Airline Resource Scheduling

Cohn, Amy M.; Lapp, Marcial

doi:10.1002/9780470400531.eorms0020

Cited by 2 publications

(1 citation statement)

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“…Because the rules governing contracts of different crew groups (technical and cabin crew) and crew ranks (captain and first officer) are considerably different, it is common to solve the crew pairing problem for each crew rank separately. Because we will not further make reference to the other scheduling problems mentioned before, we refer the reader to Cohn and Lapp () for an introduction to airline scheduling.…”

Section: Airline Crew Schedulingmentioning

confidence: 99%

Multi‐objective Approaches to the Unit Crewing Problem in Airline Crew Scheduling

Tam

Ryan

Ehrgott

2014

Multi Criteria Decision Anal

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The goal of the crew pairing problem is to partition a flight schedule into sequences of flights called pairings that crew members can operate at minimum cost. It is solved for each crew rank, for example, captains and first officers. In optimised crew pairings, crew often split and join other crew to operate outgoing flights. Because minimum cost pairings contain little buffer time, crew splitting after a delayed flight contributes to the propagation of delay. This effect can be avoided by unit crewing, that is, by keeping crew of different ranks together for as long as possible during a pairing. However, increasing unit crewing increases cost.We investigate sequential and parallel methods for unit crewing, explicitly considering the two objectives of minimising cost and maximising unit crewing. We apply multi-objective techniques in the sequential approach, where the (minimum cost) pairing problem for one crew rank is solved first and both objectives feature when solving for the second crew rank. Because the quality of the pairings obtained by this method is limited by the solution for the first crew rank, we then propose to solve the two crew pairing problems simultaneously, again using multi-objective optimisation methods. We introduce a multi-objective optimisation model for this problem and propose a new heuristic branching technique that favours unit crewed pairings when applied to a scalarised model with an auxiliary objective function. We compare this with a direct approach with management of the number of unit crewing constraints and a Dantzig-Wolfe decomposition approach.Numerical tests on domestic New Zealand data show that the multi-objective approaches considerably increase the level of unit crewing, without much increase in cost, even in the sequential approach. Moreover, we show that the parallel approach is superior in terms of quality of the pairing solution but computationally more expensive. The heuristic branching technique provides good quality solutions in reasonable time, as compared with the Dantzig-Wolfe method.

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Section: Airline Crew Schedulingmentioning

confidence: 99%

Multi‐objective Approaches to the Unit Crewing Problem in Airline Crew Scheduling

Tam

Ryan

Ehrgott

2014

Multi Criteria Decision Anal

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Managing disruption risk in express logistics via proactive planning

Chung

Tse

Choi

2015

Industrial Management &Amp; Data Systems

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Purpose – The purpose of this paper is to carry out a comprehensive review for state-of-the-art works in disruption risk management of express logistics mainly supported by air-transportation. The authors aim to suggest some new research directions and insights for express logistics practitioners to develop more robust planning in air-transportation. Design/methodology/approach – The authors mainly confined the research to papers published over the last two decades. The search process was conducted in two dimensions: horizontal and vertical. In the horizontal dimension, attention was paid to the evolution of disruption management across the timeline. In the vertical dimension, different foci and strategies of disruption management are employed to distinguish each article. Three keywords were used in the full text query: “Disruption management”, “Air transportation”, and “Airline Operations” in all database searches listed above. Duplications due to database overlap, articles other than those from academic journals, and papers in languages other than English were discarded. Findings – A total of 98 articles were studied. The authors categorized the papers into two broad categories: Reactive Recovery, and Proactive Planning. In addition, based on the problem characteristics and their application scenarios, a total of 11 sub-categories in reactive recovery and nine sub-categories in proactive planning were further identified. From the analysis, the authors identified some new categories in the air-transportation recovery. In addition, by analyzing the papers in robust planning, according to the problem characteristics and the state-of-the-art research in recovery problems, the authors proposed four new research directions to enhance the reliability and robustness of air-transportation express logistics. Research limitations/implications – This study provided a comprehensive and feasible taxonomy of disruption risk management. The classification scheme was based on the problem characteristics and the application scenarios, rather than the algorithms. One advantage of this scheme is that it enables an in-depth classification of the problem, that is, sub-categories of each class can be revealed, which provides a much wider and clearer horizon to the scientific progress in this area. This helps researchers to reveal the problem’s nature and to identify the future directions more systematically. The suggestions for future research directions also point out some critical research gaps and opportunities. Practical implications – This study summarized various reasons which account for the disruption in air-transportation. In addition, the authors suggested various considerations for express logistics practitioners to enhance logistics network reliability and efficiency. Originality/value – There are various classification schemes in the literature to categorize disruption management. Using different algorithms (e.g. exact algorithm, heuristics, meta-heuristics) and distinct characteristics of the problem elements (e.g. aircraft, crew, passengers, etc.) are the most common schemes in previous efforts to produce a disruption management classification scheme. However, the authors herein attempted to focus on the problem nature and the application perspective of disruption management. The classification scheme is hence novel and significant.

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Airline Resource Scheduling

Cited by 2 publications

References 74 publications

Multi‐objective Approaches to the Unit Crewing Problem in Airline Crew Scheduling

Multi‐objective Approaches to the Unit Crewing Problem in Airline Crew Scheduling

Managing disruption risk in express logistics via proactive planning

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