Airline crew rostering problem using particle swarm optimization

Limlawan, V.; Kasemsontitum, Boontariga; Jeenanunta, Chawalit

doi:10.1109/icqr.2011.6031590

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…In our previous work 14 , we implemented the discrete binary PSO to solve an ACRP. In that implementation, a particle is represented as a binary timetable of crew members in which the rows and the columns represent crews and pairs, respectively.…”

Section: Solution Representationmentioning

confidence: 99%

“…The PSO-IH is developed based on PSO for an ACRP 14 . The structure of the algorithms is the same, but the particle can improve itself.…”

Section: Proposed Hybrid Pso With Improvement Heuristic (Pso-ih)mentioning

confidence: 99%

“…The metaheuristic approach which is focused on here has been applied to solve ACRPa. Examples of this method are simulated annealing 11 , genetic algorithm [11][12][13] , Tabu search 11 and particle swarm optimization 14 . Moreover, there are hybrids of the two metaheuristic techniques for enhancing the performance of the algorithm such as the hybrid between genetic algorithm and simulated annealing 15 and the hybrid scatter search heuristic 16 .…”

Section: Introductionmentioning

confidence: 99%

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A hybrid particle swarm optimization and an improved heuristic algorithm for an airline crew rostering problem

Limlawan¹,

Kasemsontitum²,

Jeenanunta³

2014

ScienceAsia

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The airline crew scheduling problem is a combinatorial optimization problem and belongs to the class of NPhard problems. An effective method for solving the airline crew scheduling problem can reduce the crew costs and improve crew satisfaction. Because of its complexity, the problem is divided into two subproblems: the crew pairing problem and the crew rostering problem. In this paper, the crew rostering problem is focused on and the objective is to generate a fairness timetable in which the workloads are distributed among each crew equally. We propose a hybrid particle swarm optimization (PSO) and an improvement heuristic (IH) to solve this problem. The IH is designed to improve the standard deviation of the workloads by picking a workload from the high workload crew and assigning it to the low workload crew. The IH improves the solution of the particle after the particle changes position each generation. The proposed algorithm is tested on actual pairing data from Thai Airways and is compared with PSO without IH and the multi-commodity network flow approach. With the combination of PSO and IH, the algorithm can improve the quality of the solution by more than 20% in most cases, and PSO with IH also outperforms the network approach in 6 out of 9 cases and especially in the large size cases for which the network approach cannot find a feasible solution.

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Section: Solution Representationmentioning

confidence: 99%

“…The PSO-IH is developed based on PSO for an ACRP 14 . The structure of the algorithms is the same, but the particle can improve itself.…”

Section: Proposed Hybrid Pso With Improvement Heuristic (Pso-ih)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid particle swarm optimization and an improved heuristic algorithm for an airline crew rostering problem

Limlawan¹,

Kasemsontitum²,

Jeenanunta³

2014

ScienceAsia

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“…Also, Souai and Teghem (2009) [32] proposed an approach based on genetic algorithm to integrate airline crew pairing and rostering problem: Their purpose is to solve these two problems simultaneously. Limlawan et al (2011) [27] proposed a particle swarm optimization to solve the airline crew rostering problem by assigning appropriate balance workload for each crew member. Furthermore, Zeren and Ozkol (2012) [5] proposed a solution to the crew pairing problem based on genetic algorithms with construction of a genetic operator.…”

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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