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Experimental Analysis of Meta-Heuristic Algorithms for Moving Customer Vehicle Routing Problem

Year 2021, Volume: 36 Issue: 1, 459 - 476, 01.12.2020

Ukbe Usame Uçar , Selçuk Kürşat İşleyen , Hadi Gökçen

https://doi.org/10.17341/gazimmfd.609418
Cited By: 2

Abstract

Unmanned Combat Aerial Vehicles are defense systems
based on artificial intelligence which is intensively used by many countries to
provide national security on military operations. By means of these systems,
moving or non-moving threat factors in the operation field could be destroyed
under harsh and challenging geographical conditions without requiring a pilot
with the help of a control center. In fleet operations, the necessity of
destroying moving targets successfully under constraints of the endurance,
munition capacity, time window and fuel cost of unmanned combat aerial vehicles
brings out the moving customer-vehicle routing problem. In this study,
Heterogeneous Fleet-Moving Customer Vehicle Routing Problem with Time Windows
under constraint of vehicle capacity (endurance) has been aimed to be solved
considering the minimum operation time and cost. In order to solve the problem,
heuristic algorithms (ÇARA, RASA)   were
developed and metaheuristic algorithms (Genetic Algorithm, NSGA-II and
Simulated Annealing) were used. The effectiveness of the proposed algorithms
was tested on 30 different experimental sets with the number of pursuers
ranging from 5-10 and the number of targets ranging from 10-35. Taguchi method
was used to determine the appropriate parameter set for the algorithms. As a
result of the analysis, it has been found that Genetic Algorithm produces much
better results than other algorithms.

Keywords

Moving Customer Vehicle Routing Problem Simulated Annealing Pareto Optimization Genetic Algorithm NSGA-II

References

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Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi

Year 2021, Volume: 36 Issue: 1, 459 - 476, 01.12.2020

Ukbe Usame Uçar , Selçuk Kürşat İşleyen , Hadi Gökçen

https://doi.org/10.17341/gazimmfd.609418
Cited By: 2

Abstract

Silahlı İnsansız Hava Araçları, birçok ülkenin
ulusal güvenliğini sağlamak adına askeri operasyonlarda yoğun bir şekilde
kullandıkları yapay zekâya dayalı savunma sistemleridir. Bu sistemler sayesinde
operasyon alanındaki hareketli ve hareketsiz hedefler, zorlu coğrafik koşullar
altında pilot kullanılmaksızın kumanda merkezi yardımıyla imha edilebilmektedir.
İnsansız hava aracı filosu tarafından, seyir süresi, mühimmat kapasitesi, yakıt
maliyeti ve zaman penceresi kısıtlamaları dikkate alınarak sistemdeki hareketli
hedeflerin başarılı bir şekilde imha edilmesi gereksinimi, Hareketli Müşterili
Araç Rotalama Problemini ortaya çıkarmaktadır. Bu çalışmada Heterojen
Filolu-Zaman Pencereli-Kapasite Kısıtlı Hareketli Müşterili Araç Rotalama
Problemi, minimum görev süresi ve görev maliyeti amaçları doğrultusunda
çözülmesi amaçlanmıştır. Problemin çözümü için sezgisel algoritmalar(ÇARA, RASA)  geliştirilmiş ve metasezgisel algoritmalar
(Genetik Algoritma, NSGA-II ve Tavlama Benzetimi Algoritması) kullanılmıştır. Önerilen
algoritmaların etkinliği vurucu sayısının 5-10, hedef sayısının 10-35 arasında
değiştiği 30 farklı deney seti üzerinde test edilmiştir. Algoritmalar için
uygun parametre setinin belirlenmesinde Taguchi yönteminden yararlanılmıştır. Analiz
sonucunda Genetik Algoritmanın diğer algoritmalara kıyasla daha başarılı
sonuçlar ürettiği tespit edilmiştir.

Keywords

Hareketli müşterili araç rotalama problemi Tavlama Benzetimi Pareto Optimizasyonu Genetik Algoritma NSGA-II

References

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  • [2]. Bertsimas, D. J., Van Ryzin, G., A stochastic and dynamic vehicle routing problem in the Euclidean plane, Operations Research, 39(4), 601-615, 1991.
  • [3]. Bertsimas, D. J., Van Ryzin, G., Stochastic and dynamic vehicle routing in the Euclidean plane with multiple capacitated vehicles, Operations Research, 41(1), 60-76, 1993.
  • [4]. Liao, T. Y., On‐line vehicle routing problems for carbon emissions reduction, Computer‐Aided Civil and Infrastructure Engineering, 2017.
  • [5]. Haghani, A., Jung, S., A dynamic vehicle routing problem with time-dependent travel times, Computers & operations research, 32(11), 2959-2986, 2005.
  • [6]. Novoa, C., Storer, R., An approximate dynamic programming approach for the vehicle routing problem with stochastic demands, European Journal of Operational Research, 196(2), 509-515, 2009.
  • [7]. Haghani, A., Jung, S., A dynamic vehicle routing problem with time-dependent travel times, Computers & operations research, 32(11), 2959-2986, 2005.
  • [8]. Potvin, J. Y., Xu, Y., Benyahia, I., Vehicle routing and scheduling with dynamic travel times, Computers & Operations Research, 33(4), 1129-1137, 2006.
  • [9]. Van Woensel, T., Kerbache, L., Peremans, H., Vandaele, N., Vehicle routing with dynamic travel times: A queueing approach, European Journal of Operational Research, 186(3), 990-1007, 2008.
  • [10]. Taniguchi, E., Shimamoto, H., Intelligent transportation system based dynamic vehicle routing and scheduling with variable travel times, Transportation Research Part C: Emerging Technologies, 12(3-4), 235-250, 2004.
  • [11]. Ning, T., Guo, C., Chen, R., A novel method for dynamic vehicle routing problem, The Open Cybernetics & Systemics Journal, 9(1), 2015.
  • [12]. Chen, S., Chen, R., Gao, J., A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem, Algorithms, 10(3), 107, 2017.
  • [13]. Kilby, P., Prosser, P., Shaw, P., Dynamic VRPs: A study of scenarios, University of Strathclyde Technical Report, 1-11, 1998.
  • [14] Ritzinger, U., Puchinger, J., Hartl, R. F. A survey on dynamic and stochastic vehicle routing problems, International Journal of Production Research, 54(1), 215-231, 2016.
  • [15]. Ries, J., Ishizaka, A., A Multi-Criteria Support System for Dynamic Aerial Vehicle Routing Problems, In Communications, Computing and Control Applications (CCCA), 2012 2nd International Conference on (pp. 1-4). IEEE, December, 2012.
  • [16]. Karakaya, M. En Az Sayıda İnsansız Hava Aracı Kullanarak Sabit Hedeflerin Gözetlenmesinin Planlanması. https://docplayer.biz.tr/4654060-En-az-sayida-insansiz-hava-araci-kullanarak-sabit-hedeflerin-gozetlenmesinin-planlanmasi.html Erişim tarihi Ağustos 9, 2019.
  • [17]. Innocenti, M., Pollini, L., Turra, D., A fuzzy approach to the guidance of unmanned air vehicles tracking moving targets. IEEE Transactions on Control Systems Technology, 16(6), 1125-1137, 2008.
  • [18]. Morris, S., Frew, E. W., Jones, H., Cooperative tracking of moving targets by teams of autonomous unmanned air vehicles, MLB CO MOUNTAIN VIEW CA, 2005.
  • [19]. Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M. D., Camacho, D., Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms, Soft Computing, 21(17), 4883-4900, 2017.
  • [20]. Savuran, H., & Karakaya, M., Efficient route planning for an unmanned air vehicle deployed on a moving carrier, Soft Computing, 20(7), 2905-2920, 2016.
  • [21]. Shima, T., Schumacher, C., Assignment of Cooperating UAVs to Simultaneous Tasks using Genetic Algorithms, In AIAA Guidance, Navigation, and Control Conference and Exhibit (p. 5829), (2005, August)..
  • [22]. Kim, J., Morrison, J. R., On the concerted design and scheduling of multiple resources for persistent UAV operations, Journal of Intelligent & Robotic Systems, 74(1-2), 479-498, 2014.
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  • [24]. Fügenschuh, A., Knapp, M., Rothe, H., The multiple traveling salesmen problem with moving targets. Helmut-Schmidt-Univ., Professur für Angewandte Mathematik, 2014.
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  • [26]. Stieber, A., Fügenschuh, A., Variants in Modeling Time Aspects for the Multiple Traveling Salesmen Problem with Moving Targets, 2016.
  • [27]. Jiang, Q., Sarker, R., Abbass, H., Tracking moving targets and the non-stationary traveling salesman problem, Complexity International, 11(2005), 171-179, 2005.
  • [28]. Jindal, P., Kumar, A., Multiple Target Intercepting Traveling Salesman Problem, International Journal of Computer Science and Technology, 2(2), 327-331, 2011.
  • [29]. Englot, B., Sahai, T., Cohen, I., Efficient Tracking and Pursuit of Moving Targets by Heuristic Solution of the Traveling Salesman Problem, In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on (pp. 3433-3438). IEEE, December, 2013.
  • [30]. Jindal, P., Kumar, A., Kumar, S., Dynamic version of Traveling Salesman Problem, International Journal of Computer Applications (0975–8887), 19(1), 2011.
  • [31]. Khosravi, M., Aghdam, A. G., Cooperative Receding Horizon Control for Multi-Target İnterception in Uncertain Environments, In Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on (pp. 4497-4502). IEEE, December, 2014.
  • [32]. Zhou, A., Kang, L., Yan, Z. Solving Dynamic TSP with Evolutionary Approach in Real Time, In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 2, pp. 951-957). IEEE, December, 2013.
  • [33]. Choubey, N. S., Moving Target Travelling Salesman Problem using genetic algorithm, International Journal of Computer Applications, 70(2), 2013.
  • [34]. Lee, Z. J., Lee, C. Y., Su, S. F., An immunity-based ant colony optimization algorithm for solving weapon–target assignment problem Applied Soft Computing, 2(1), 39-47, 2002.
  • [35]. Agharkar, P., Bullo, F., Vehicle routing algorithms to intercept escaping targets. In American Control Conference (ACC), 2014 (pp. 952-957). IEEE, June, 2014.
  • [36]. Knapp, M., Rothe, H., Concept for simulating engagement strategies for C-RAM systems using laser weapons. Proceedings of the DMMS, 2012.
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There are 83 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ukbe Usame Uçar 0000-0002-9872-2890

Selçuk Kürşat İşleyen 0000-0002-0113-1083

Hadi Gökçen 0000-0002-5163-0008

Publication Date December 1, 2020
Submission Date August 22, 2019
Acceptance Date September 23, 2020
Published in Issue Year 2021 Volume: 36 Issue: 1

Cite

APA Uçar, U. U., İşleyen, S. K., & Gökçen, H. (2020). Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(1), 459-476. https://doi.org/10.17341/gazimmfd.609418
AMA Uçar UU, İşleyen SK, Gökçen H. Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi. GUMMFD. December 2020;36(1):459-476. doi:10.17341/gazimmfd.609418
Chicago Uçar, Ukbe Usame, Selçuk Kürşat İşleyen, and Hadi Gökçen. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, no. 1 (December 2020): 459-76. https://doi.org/10.17341/gazimmfd.609418.
EndNote Uçar UU, İşleyen SK, Gökçen H (December 1, 2020) Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 1 459–476.
IEEE U. U. Uçar, S. K. İşleyen, and H. Gökçen, “Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi”, GUMMFD, vol. 36, no. 1, pp. 459–476, 2020, doi: 10.17341/gazimmfd.609418.
ISNAD Uçar, Ukbe Usame et al. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/1 (December 2020), 459-476. https://doi.org/10.17341/gazimmfd.609418.
JAMA Uçar UU, İşleyen SK, Gökçen H. Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi. GUMMFD. 2020;36:459–476.
MLA Uçar, Ukbe Usame et al. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 36, no. 1, 2020, pp. 459-76, doi:10.17341/gazimmfd.609418.
Vancouver Uçar UU, İşleyen SK, Gökçen H. Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi. GUMMFD. 2020;36(1):459-76.

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