Kargo Taşımacılığında Enerji Tasarrufuna Dayalı Bir Modelleme Yaklaşımı

Year 2024, Volume: 36 Issue: 2, 965 - 978, 30.09.2024

Sevcan Emek , İsmail Tosun , Mehmet Emre Yılmaz Zafer Say , Yusuf Burak Peker

https://doi.org/10.35234/fumbd.1468659

Abstract

Bu çalışma, kullanıcıların kargo sürecini verimli ve ekonomik bir şekilde gerçekleştirmelerini imkan sağlayan bir kargo taşımacılığı sistemi yönetim modeli sunmaktadır. Kargo taşımacılığı, ulaşım sistemleri ağının önemli bir parçasıdır. Kargo taşımacılığının avantajları, ürünlerin güvenli bir şekilde teslim edilmesini sağlamak, zamandan tasarruf etmek ve maliyetleri azaltmaktır. Bu çalışma, kargo şirketlerinin yoğunluk ve lojistik stratejilerini dikkate alarak, büyük dağıtım ağlarının neden olduğu yüksek karbondioksit emisyonları, geç ve pahalı teslimatlar sorunlarına çözüm getirmektedir. Önerilen kargo yönetim sistemi, kullanıcıların şu anda seyahat ettiği rota üzerindeki varış noktalarına kargo teslim etmeye odaklanmaktadır. Bu sistem üç ana bileşen etrafında oluşturulmuştur: A* algoritması tarafından optimize edilmiş rota planlaması, mobil ve web arayüzleri aracılığıyla kontrol edilebilen bir sistem modeli ve kargo için bırakma ve alma noktaları olarak belirlenmiş düğümlerdir. A* algoritması, optimum rotayı hesaplamak için yönü de hesaba katan bir ödül matrisi ile çalışır. Kullanıcılar, kargo yönetimi ve izleme süreçlerini mobil ve web arayüzleri üzerinden gerçekleştirmektedir. Çalışmanın model prototipi olan otomatik düğümler, kullanıcıların kargolarını teslim ettikleri/aldıkları kargo satış makinelerini temsil eder. Bu çalışma sayesinde kullanıcılar, varışlar arası seyahat ederken taşıyacakları kargoyu görüntüleyip seçebilecek ve kargo taşıyıcısı/sürücüsü olarak hareket ederek kazanç elde edebileceklerdir.

Keywords

Kargo yönetimi sistem modeli, A* algoritması, rota planlama, otomatik düğüm

A Modeling Approach for Cargo Transportation Considering Energy Saving

Abstract

This study presents a cargo transportation system management model that enables users to carry out the cargo process efficiently and economically. Cargo transportation is an important part of the transportation systems network. The advantages of cargo transportation are to ensure the safe delivery of products, save time and reduce costs. This study addresses the solution to the problems of high carbon dioxide emissions, and late and expensive deliveries caused by large distribution networks, by taking into account the density and logistics strategies of the cargo companies. The proposed cargo management system focuses on delivering cargo to destinations along the route that users are currently traveling on. This system is built around three main components: optimized route planning by an A* algorithm, a system model controllable through mobile and web interfaces, and nodes designated as drop-off and pick-up points for cargo. The A* algorithm runs with a reward matrix that also takes direction into account to calculate the optimal route. Users carry out the cargo management and tracking processes on mobile and web interfaces. Automatic nodes, which are the model prototype of the study, represent the cargo vending machines where users deliver/receive their cargo. Through this work, users can view and select cargo to carry while traveling between destinations and earn profits by acting as cargo carriers/drivers.

Keywords

cargo transportation system management model, A* algorithm, route planning, automatic node

References

• Smirnov SA, Smirnova OY. Magnetic Levitation Cargo Ransport Role in World Economy, Transportation Systems and Technology, 2019; 5(2), 106-117.
• Prokofieva E. Review of research in cargo transportation reliability, E3S Web of Conferences, 157, 05008, 2022.
• Amiri-Khorheh M, Moisiadis F, Davarzani H. Socio-environmental performance of transportation systems, Management ofEnvironmental Quality: An International Journal, 2015; 26, 826-851.
• Schodl R Eitler S, Ennser B, Breinbauer A, Hu B, Markvica K, Prandtstetter M, Zajicek J, Berger T, Pfoser S, Berkowitsch C, Hauger G. Innovative means of cargo transport: A scalable method for estimating regional impacts, Transportation Research Procedia, 2018; 30, 342-349.
• Rodrigue JP. Transportation and Energy (Chapter 4). The Geography of Transport Systems, 5th ed., 2020.
• Oubnaki H, Haouraji, C, Mounir, B, Mounir, I, Farchi, A. Energy Consumption in the Transport Sector: Trends and Forecast Estimates in Morocco. E3S Web of Conferences 336, 00078, 2022.
• Juan AA, Mendez CA, Faulin J, Armas J, Grasman SE. Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges. Energies, 2016; 9(2), 86.
• Speranza MG. Trends in transportation and logistics, European Journal of Operational Research, 2016; 264(3), 830-836.
• Figliozzi M. Vehicle routing problem for emissions minimization, Transportation Research Record 2010; 2197(1), 1–7.
• Berechman J. Urban and regional economic impacts of transportation investment: a critical assessment and proposed methodology. Transportation Research Part A: Policy and Practice, 1994, 28(4), 351-362.
• Zubkov V, Sirina N. Improvement of Cargo Transportation Technology in Rail and Sea Traffic. In: Popovic, Z., Manakov, A., Breskich, V. (eds) VIII International Scientific Siberian Transport Forum. TransSiberia, 2019. Advances in Intelligent Systems and Computing, 2020, 1116. Springer, Cham.
• Kern J. The Digital Transformation of Logistics: Demystifying Impacts of the Fourth Industrial Revolution, A Review About Technologies and Their Implementation Status (Chapter 25), 2021.
• Ivanov D, Dolgui A, Sokolov B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics, International Journal of Production Research, Taylor & Francis Journals, 2019; 57(3), 829-846.
• Gesing B, Peterson SJ, Michelsen D. Artificial Intelligence in Logistics, DHL Customer Solutions & Innovation, 2018, DHL CSI, 53844 Troisdorf, Germany.
• Soltani ZK. The applications of artificial intelligence in logistics and supply chain, Turkish Journal of Computer and Mathematics Education, 2021; 12(13), 4488–4499.
• Adıgüzel S. Use of artificial intelligence in logistics management, Proceedings of the 1st International Conference on Interdisciplinary Applications of Artificial Intelligence, 2021, pp. 17– 25.
• Boute RN, Udenio M. Ai in logistics and supply chain management, 2021.
• Toorajipour R, Sohrabpour V, Nazarpour A, Oghazi P, Fischl M. Artificial intelligence in supply chain management: A systematic literature review, Journal of Business Research, 2021; 122, 502–517.
• Sinha K, Labi S. Transportation Decision Making: Principles of Project Evaluation and Programming, JohnWiley & Sons, New Jersey, NY, USA, 2007.
• Grimm N, Faeth S, Golubiewski N, Redman C, Wu J, Bai X Briggs, J. Global change and the ecology of cities, Science, 2008; 319(5864), 756–760.
• Schliwa G, Armitage R, Aziz S, Evans J, Rhoades J. Sustainable city logistics — making cargo cycles viable for urban freight transport, Research in Transportation Business & Management, 2015; 15, 50–57.
• Nüesch T, Cerofolini A, Mancini G, Cavina N, Onder C, Guzzella L. Equivalent consumption minimization strategy for the control of real driving nox emissions of a diesel hybrid electric vehicle, Energies, 2014; 7(5), 3148–3178.
• Colin G, Chamaillard Y, Charlet A, Nelson-Gruel D. Towards a friendly energy management strategy for hybrid electric vehicles with respect to pollution, battery and drivability, Energies, 2014; 7(9), 6013–6030.
• Chen Z, Xiong R, Wang K, Jiao B. Optimal energy management strategy of a plug-in hybrid electric vehicle based on a particle swarm optimization algorithm, Energies, 2015; 8(5), 3661–3678.
• Hwang T, Ouyang Y. Urban freight truck routing under stochastic congestion and emission considerations, Sustainability, 2015; 7(6), 6610–6625.
• Bektas T, Laporte, G. The pollution-routing problem, Transportation Research Part B: Methodological, 2011; 45(8), 1232–1250.
• Min H. Artificial intelligence in supply chain management: theory and applications, International Journal of Logistics Research and Applications, 2010; 13(1), 13–39.
• Karur K, Sharma N, Dharmatti C, Siegel JE. A survey of path planning algorithms for mobile robots, Vehicles, 2021; 3(3), 448–468.
• Dilmegani C. Top 15 use cases and applications of ai in logistics. https://research.aimultiple.com/logistics-ai/ Accessed 19 August 2023.
• McKinnon AC. Preparing Logistics for the Low-Carbon Economy. In: Merkert, R., Hoberg, K. (eds) Global Logistics and Supply Chain Strategies for the 2020s. Springer, 2023.
• Altunsoy U. An investigation on the use of electric vehıcles in the cargo transport system, International Anatolia Academic Online Journal Sciences Journal, 2021; 7(2), 1–14.
• Nathanail E, Papoutsis K. Towards a Sustainable Urban Freight Transport and Urban Distribution, Journal of Traffic and Logistics Engineering 2013; 1(1), 58-63.
• Arif SM, Lie TT, Seet BC, Ayyadi S, Jensen K. Review of Electric Vehicle Technologies, Charging Methods, Standards and Optimization Techniques. Electronics, 2021; 10(16):1910.
• Aydın GT, Öğüt KS. Logistic villages in Europe and Turkey, Proceedings of the 2nd International Railway Symposium, 2008, pp. 1471–1481.
• Amazon Flex. Website https://flex.amazon.com/ Accessed 18 September 2024.
• Uber Freight. Website https://www.uberfreight.com/ Accessed 18 September 2024.
• CargoX. Website https://cargox.io/ Accessed 18 September 2024.
• Convoy. Website https://convoy.com/ Accessed 18 September 2024.
• Kaplanseren B, Mercan B, Özdemir B., Kadıoğlu HH, Sel C. Carbon footprint in vehicle routing and an industrial application, International Journal of Engineering Research and Development, 2019; 11(1), 239–252.
• McBain B, Lenzen M, Albrecht G, Wackernagel M. Reducing the ecological footprint of urban cars, International Journal of Sustainable Transportation, 2018; 12(2), 117–127.
• Kumar A, Anbanandam R. Development of social sustainability index for freight transportation system, Journal of Cleaner Production, 2019, 210, 77–92.
• Chi G, Stone B. Sustainable transport planning: Estimating the ecological footprint of vehicle travel in future years, Journal of Urban Planning and Development, 2005; 131(3), 170–180.