A hybrid scatter search heuristic for personalized crew rostering in the airline industry

Maenhout, Broos; Vanhoucke, Mario

doi:10.1016/j.ejor.2010.01.040

Cited by 69 publications

(38 citation statements)

References 39 publications

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“…The heuristic methods can derive a reasonable solution with less computational effort. For instance, a genetic algorithm (Moudani et al, 2001;Lucic and Teodorovic, 2007;Sounai and Teghem, 2009), a simulated annealing algorithm (Lucic and Teodorovic, 2007), a tabu search (Lucic and Teodorovic, 2007) and a scatter search (Maenhout and Vanhoucke, 2010). These methods are sometimes effective with limited objectives and constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first one is the algorithm based on mathematical programming approaches. These methods utilize a set covering model (Moudani et al, 2001), a set partitioning model (Ryan, 1992;Gamache et al, 1998;Maenhout and Vanhoucke, 2010). The solution of these models is generally derived by branch and cut algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the constraints of the airline industry are much severer than the constraints of other transportation industries. Therefore, most of the conventional methods concentrate on the application of heuristic algorithms (Lucic and Teodorovic, 2007;Maenhout and Vanhoucke, 2010;Moudani et al, 2001;Souai and Teghem, 2009). However, it is difficult to evaluate the optimality of the solutions derived from these heuristic algorithms.…”

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confidence: 99%

“…The upper bound is improved by a metaheuristic algorithm. The effectiveness of the proposed method for a large scale rostering problem is shown from computational experiments.Application of two-phase decomposition algorithm to practical airline crew rostering problem for fair working time cost (Moudani et al, 2001;Maenhout and Vanhoucke, 2010), minimizing the most important cost (Guo et al, 2006), maximizing the utility of the rosters (Dawid et al, 2001), and the main objective is to minimize the operational cost and the second objective is to minimize the deviation of the working time (Souai and Teghem, 2009). Few works consider the crew satisfaction as an objective function.…”

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Application of two-phase decomposition algorithm to practical airline crew rostering problem for fair working time

Doi

Nishi

2016

JAMDSM

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IntroductionAutomating cost-efficient scheduling in the airlines is getting more and more significant in recent years. As new low cost carriers come to appear, it is highly required for traditional full service carriers to reduce the operational costs. In the airlines, there are many types of scheduling problems in order to operate their flights. These problems are commonly categorized into flight scheduling design, fleet assignment, aircraft maintenance routing and crew scheduling (Dawid et al., 2001). In these problems, crew scheduling problems are well-known as one of the most difficult problems in the airlines (Guo et al., 2006). There are commonly two phases in the airline crew scheduling problems: the crew pairing problems and the crew rostering problems. In the crew pairing problems, a crew pairing that is a sequence of flight duties is allocated. Pairings are generated such that all of the duties are covered by the least number of pairings. The pairing also originates and terminates at the crew base. In the crew rostering problems, each pairing, holidays and reserved duties are assigned to each crew member satisfying all hard constraints such as rest days, rules and regulations. The reserved duties are trainings, medical appointments and so on. In this paper, we study a static crew rostering problem when the set of crew pairings is given in advance. It is hard to solve the crew rostering problems due to a large number of hard constraints. However, it is still important to obtain a better schedule because the quality of the solution has a direct impact on the operational costs in the airline industry.The constraints of the airline crew rostering problems differ for each airline company or each case study. For instance, there are consecutive flight days constraints (Gamache et al., 1998), rest days constraints (Souai and Teghem, 2009), total working time constraints (Moudani et al., 2001;Souai and Teghem, 2009). The problem in this paper additionally considers the practical constraints on consecutive rest days after an international flight, prohibition of assignment of crew members with the same group to the same duty. Most of the conventional studies do not emphasize on these practical constraints. The objective function of the problem also differs for each case. For instance, minimizing the operational 1 Tsubasa DOI * and Tatsushi NISHI * * Graduate School of Engineering Science, Osaka University 1-3 Machikaneyama-cho, Toyonaka city 560-8531, Japan E-mail: nishi@sys.es.osaka-u.ac.jp Received 15 November 2015 AbstractWe propose an application of a two-phase decomposition algorithm for a practical airline crew rostering problem for fair working time. The problem is to find an optimal assignment of duties to individual crew members such that various hard constraints such as rest days, rules and regulations are satisfied. The objective is to minimize the total deviation of the average working time from the standard working time for crew members. A two-phase decomposition algorithm is successfully applied to ...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

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Application of two-phase decomposition algorithm to practical airline crew rostering problem for fair working time

Doi

Nishi

2016

JAMDSM

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“…The exact methods are only used for small-sized problems (Andersson et al, 1998;Barnhart et al, 2003;Cabral et al, 2000;Kohn and Karisch, 2004;Gopalakrishnan and Johnson, 2005;Lucic and Teodorovic, 2007;Maenhout and Vanhoucke, 2010).…”

Section: Introductionmentioning

confidence: 99%

Heuristics to solve the integrated airline crew assignment problem

Gomes

Gualda

2015

J. Transp. Lit.

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Article Info AbstractKeywords: air transportation airline crew assignment combinatorial optimization heuristics A typical problem related to airline crew management consists of optimally assigning the required crew members to planned flights for a given period of time, while complying with a variety of labor regulations, safety rules and policies of the airline. This problem, called crew assignment problem (CAP), is of the NPHard class. So, it is usually divided into two independent subproblems, crew pairing problem (CPP) and crew rostering problem (CRP), modeled and solved sequentially. This division does not provide a global treatment to the CAP in terms of total cost and quality of the final solution. IntroductionThis paper presents the main results of a research addressed to solve the crew assignment problem (CAP) with a proposed heuristic procedure. The importance of this type of approach is given by the combinatorial nature of the problem, which turns it impossible to be solved through exact models for large instances. The proposed heuristics presented results equal to or better than those obtained by exact models for the tested instances related to small and medium-sized Brazilian airlines (up to 894 flights and 36 crew members), with reduced CPU times (less than 5 seconds). Exact models led to feasible solutions in instances with up to 656 flights and 20 crew members.The CAP treated in this paper is defined as the problem of assigning the required crew members of the same category (only technical crew, in this case) to a set of flights of a given aircraft type, such that it minimizes the total cost of the aircrew, taking into consideration the proper legislation and the satisfaction of the crew members.CAP has been decomposed in the literature into two subproblems, both of which are solved sequentially: crew pairing problem (CPP) and crew rostering problem (CRP). The CPP seeks to provide an optimal set of pairings that covers all the planned flights. Then, in the CRP, the best combination of rosters (composed by the pairings of CPP and other activities) to crew members is determined, seeking the optimal coverage of planned flights and, eventually, the balancing of the total flying time among the crew members (Andersson et al., 1998;Barnhart et al., 2003; Kohl and Karisch, 2004;Gopalakrishnan and Johnson, 2005).Pairing (or crew rotation) is the work accomplished by crew member starting and ending at the same home base, featuring a cycle. A pairing can be formed by one or more duty periods, which are series of sequential flights comprising a workday for a crew member.Subproblems CPP and CRP are usually modelled as a set partitioning (or covering) problem, and solved through approaches based on heuristics or metaheuristics. The exact methods are only used for small-sized problems (Andersson et al., 1998;Barnhart et al., 2003;Cabral et al., 2000;Kohn and Karisch, 2004;Gopalakrishnan and Johnson, 2005;Lucic and Teodorovic, 2007;Maenhout and Vanhoucke, 2010).This decomposition does not incorporate all the cre...

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