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

Yıl 2021, Cilt: 36 Sayı: 1, 459 - 476, 01.12.2020

Ukbe Usame Uçar Selçuk Kürşat İşleyen Hadi Gökçen

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

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

Yıl 2021, Cilt: 36 Sayı: 1, 459 - 476, 01.12.2020

Ukbe Usame Uçar Selçuk Kürşat İşleyen Hadi Gökçen

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

  • [1]. Psaraftis, H. N., Dynamic vehicle routing: status and prospects, Annals Of Operations Research, 61(1), 143-164, 1995.
  • [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.
  • [23]. Helvig, C. S., Robins, G., & Zelikovsky, A., Moving-target TSP and related problems, In European Symposium on Algorithms (pp. 453-464), Springer Berlin Heidelberg, (1998, August).
  • [24]. Fügenschuh, A., Knapp, M., Rothe, H., The multiple traveling salesmen problem with moving targets. Helmut-Schmidt-Univ., Professur für Angewandte Mathematik, 2014.
  • [25]. Stieber, A., Fügenschuh, A., Epp, M., Knapp, M., & Rothe, H., The multiple traveling salesmen problem with moving targets, Optimization Letters, 9(8), 1569-1583, 2015.
  • [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.
  • [37]. Hammar, M., Nilsson, B. J., Approximation results for kinetic variants of TSP, In International Colloquium on Automata, Languages, and Programming (pp. 392-401). Springer Berlin Heidelberg, (1999, July).
  • [38]. Bengt, J., Approximation Results for Kinetic Variants of TSP, Discrete & Computational Geometry, 4(27), (2002).
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  • [49]. Del Bimbo, A., & Pernici, F., Distant targets identification as an on-line dynamic vehicle routing problem using an active-zooming camera. In Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on (pp. 97-104). IEEE, October, 2005.
  • [50]. Bimbo, A. D., & Pernici, F., Saccades Planning with Kinetic TSP for Distant Targets İdentification, In Imaging for Crime Detection and Prevention, 2005. ICDP 2005. The IEE International Symposium on (pp. 145-149). IET, June, 2005.
  • [51]. Ilavarasi, K., & Joseph, K. S., Variants of travelling salesman problem: A survey. In Information Communication and Embedded Systems (ICICES), 2014 International Conference on (pp. 1-7). IEEE, February, 2004.
  • [52]. Asahiro, Y., Horiyama, T., Makino, K., Ono, H., Sakuma, T., Yamashita, M, How to collect balls moving in the Euclidean plane, Electronic Notes in Theoretical Computer Science, 91, 229-245, 2004.
  • [53]. Asahiro, Y., Miyano, E., Shimoirisa, S., Grasp and delivery for moving objects on broken lines, Theory of Computing Systems, 42(3), 289-305, 2008.
  • [54]. Chalasani, P., Motwani, R., Rao, A. N. I. L., Algorithms for robot grasp and delivery, In 2nd International Workshop on Algorithmic Foundations of Robotics, (1996, July).
  • [55]. Uçar, U , İşleyen, S., A New Solution Approach for UAV Routing Problem with Moving Target – Heterogeneous Fleet. Journal of Polytechnic, (), 0-0, 2019. DOI: 10.2339/politeknik.466393
  • [56]. Uçar, U.Ü., Işleyen, S.K., A solution approach based on simulated annealing for the destruction of moving targets in time window by air operations, 8th InternationalAdvanced Technologies Symposium(IATS) 19-22 October 2017.
  • [57]. Isleyen, S. K., Uçar, U., Balo, F., A New Solution Approach for Maritime Surveillance Operation: The Case of Aegean Sea, Mathematical Problems in Engineering. 2019, 1-16, 2019.
  • [58]. Papadakos, N., Tzallas-Regas, G., Rustem, B., Thoms, J., Risky traveling salesman problem. European Journal of Operational Research, 212(1), 69-73, 2011.
  • [59]. Bullo, F., Frazzoli, E., Pavone, M., Savla, K., & Smith, S. L., Dynamic Vehicle Routing for Robotic Systems. Proceedings of the IEEE, 99(9), 1482-1504, 2011.
  • [60]. 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.
  • [61]. Enright, J., Frazzoli, E., Savla, K., & Bullo, F., On multiple UAV routing with stochastic targets: Performance bounds and algorithms. In AIAA Guidance, Navigation, and Control Conference and Exhibit (p. 5830), August, 2005.
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  • [67]. Murray, C. C., Dynamic reassignment and rerouting in cooperative airborne operations, State University of New York at Buffalo, 2010.
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  • [76]. Temiz, İ., Erol, S., Multıobjectıve genetıc algorıthm for fuzzy flowshop schedulıng problem, Journal of the Faculty of Engineering and Architecture of Gazi University, 22(4), 2007.
  • [77]. Lokman, B., Çok amaçlı tamsayı programlama problemleri için temsili çözüm üreten yaklaşımların ve kalite ölçülerinin incelenmesi, Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers), 28(1), 2017.
  • [78]. Durmaz, E. D., Çok amaçlı tek sıra tesis düzenleme probleminin çözümü için NSGA II ve hedef programlama yaklaşımı, Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, 2016.
  • [79]. Sağ, T., Çok kriterli optimizasyon için genetik algoritma yaklaşımları, Yüksek Lisans Tezi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, (2018).
  • [80]. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. A. M. T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197, 2002.
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  • [83]. Talbi, El-Ghazali, Metaheuristics: From Design to İmplementation. John Wiley & Sons, 2009.
Toplam 83 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Ukbe Usame Uçar 0000-0002-9872-2890

Selçuk Kürşat İşleyen 0000-0002-0113-1083

Hadi Gökçen 0000-0002-5163-0008

Yayımlanma Tarihi 1 Aralık 2020
Gönderilme Tarihi 22 Ağustos 2019
Kabul Tarihi 23 Eylül 2020
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 1

Kaynak Göster

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. Aralık 2020;36(1):459-476. doi:10.17341/gazimmfd.609418
Chicago Uçar, Ukbe Usame, Selçuk Kürşat İşleyen, ve Hadi Gökçen. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, sy. 1 (Aralık 2020): 459-76. https://doi.org/10.17341/gazimmfd.609418.
EndNote Uçar UU, İşleyen SK, Gökçen H (01 Aralık 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, ve H. Gökçen, “Hareketli müşterili araç rotalama problemi için Meta-Sezgisel algoritmaların deneysel analizi”, GUMMFD, c. 36, sy. 1, ss. 459–476, 2020, doi: 10.17341/gazimmfd.609418.
ISNAD Uçar, Ukbe Usame vd. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/1 (Aralık 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 vd. “Hareketli müşterili Araç Rotalama Problemi için Meta-Sezgisel algoritmaların Deneysel Analizi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 36, sy. 1, 2020, ss. 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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