An artificial bee colony algorithm approach for the team orienteering problem with time windows

Cura, Tunchan

doi:10.1016/j.cie.2014.06.004

Cited by 58 publications

(25 citation statements)

References 27 publications

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“…This concept was confirmed by Tricoire et al (2010) who modified (T)OP problem solving with limited time to get better results. Model TOPTW was used in Vansteenwegen et al (2009b) paper, Vansteenwegen et al (2009c, Labadie et al (2010), Montemanni et al (2011), Lin and Yu (2012), Hu and Lim (2014), Cura (2014), Gunawan et al (2015a), and Gunawan et al (2015b). The reason why the researchers used the same model was because they can compare the performance of the metaheuristic method in terms of result quality and run time.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

2019

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One of the problems tourism faces is how to make itineraries more effective and efficient. This research has solved the routing problem with the objective of maximizing the score and minimizing the time needed for the tourist's itinerary. Maximizing the score means collecting a maximum of various kinds of score from each destination that is visited. The profits differ according to whether those destinations are the favorite ones for the tourists or not. Minimizing time means traveling time and visiting time in the itinerary being kept to a minimum. Those are small case with 16 tourism destinations in East Java, and large case with 56 instances consists of 100 destinations each from previous research. The existing model is the Team Orienteering Problem with Time Window (TOPTW), and the development has been conducted by adding another objective, minimum time, become Flexible TOPTW. This model guarantees that an effective itinerary with efficient timing to implement will be produced. Modification of Iterated Local Search (ILS) into Adjustment ILS (AILS) has been done by replacing random construction in the early phase with heuristic construction, continue with Permutation, Reserved and Perturbation. This metaheuristic method will address this NP-hard problem faster than the heuristic method because it has better preparation and process. Contributing to this research is a multi-objective model that combines maximum score and minimum time, and a metaheuristics method to solve the problem faster and effectively. There are calibration parameter with 17 instances of 100 destinations each, small case test using Mixed Integer Linear Programming, and large case test comparing AILS with Multi-Start Simulated Annealing (MSA), Simulated Annealing (SA), Artificial Bee Colony (ABC), and Iterated Local Search. The result shows that the proposed model will provide itinerary with less number of visited destination 4.752% but has higher total score 8.774%, and 3836.877% faster, comparing with MSA, SA, and ABC. While AILS is compared with ILS, it has less visited destination 5.656%, less total score 56.291%, and faster 375.961%. Even though AILS has more efficient running time than other methods, it needs improvement in algorithm to create better result.

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Section: Literature Reviewmentioning

confidence: 99%

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

2019

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“…Compared to other methods the method proved to be effective and improved the results on 25 benchmark instances. Cura [29] propose an artificial bee colony approach to solve the TOPTW which achieves comparable results. Hu and Lim [65] propose an iterative three-component heuristic for the TOPTW.…”

Section: The Team Orienteering Problem (With Time Windows)mentioning

confidence: 99%

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

Verbeeck¹

2016

4OR-Q J Oper Res

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“…For each island, the artificial bee colony (ABC) algorithm first proposed by Karaboga [26], is selected as the search engine. The ABC algorithm was very competitive to EAs and other global optimization algorithms [27,28], and was successfully applied to many practical problems, such as IIR filters [29], team orienteering problem [30], portfolio optimization problem [31], etc. In order to enhance the efficiency and effectiveness of the ABC algorithm, the parallelization approaches were studied in [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

Xiang

2015

Applied Soft Computing

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An artificial bee colony algorithm approach for the team orienteering problem with time windows

Cited by 58 publications

References 27 publications

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

Solving multi-objective team orienteering problem with time windows using adjustment iterated local search

Optimizing practical orienteering problems with stochastic time-dependent travel times: towards congestion free routes

A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization

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