Crew pairing optimization based on hybrid approaches

Aydemir-Karadag, Ayyuce; Dengız, Berna; Bolat, Ahmet

doi:10.1016/j.cie.2011.12.005

Cited by 25 publications

(13 citation statements)

References 26 publications

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“…The results showed that the new operator dramatically improved the convergence rate. Aydemir-Karadag et al (2013) used SCP to formulate CPP and introduced three algorithms to solve it. The comparison of the randomly generated data set problems confirmed the better function of the last two algorithms compared to the first one.…”

Section: Literature Reviewmentioning

confidence: 99%

A new mathematical model to cover crew pairing and rostering problems simultaneously

Saemi

Komijan

Tavakkoli–Moghaddam³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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Crew scheduling problem includes two separate subproblems, namely, crew pairing and crew rostering problems. Solving these two subproblems in a sequential order may not lead to an optimal solution. This study includes two main novelties. It combines these two subproblems and presents them in a single model. On the other hand, despite previous researches that considered a pairing continuously, the proposed model benefits from the capability of considering one or more days off in a pairing assigned to a crew member. This is extremely useful as it enables the crew to participate in required courses, doing medical checks, etc. Two solution approaches, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to solve the model. Eventually, the performance of the proposed algorithms is evaluated. Both ended to satisfactory results; however, PSO relatively outperformed GA in terms of solution optimality and computational time.

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Section: Literature Reviewmentioning

confidence: 99%

A new mathematical model to cover crew pairing and rostering problems simultaneously

Saemi

Komijan

Tavakkoli–Moghaddam³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…The solution to this problem is dealt with, for example, in [10], where the authors formulate the problem as a business traveler task, which they then solve using the ant colony algorithm. The authors of the paper compare the different methods for solving the issue of crew pairing [11]. These are the Knowledge Based Random Algorithm (KBRA), a hybrid algorithm using genetic algorithms and the column generation method.…”

Section: Crew Schedulingmentioning

confidence: 99%

Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation

et al. 2021

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The creation of a flight schedule and the associated crew planning are clearly among the most complicated tasks in terms of traffic preparation. Even with a relatively small number of pilots and aircraft, numerous specific constraints arising from real operations must be included in the calculation, thus increasing the complexity of the planning process. However, even in a precision-planned operation, non-standard situations often occur, which must be addressed flexibly. It is at this point that an operational solution must be applied, the aims of which are to stabilize the flight schedule as soon as possible and minimize the financial impacts resulting from the non-standard situation. These problems are resolved by the airline’s Operational Control Center, which also uses various software approaches to solve the problem. The problem is approached differently by large air carriers, which use software products to address it, and small and medium-sized air carriers, which resolve the issue of operational rescheduling intuitively, based on the experience of dispatchers. However, this intuitive approach can lead to inaccuracies that can lead to unnecessary financial losses. In this paper, we present an optimization model that can serve as a tool to support the decision-making of employees of the operations centers of smaller and medium-sized air carriers.

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“…For instance, the paper of AydemirKaradag et al (2013) [2] aimed to propose a hybrid method which uses meta-heuristic to reduce the search space and then uses an exact method to have the solution. Also, Hanafi and Kozan (2014) [19] used a constructive heuristic coupled with the Simulated Annealing algorithm to solve the problem of railway crew scheduling.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization algorithm for solving airline crew scheduling problem

Ezzinbi

Sarhani

Afia

et al. 2014

2014 International Conference on Logistics Operations Management

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In air transport, the cost related to crew members presents one of the most important cost supported by airline companies. The objective of the crew scheduling problem is to determine a minimum-cost set of pairings so that every flight leg is assigned a qualified crew and every pairing satisfies the set of applicable work rules. In this paper, we propose a solution for the crew scheduling problem with Particle Swarm Optimization (PSO) algorithm, this solution approach is compared with the Genetic Algorithm (GA) for both crew pairing and crew assignment problems which are the two part of crew scheduling problem.

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Crew pairing optimization based on hybrid approaches

Cited by 25 publications

References 26 publications

A new mathematical model to cover crew pairing and rostering problems simultaneously

A new mathematical model to cover crew pairing and rostering problems simultaneously

Dynamic Model of Contingency Flight Crew Planning Extending to Crew Formation

Particle swarm optimization algorithm for solving airline crew scheduling problem

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