A multi-objective genetic algorithm for robust flight scheduling using simulation

Lee, Loo Hay; Lee, Chulung; Tan, Yen Ping

doi:10.1016/j.ejor.2005.12.014

Cited by 71 publications

(35 citation statements)

References 8 publications

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“…Therefore, we can obtain a solution set rather than only one solution using GA. Secondly, multi-objective genetic algorithm (MOGA) (see Deb et al, 2002) based on GA is quite suitable to solve NPHard multi-objective optimization problems. The existing research have shown that MOGA is effective in solving multi-objective problems in many fields (Mansouri, 2005;Tan et al, 2006;Lee et al, 2007Lee et al, , 2008Jozefowiez et al, 2009). Additionally, we improve the NSGA-II to enhance its effectiveness and efficiency.…”

Section: Improved Nondominated Sorting Genetic Algorithm II (Insga-ii)mentioning

confidence: 99%

A method for member selection of cross-functional teams using the individual and collaborative performances

Feng

Jiang

Fan

et al. 2010

European Journal of Operational Research

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a b s t r a c tThe member selection problem is an important aspect of the formation of cross-functional teams (CFTs). Selecting suitable members from a set of candidates will facilitate the successful task accomplishment. In the existing studies of member selection, the individual performance concerning a single candidate is mostly used, whereas the collaborative performance associating with a pair of candidates is overlooked. In this paper, as a solution to this problem, we propose a method for member selection of CFTs, where both the individual performance of candidates and the collaborative performance between candidates are considered. In order to select the desired members, firstly, a multi-objective 0-1 programming model is built using the individual and collaborative performances, which is an NP-hard problem. To solve the model, we develop an improved nondominated sorting genetic algorithm II (INSGA-II). Furthermore, a real example is employed to illustrate the suitability of the proposed method. Additionally, extensive computational experiments to compare INSGA-II with the nondominated sorting genetic algorithm II (NSGA-II) are conducted and much better performance of INSGA-II is observed.

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Section: Improved Nondominated Sorting Genetic Algorithm II (Insga-ii)mentioning

confidence: 99%

A method for member selection of cross-functional teams using the individual and collaborative performances

Feng

Jiang

Fan

et al. 2010

European Journal of Operational Research

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“…The problem then is solved implementing a branching algorithm. CSP is modeled as a multi-objective problem in Lee et al [4] with the objectives of minimizing the percentage of late arrivals, and flight-timecredit (FTC). The problem then is solved implementing PAES.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

Jolai

Assadipour

2010

Lecture Notes in Computer Science

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Abstract. Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multiobjective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

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“…In our algorithm, a number of parents were selected using the roulette wheel method [12] [13]. In this method, the probability of selecting an individual was proportional to its fitness.…”

Section: Selection and Crossovermentioning

confidence: 99%

“…In this work, a GA-based method was used because it performs well for largescale problems [12] [13] and could easily be integrated with existing scheduling algorithms. However, one problem of applying GA to the given scenario was the tendency of the algorithm to get trapped in local optima, leading to very suboptimal solutions.…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

Ayo¹

2017

JCC

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Adverse weather has serious implications for flight timeliness, as well as passenger and aircraft safety. This often implies that alternative flight paths have to be used by aircraft to avoid adverse weather. To reduce the impact of such path re-routes, exact techniques such as artificial potential field model and Dijkstra's algorithms have been proposed. However, such approaches are often unsuitable for real time scenarios involving large number of waypoints and constraints. This has led to the use of metaheuristic techniques that give sub-optimal solutions in good time. In this work, an improved genetic algorithm-based technique has been proposed. The algorithm used an improved mutation operator, reduced passenger inconvenience and considered the schedules of aircraft.

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A multi-objective genetic algorithm for robust flight scheduling using simulation

Cited by 71 publications

References 8 publications

A method for member selection of cross-functional teams using the individual and collaborative performances

A method for member selection of cross-functional teams using the individual and collaborative performances

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

An Improved Genetic Algorithm for Flight Path Re-Routes with Reduced Passenger Impact

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