A multi-commodity flow approach for aircraft routing and maintenance problem

Jeenanunta, Chawalit; Kasemsontitum, Boontariga; Noichawee, Tawinan

doi:10.1109/icqr.2011.6031699

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Step 7: Change the position of each particle by using (10) and (11) Step 8: Repeat Step 2 until stopping criterion is met.…”

Section: Methodsmentioning

confidence: 99%

“…The disadvantage of the heuristic methods is the quality of the solution and the flexibility in response to the different ACRPs. An ACRP is formulated as a generalized set-partitioning model and solved by branch and bound 6 , column generation technique 7 , branch and cut 8 , or multi-commodity flow approaches 9,10 . Although the exact approaches can give the optimal solution, the approaches cannot tackle the largescale ACRP.…”

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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“…Step 7: Change the position of each particle by using (10) and (11) Step 8: Repeat Step 2 until stopping criterion is met.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Limlawan¹,

Kasemsontitum²,

Jeenanunta³

2014

ScienceAsia

Self Cite

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show abstract

“…The aircraft scheduling is a controlling work of production scheduling in airlines. Because of the importance and complexity of the aircraft scheduling work in the air transport, the in-depth research and application have been carried out in aviation developed countries of Europe and America [1][2][3]. In China, because airlines had small amount of aircrafts a few years ago, they have not paid more attention to production plan and management and their planning mode was simple and manned.…”

Section: Introductionmentioning

confidence: 99%

Study on Fleet Assignment Problem Model and Algorithm

Tan

2013

Mathematical Problems in Engineering

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The Fleet Assignment Problem (FAP) of aircraft scheduling in airlines is studied, and the optimization model of FAP is proposed. The objective function of this model is revenue maximization, and it considers comprehensively the difference of scheduled flights and aircraft models in flight areas and mean passenger flows. In order to solve the model, a self-adapting genetic algorithm is supposed to solve the model, which uses natural number coding, adjusts dynamically crossover and mutation operator probability, and adopts intelligent heuristic adjusting to quicken optimization pace. The simulation with production data of an airline shows that the model and algorithms suggested in this paper are feasible and have a good application value.

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“…within a network that consists of interconnected hubs and where the transport of a commodity occurs between its source and destination node. Variants of the SNDP have been successfully applied to many problems in, e.g., road transportation planning [105,42,139], railway planning [14,102,10,178], flight scheduling [80,19,106] and telecommunication [116,115,111]. This chapter contributes to the academic literature in the following ways.…”

Section: Introductionmentioning

confidence: 99%

Improving supply chain performance

Rajabighamchi

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I am very thankful for my Iranian colleagues, Ashkan and Arman. Spending time together with our Iranian colleagues in the Netherlands has been a source of comfort and camaraderie, especially as we navigate the challenges of being away from our homeland. The shared language and culture create a unique bond that goes beyond professional collaboration. Whether it's sharing stories over a cup of Persian tea, celebrating cultural festivities, or simply supporting each other during moments of homesickness, the sense of community we've cultivated is invaluable. In these shared moments, we find solace and connection, making our workplace feel like a home away from home. Contents

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A multi-commodity flow approach for aircraft routing and maintenance problem

Cited by 5 publications

References 17 publications

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

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

Study on Fleet Assignment Problem Model and Algorithm

Improving supply chain performance

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