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Emden-Weinert, Thomas; Proksch, Mark

doi:10.1023/a:1009632422509

Cited by 36 publications

(2 citation statements)

References 22 publications

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“…The crew scheduling problem in the literature is solved by using column generation algorithm, metaheuristics based on genetic algorithm and simulated annealing algorithm technics. Yan and Tu (2002) with a network model [13], Emden-Weinert and Proksch (1999) with simulated annealing [14], Caviqu et al (1999) with tabu search algorithm [15] solved the crew scheduling problem. Vance et al (1997) using Dantzig-Wolfe decomposition algorithm in the first stage of the flight duty periods and in the second stage by getting pairing from the duty period, provided a tighter linear programming boundary according to the set partitioning [16].…”

Section: Introductionmentioning

confidence: 99%

Bi-Objective Goal Programming for Airline Crew Pairing

ORHAN

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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The crew cost constitutes 20% of the direct operating cost in airline operations after the aircraft fuel cost. Effective crew scheduling can save tens of millions of dollars to the airlines and result in low-cost flight tickets for the passengers and improved quality of life for the crew. The crew pairing requires addressing union expectations, company rules, regulations of countries' civil aviation authorities. In this study, bi-objective goal programming is proposed to minimize the number of crew to perform flights and minimize the flight pairings cost while addressing the challenging issues mentioned above. The integer set-covering goal programming model is formulated and solved using GAMS mathematical programming software. The results showed that the bi-objective model could provide significant cost advantages to airlines. The computational experiments have been performed over a set of real data.

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Section: Introductionmentioning

confidence: 99%

Bi-Objective Goal Programming for Airline Crew Pairing

ORHAN

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…In addition, meta-heuristics has nowadays become a popular method because of its good performance in solving the complicated, large-scale real-world problems although they usually only find the near-optimal solutions. Regarding their application in crew scheduling problems, Emden-Weinert et al [11] used a simulated annealing approach to solve the airline crew scheduling problem, and Dias et al [12] proposed a genetic algorithm for bus driver scheduling problem. Moreover, Elizondo et al [13] presented a constructive hybrid method to address the URCSOP, and provided better results with regard to idle time than both the hybrid and greedy methods.…”

Section: Introductionmentioning

confidence: 99%

Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

Zhou

Yang

Deng

et al. 2016

PROMET

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Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time.

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