Hybrid journal (It can contain Open Access articles) ISSN (Print) 1755-2176 - ISSN (Online) 1755-2184 Published by Inderscience Publishers[451 journals]
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Authors:Budi Santosa, Maria Krisnawati, Ahmad Rusdiansyah Pages: 307 - 329 Abstract: This paper presents a comparison of metaheuristics algorithms for solving crew rostering problem in airline company. Many optimisation methods have been developed to improve both roster quality and computational time. This paper proposes simple iterative mutation (SIMA) method to solve airline crew rostering problem. The proposed method is originated from genetic algorithm. Unlike genetics algorithm which is commonly used, the proposed simple iterative method consists of only three steps including initialisation, selection, and mutation. The method is applied to the datasets from Indonesia airline company, Merpati Nusantara Airline (MNA). To evaluate the performance of the proposed method, the results are compared to those of cross entropy, differential evolution, column generation and MOSI (method used by the airline) in minimising number of assigned crews to cover all of scheduled flights. From the experiments, SIMA method produced better result in term of roster quality and computational time. Keywords: airline crew rostering; genetic algorithm; roster quality; Indonesia Citation: International Journal of Metaheuristics, Vol. 7, No. 4 (2020) pp. 307 - 329 PubDate: 2020-12-03T23:20:50-05:00 DOI: 10.1504/IJMHEUR.2020.111598 Issue No:Vol. 7, No. 4 (2020)
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Authors:Dylan Gaspar, Yun Lu, Myung Soon Song, Francis J. Vasko Pages: 330 - 351 Abstract: The multiple-demand, multiple-choice, multi-dimensional knapsack problem (MDMMKP), defined by Lamine et al. (2012), is a generalisation of the classic 0-1 knapsack problem. The MDMMKP can easily be shown to be NP-hard. As usual, the objective of the MDMMKP is to maximise the value of objects placed in one knapsack. In this case, there are three categories of constraints. The constraints are multiple demand constraints, multiple-choice constraints, and multiple dimensional constraints. To our knowledge, there are no published solution methods designed to solve the MDMMKP, i.e., methods designed to handle all three categories of constraints in the same problem. In this paper, we develop several simple population-based metaheuristics that are founded on the teaching-learning-based optimisation (TLBO) metaheuristic (Rao et al., 2011) and the Jaya metaheuristic (Rao, 2016). It is important to note that both TLBO and Jaya were originally developed for continuous nonlinear engineering design problems. To test the performance of these metaheuristics, we will use 810 MDMMKP problem instances recently defined by Lu and Vasko (2019). The empirical results will be examined using statistical analyses. Keywords: multi-demand; multiple-choice; multi-dimensional knapsack problem; MDMMKP; Jaya metaheuristic; teaching-learning based optimisation metaheuristic; population-based metaheuristics; hybrid metaheuristics Citation: International Journal of Metaheuristics, Vol. 7, No. 4 (2020) pp. 330 - 351 PubDate: 2020-12-03T23:20:50-05:00 DOI: 10.1504/IJMHEUR.2020.111600 Issue No:Vol. 7, No. 4 (2020)
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Authors:Paulo Neis, Rhyd Lewis Pages: 352 - 378 Abstract: This paper aims to analyse the influence of parameter setup over a set of five heuristic methods applied to the graph colouring problem. Each heuristic is applied to a considerable set of problem instances, using a range of different parameter values. Multidimensional analysis is applied to extract and express knowledge about the performance of heuristic methods according to problem instance feature values, highlighting the effect of different parameter setups. The dynamic behaviour of the heuristics is also evaluated at different stages of execution (runtime), providing additional knowledge about speed of convergence/stagnation. Results demonstrate that it is possible to associate regions of the instance space in which problem instances exhibit particular features with specific parameter values yielding superior performance. Information relating runtime with average rate of solution improvement also suggests that certain instance features can be used to determine for how long the heuristics need to run before they converge or stagnate. Keywords: parameter tuning; algorithm performance; heuristics; graph colouring Citation: International Journal of Metaheuristics, Vol. 7, No. 4 (2020) pp. 352 - 378 PubDate: 2020-12-03T23:20:50-05:00 DOI: 10.1504/IJMHEUR.2020.111602 Issue No:Vol. 7, No. 4 (2020)
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Hybrid journal (It can contain Open Access articles)
[451 journals]
