFTWARE DEVELOPMENT | en_GB |
dc.description.abstract | Scheduling problems are of keen interest to many researchers, mostly due to the challenge of obtaining near-optimal results in polynomial time. Research for crew scheduling has been conducted and incremental improvements have been presented throughout the years, yet the need for further research is evident as no solution can be guaranteed to be optimal, since crew scheduling is proved to be an NP-hard problem. Problems of such complexity require comprehensive thought on the problem representation in order to provide a satisfactory end solution. The main motivation for this research is to investigate whether or not the application of Genetic Algorithms with an appropriate chromosome representation can help to improve on the current solutions to crew scheduling. Undoubtedly, managing to provide a solution that improves on the current state-of-the-art is hard to achieve. The current state-of-the-art technique for crew scheduling is Column Generation, due to its wide use in literature and the impressive results presented through such an approach. This study considers the crew assignment problem for cock-pit crew, where the problem is modelled as a graph. The initial solutions from the graph provide feasible schedules that comply to the airline regulations established. Such solutions represent the population for the Genetic Algorithm. The Genetic Algorithm is then compared to a Column Generation approach in terms of crew satisfaction and results are presented for data from a major US carrier. Satisfactory results are reported. Aspects for further research are discussed as to possibly improve upon the solution. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Genetic algorithms | en_GB |
dc.subject | Production scheduling | en_GB |
dc.title | Heuristic and meta-heuristic approaches to cockpit crew scheduling | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Information and Communication Technology. Department of Computer Information Systems | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Zammit, Isaac | - |
Appears in Collections: | Dissertations - FacICT - 2019 Dissertations - FacICTCIS - 2019 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
19BITSD021.pdf Restricted Access | 1.47 MB | Adobe PDF | View/Open Request a copy |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.