Re-design of a logistics distribution network with genetic algorithm

Abstract

Logistics management has become a critical part of any company that wants to achieve its goals through capacity, time, and service quality in a financial environment. These improvements have been influenced by economic tendencies stemming from the development of technological infrastructures and an increasing complication of commerce flows. Therefore, the design of logistics networks has become an important issue for both businesses and researchers. In this study, a deterministic model is created in which the shipments are distributed from origin cities to destination cities, either directly or through distribution centers whose locations will be determined by the model. While only one distribution center is assigned to each destination city, each distribution center can serve more than one destination city. The model decides where distribution centers will be located and aims to minimize logistics operating cost. The model considers capacity constraint for each distribution center. A genetic algorithmbased method has been developed to solve the problem. The genetic algorithm model is coded in Python. The genetic algorithm solution is validated on small-sized problems by comparing it with optimal solution obtained by the General Algebraic Modeling System (GAMS).

Keywords

• Supply chain
• Logistics
• Distribution network
• Genetic Algorithm

BİR LOJİSTİK DAĞITIM AĞININ GENETİK ALGORİTMA İLE YENİDEN TASARLANMASI

Abstract

Lojistik yönetimi, özellikle ticaretin küreselleşmesi ve endüstriyel döngülerin hızlandığı bir ekonomik ortamda, ekonomik rekabet gücü, zaman ve hizmet kalitesi açısından hedeflerine ulaşmak isteyen herhangi bir şirket için stratejik bir konu haline gelmiştir. Bu gelişmeler, teknolojik alt yapıların gelişmesi, ticaret akışlarının artan karmaşıklığı, artan rekabet ve sürdürülebilir kalkınmadan kaynaklanan ekonomi eğilimlerden etkilenmiştir. Bu nedenle lojistik ağların tasarımı ve planlaması hem işletmeler hem de araştırmacılar için giderek daha önemli hale gelmiştir. Bu çalışmada, gönderilerin üreticilerin bulunduğu şehirlerden perakendecilerin bulunduğu şehirlere, doğrudan veya yerleri model tarafından belirlenecek olan bir dizi dağıtım merkezi aracılığıyla dağıtıldığı bir deterministik model oluşturulmuştur. Her varış şehrine sadece bir dağıtım merkezi atanırken, her dağıtım merkezi birden fazla varış şehrine hizmet verebilmektedir. Model, dağıtım merkezlerinin nereye yerleştirileceğine karar vermekte ve lojistik işletme maliyetini en aza indirmeyi amaçlamaktadır. Model, her bir dağıtım merkezi için kapasite kısıtını dikkate almaktadır. Problemi çözmek için genetik algoritma tabanlı bir yöntem geliştirilmiştir. Genetik algoritma modeli Python dili ile kodlanmıştır. Genetik algoritma çözümü, Genel Cebirsel Modelleme Sistemi (GAMS) tarafından elde edilen optimal çözümle karşılaştırılarak küçük boyutlu problemler üzerinde doğrulanmıştır.

Keywords

• Tedarik zinciri
• Lojistik
• Dağıtım ağı
• Genetik algoritma

References

1. Agustina, D., Lee C. K. M. ve Piplani R. (2014) Vehicle scheduling and routing at a cross docking center for food supply chains, International Journal of Production Economics, 152, 29–41. doi:10.1016/j.ijpe.2014.01.002
2. Aravendan, M. ve Panneerselvam, R. (2014) Literature Review on Network Design Problems in Closed Loop and Reverse Supply Chains, Intelligent Information Management, 6, 104-117. doi:10.4236/iim.2014.63012
3. Ayvaz, B., Kusakcı, A.O., Ozturk, F. ve Sırakaya, M. (2018) Biyodizel Tedarik Zinciri Ağ Tasarımı İçin Çok Amaçlı Karma Tam Sayılı Doğrusal Programlama Modeli Önerisi, Uludağ University Journal of The Faculty of Engineering, 23(4), 55-70. doi:10.17482/uumfd.455307
4. Beamon, B.M. (1998) Supply chain design and analysis: Models and methods, International Journal of Production Economics, 55(3), 281-294. doi:10.1016/S0925-5273(98)00079-6
5. Baniamerian A., Bashiri M. ve Zabihi F. (2017) Two phase genetic algorithm for vehicle routing and scheduling problem with cross-docking and time windows considering customer satisfaction, Journal of Industrial Engineering International, 14(1), 15–30. doi:10.1007/s40092-017-0203-0
6. Baniamerian A., Bashiri M. ve Tavakkoli-Moghaddam R. (2019) Modified variable neighborhood search and genetic algorithm for profitable heterogeneous vehicle routing problem with crossdocking, Applied Soft Computing, 75, 441–460. doi: 10.1016/j.asoc.2018.11.029
7. Beamon, B.M. (1998) Supply chain design and analysis: Models and methods, International Journal of Production Economics, 55(3), 281-294. doi:10.1016/S0925-5273(98)00079-6
8. Bediroğlu, Y. ve Yıldırım, V. (2020) Lojistik Merkez Yer Seçimi İçin CBS & ÇÖKV Ara Yüzü Geliştirilmesi ve Ordu İli Pilot Bölge Çalışması, Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 9(1), 323-334. doi:10.28948/ngumuh.561680

9. Brahami, M.A., Dahane, M., Souier, M. (2022) Sustainable capacitated facility location/network design problem: a Non-dominated Sorting Genetic Algorithm based multiobjective approach. Annals of Operations Research, 311, 821–852, doi:10.1007/s10479-020-03659-9
10. Bräysy, O. ve Gendreau, M. (2001) Genetic algorithms for the vehicle routing problem with time windows. Arpakannus, (1), 33-38.
11. Calvo, M.A., Navarro, A. ve Peria, C.R. (2015) Project management and key knowledge to improve business results through the efqm excellence model, International Journal of Project Management, 33(8), 1638-1651. doi:10.1016/j.ijproman.2015.01.010
12. Cao, E. Ve Lai, M. (2007) An improved genetic algorithm for the Vehicle Routing Problem with Simultaneous Delivery and Pick-up Service, In Proceedings of the 6th Wuhan International Conference on E-Business, 2100-2106. doi: 10.1109/ICNC.2007.209
13. Chopra, S. ve Meindl, P. (2010) Supply Chain Management: Strategy, Planning and Operations, Prentice Hall, New Jersey.
14. Cui H., Qiu J., Cao J., Guo M., Chen X. ve Gorbachev S. (2023) Route optimization in township logistics distribution considering customer satisfaction based on adaptive genetic algorithm, Mathematics and Computers in Simulation, 204, 28-42, ISSN 0378-4754. doi:10.1016/j.matcom.2022.05.020
15. Demirel, N., Gökçen, H., Akçayol, M.A. ve Demirel, E. (2011) Çok Aşamalı Bütünleşik Lojistik Ağı Optimizasyonu Probleminin Melez Genetik Algoritma ile Çözümü, Journal of the Faculty of Engineering and Architecture of Gazi University, 26(4), 929-936.
16. Govindan, K., Fattahi, M. ve Keyvanshokooh, E. (2017) Supply chain network design under uncertainty: A comprehensive review and future research directions, European Journal of Operational Research, 263, 108-141. doi:10.1016/j.ejor.2017.04.009
17. Guerrero-Lorente, J., Gabor, A.F. ve Ponce-Cueto, E. (2020) Omnichannel logistics network design with integrated customer preference for deliveries and returns, Computers & Industrial Engineering, 160, 107569. doi:10.1016/j.cie.2020.106433
18. Guo, K. (2020) Research on location selection model of distribution network with constrained line constraints based on genetic algorithm. Neural Computing & Application, 32, 1679–1689. doi:10.1007/s00521-019-04257-y
19. Ghoseiri K., Ghannadpour S.F. (2010) Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm, Applied Soft Computing, 10:1096–1107. doi: 10.1016/j.asoc.2010.04.001
20. Hassanat, A., Almohammadi, K., Alkafaween, E., Bunawas, E., Hammouri, A. ve Prasath, S. (2019) Choosing Mutation and Crossover Ratios for Genetic Algorithms-A Review with a New Dynamic Approach, Information, 10, 390. doi:10.3390/info10120390
21. Hiremath, N.C., Sahu, S. ve Tiwari, M.K. (2013) Multi Objective Outbound Logistics Network Design for a Manufacturing Supply Chain, Journal of Intelligent Manufacturing, 24, 1071-1084. doi:10.1007/s10845-012-0635-8
22. Koç, Ç., Özceylan, E., Kesen, S.E., Çil, Z.A. ve Mete, S. (2018) Forward supply Chain network design problem: Heuristic approaches, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 24(4), 749-763. doi:10.5505/pajes.2018.72324
23. Kong P., Lian Z., He M., Peng X., Song J. ve Lan Y. (2020) Design optimization of composite curved rod for wind tunnel virtual flight test based on multi-island genetic algorithm, Journal of Physics: Conference Series, 1624(4), Article ID 042027. doi: 10.1088/1742-6596/1624/4/042027
24. Kudova, P. (2007) Clustering Genetic Algorithm, 18th International Workshop on Database and Expert Systems Applications, 138-142. doi:10.1109/DEXA.2007.65
25. Kumar, M., Husian, M., Upreti, N. ve Gupta, D. (2010) Genetic Algorithm: Review and Application, International Journal of Information Technology and Knowledge Management, 2(2), 451-454. doi:10.2139/ssrn.3529843
26. Kumar, S.N. ve Panneerselvam, R. (2012) A survey on the vehicle routing problem and its variants, Intelligent Information Management, 4(3), 66–74. doi:10.4236/iim.2012.43010
27. Lahrichiac, N., GabrielCrainicab, T., Gendreauac, M., Walter, R., CeraselaCrisanae, G., ve Vidalad, T. (2015) An integrative cooperative search framework for multi-decision-attribute combinatorial optimization: application to the mdpvrp sciencedirect, European Journal of Operational Research, 246(2), 400–412. doi:10.1016/j.ejor.2015.05.007
28. Laporte, G. (2009) Fifty years of vehicle routing, Transportation Science, Canada Research Chair in Distribution Management, HEC Montreal, 43, 408-416. doi: 10.1287/trsc.1090.0301
29. Lau, H.C., Chan, T.M., Tsui, W.T. ve Pang, W.K. (2010) Application of genetic algorithms to solve the multidepot vehicle routing problem, IEEE Transactions on Automation Science and Engineering, 7(2), 383-392. doi:10.1109/TASE.2009.2019265
30. Liu, D., Zhao, S., Jiang, W. ve Liu, J. (2014) Research of intermodal integrated optimization model of total logistics cost based on economies of scale, Computer Engineering & Applications, 50(14), 255–312. doi:10.3901/JME.2014.11.093
31. Mirjalili, S. (2019) Evolutionary Algorithms and Neural Networks, Studies in Computational Intelligence. doi:10.1007/978-3-319-93025-1
32. Okyere, S., Yang, J. ve Adams, C.A. (2022) Optimizing the Sustainable Multimodal Freight Transport and Logistics System Based on the Genetic Algorithm, Sustainability, 14(18):11577, doi:10.3390/su141811577
33. Possel, B., Wismans, L., Berkum, E. ve Bliemer, M., (2018) The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing g framework, Transportation, 45(2), 1–28. doi:10.1007/s11116-016-9738-y
34. Ren, T., Luo, T., Jia, B., Yang, B., Wang, L. ve Xing, L. (2023) Improved ant colony optimization for the vehicle routing problem with split pickup and split delivery, Swarm and Evolutionary Computation, 77, 101228. doi:10.1016/j.swevo.2023.101228
35. Saddoune, M., Desaulniers, G., Elhallaoui, I ve Soumis, F. (2011) Integrated airline crew scheduling: a bi-dynamic constraint aggregation method using neighborhoods, European Journal of Operational Research, 212(3), 445–454. doi: 10.1016/j.ejor.2011.02.009
36. Sadjadi, S. J., Jafari, M. ve Amini, T. (2009) A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study), Ce International Journal of Advanced Manufacturing Technology, 44(1-2), 194–200. doi:10.1007/s00170-008-1648-5
37. Sarıkaya, H.A., Çalıkan, E. ve Türkbey, O. (2014) Bütünleşik Tedarik Zinciri Ağında Tesis Yeri Seçimi İçin Bulanık Çok Amaçlı Programlama Modeli, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(5), 150-161. doi:10.5505/pajes.2014.98853
38. Shakeriana, H., Dehnavia, H.D. ve Shaterib, F.A. (2016) Framework for the implementation of knowledge management in supply chain management, Procedia - Social and Behavioral Sciences, 230, 176 – 183. doi:10.1016/j.sbspro.2016.09.022
39. Sicilia, J.A., Quemadab, C., Royoc, B. ve Escuínd, D. (2016) An optimization algorithm for solving the rich vehicle routing problem based on variable neighborhood search and tabu search metaheuristics. Journal of Computational and Applied Mathematics, 291:468–477. doi: 10.1016/j.cam.2015.03.050
40. Stock, J.R., Boyer, S.L. ve Harmon, T. (2010) Research opportunities in supply chain management, Journal of the Academy of Marketing Science, 38, 32-41. doi:10.1007/s11747- 009-0136-2\
41. Tiwari, K.V ve Sharma, S.K. (2023) An optimization model for vehicle routing problem in last-mile delivery, Expert Systems with Applications, 222, 119789. doi:10.1016/j.eswa.2023.119789
42. Torun, H. ve Canbulut, G. (2019) İki aşamalı tedarik zinciri koordinasyonunun bulanık talep altında analizi, Journal of the Faculty of Engineering and Architecture of Gazi University, 34(3), 1315-1328. doi:10.17341/gazimmfd.460528
43. Wang, F., Ying, Z., Teng F., (2014) Low carbon logistics distribution route optimization research based on chaos ant colony algorithm, Journal of Investigative Medicine, 62(1), 105.
44. Wasil, E., Gulczynski, D., Golden, B (2011) The multi-depot split delivery vehicle routing problem: An integer programming-based heuristic, new test problems, and computational results, Computers & Industrial Engineering, 61, 794–804. doi:10.1016/j.cie.2011.05.012
45. Wenxue, R.A.N., Xinling, S.H.I., Huasen, F.U. ve Guomin, Y.A.N.G. (2013) Application research on ant colony algorithm in logistic distribution route-optimization of fresh agricultural products, International Journal of Digital Content Technology and its Applications, 7(6), 391–399. doi:10.4156/jdcta.vol7.issue6.44
46. Yıldız, K., Tabak, Ç., Yerlikaya, M.A. ve Efe, B. (2022) A Logistics Model Suggestion for A Logistics Centre to Be Established: An Application in Aegean and Central Anatolia Region, Gazi University Journal of Science, 35(1), 73-90. doi:10.35378/gujs.844650
47. Zhang, Y. (2022) Logistics distribution scheduling model of supply chain based on genetic algorithm, Journal of Industrial and Production Engineering, 39(2), 83–88. doi: 10.1080/21681015.2021.1958938