PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Green last-mile route planning for efficient e-commerce distribution

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study aims to design vehicle routes based on cost minimisation and the minimisation of greenhouse gasses (GHG) emissions to help companies solve the vehicle routing problem with pickup and delivery (VRPPD) via particle swarm optimisation (PSO). An effective metaheuristics search technique called particle swarm optimisation (PSO) was applied to design the optimal route for these problems. Simulated data from Li and Lim (2001) were used to evaluate the PSO performance for solving green vehicle routing problems with pickup and delivery (Green VRPPD). The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to customers living in a specific area called a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed. PSO has been confirmed for solving VRPPD effectively. The study compared the results based on the use of two different objective functions with fuel consumption from diesel and liquefied petroleum gas (LPG). It indicates that solving VRPPD by considering the emissions of direct greenhouse gases as an objective function provides cleaner routes, rather than considering total cost as the objective function for all test cases. However, as Green VRPPD requires more vehicles and longer travel distances, this requires a greater total cost than considering the total cost as the objective function. Considering the types of fuels used, it is obvious that LPG is more environmentally friendly than diesel by up to 53.61 %. This paper should be of interest to a broad readership, including those concerned with vehicle routing problems, transportation, logistics, and environmental management. The findings suggest that green vehicle routing problems with pickup and delivery should be used when distributing products to a cluster. However, the design of vehicle routes by Green VRPPD costs more when used to distribute products to customers living randomly in a coverage service area. When logistics providers decide to use Green VRPPD instead of VRPPD, they need to be concerned about possible higher costs if an increase in the number of vehicles is needed.
Rocznik
Strony
1--12
Opis fizyczny
Bibliogr. 45 poz., tab.
Twórcy
  • Burapha University International College, Thailand
  • Burapha University International College, Thailand
  • Burapha University International College, Thailand
Bibliografia
  • Al-Tit, A. A. (2020). E-commerce drivers and barriers and their impact on e-customer loyalty in small and medium-sized enterprises (SMES). Business: Theory and Practice, 21(1), 146-157. doi: 10.3846/btp.2020.11612
  • Bansal, S., & Wadhawan, S. (2021). A hybrid of sine cosine and particle swarm optimization (HSPS) for solving heterogeneous fixed fleet vehicle routing problem. International Journal of Applied Metaheuristic Computing (IJAMC), 12(1), 41-65.
  • Belmecheri, F., Prins, C., Yalaoui, F., & Amodeo, L. (2013). Particle swarm optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows. Journal of Intelligent Manufacturing, 24(4), 775-789.
  • Bent, R., & Van Hentenryck, P. (2006). A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Computers & Operations Research, 33(4), 875-893.
  • Bruglieri, M., Mancini, S., & Pisacane, O. (2019). The green vehicle routing problem with capacitated alternative fuel stations. Computers & Operations Research, 112, 104759.
  • Chen, M. C., Hsiao, Y. H., Reddy, R. H., & Tiwari, M. K. (2016). The self-learning particle swarm optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks. Transportation Research Part E: Logistics and Transportation Review, 91, 208-226.
  • Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce – taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer Services, 59, 102344.
  • Créput, J. C., Koukam, A., Kozlak, J., & Lukasik, J. (2004). An evolutionary approach to pickup and delivery problem with time windows. In International Conference on Computational Science (pp. 1102-1108). Springer, Berlin, Heidelberg.
  • Fan, H., Zhang, Y., Tian, P., Lv, Y., & Fan, H. (2021). Time-dependent multi-depot green vehicle routing problem with time windows considering temporal-spatial distance. Computers & Operations Research, 129, 105211.
  • Faugère, L., & Montreuil, B. (2020). Smart locker bank design optimization for urban omnichannel logistics: Assessing monolithic vs. modular configurations. Computers & Industrial Engineering, 139, 105544.
  • Fedorko, R., Fedorko, I., Riana, I. G., Rigelský, M., Oleárová, M., & Obšatníková, K. (2018). The impact of selected elements of e-commerce to e-shop recommendation. Polish Journal of Management Studies, 18(1), 107-120. doi: 10.17512/pjms.2018.18.1.09
  • Florek-Paszkowska, A., Ujwary-Gil, A., & Godlewska-Dzioboń, B. (2021). Business innovation and critical success factors in the era of digital transformation and turbulent times. Journal of Entrepreneurship, Management, and Innovation, 17(4), 7-28. doi: 10.7341/20211741
  • Foroutan, R. A., Rezaeian, J., & Mahdavi, I. (2020). Green vehicle routing and scheduling problem with heterogeneous fleet including reverse logistics in the form of collecting returned goods. Applied Soft Computing, 94, 106462.
  • Fu, H., Manogaran, G., Wu, K., Cao, M., Jiang, S., & Yang, A. (2020). Intelligent decision-making of online shopping behavior based on internet of things. International Journal of Information Management, 50, 515-525.
  • Goksal, F. P., Karaoglan, I., & Altiparmak, F. (2013). A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Computers & Industrial Engineering, 65(1), 39-53.
  • Gregory, G. D., Ngo, L. V., & Karavdic, M. (2019). Developing e-commerce marketing capabilities and efficiencies for enhanced performance in business-to-business export ventures. Industrial Marketing, 78, 146-157.
  • Gulc, A. (2021). Multi-stakeholder perspective of courier service quality in B2C e-commerce. PLoS ONE, 16(5), 1-18. doi: 10.1371/journal.pone.0251728
  • Gupta, P., Govindan, K., Mehlawat, M. K., & Khaitan, A. (2021). Multiobjective capacitated green vehicle routing problem with fuzzy time-distances and demands split into bags. International Journal of Production Research, 1-17.
  • Harbaoui Dridi, I., Ben Alaïa, E., Borne, P., & Bouchriha, H. (2020). Optimisation of the multi-depots pick-up and delivery problems with time windows and multi-vehicles using PSO algorithm. International Journal of Production Research, 58(14), 4201-4214.
  • Hasle, G., & Kloster, O. (2007). Industrial vehicle routing. In G. Hasle, K.-A. Lie, E. Quak (Eds.), Geometric modelling, Numerical Simulation, and Optimization (pp.397-435). Berlin, Heidelberg: Springer.
  • Jacobs, K., Warner, S., Rietra, M., Mazza, L., Buvat, J., Khadikar, A., Cherian, S., & Khemka, Y. (2019). The last-mile delivery challenge: Giving retail and consumer product customers a superior delivery experience without impacting profitability. Retrieved from https://www.capgemini.com/wp-content/uploads/2019/01/Report-Digital-%E2%80%93-Last-Mile-Delivery-Challenge1.pdf
  • Lagos, C., Guerrero, G., Cabrera, E., Moltedo, A., Johnson, F., & Paredes, F. (2018). An improved particle swarm optimization algorithm for the VRP with simultaneous pickup and delivery and time windows. IEEE Latin America Transactions, 16(6), 1732-1740.
  • Lemke, J., Iwan, S., & Korczak, J. (2016). Usability of the parcel lockers from the customer perspective – the research in Polish Cities. Transportation Research Procedia, 16, 272-287.
  • Li, H., & Lim, A. (2003). A metaheuristic for the pickup and delivery problem with time windows. International Journal on Artificial Intelligence Tools, 12(02), 173-186.
  • Liu, X., Zhang, K., Chen, B., Zhou, J., & Miao, L. (2018). Analysis of logistics service supply chain for the One Belt and One Road initiative of China. Transportation Research Part E: Logistics and Transportation Review, 117, 23-39.
  • Mehlawat, M. K., Gupta, P., Khaitan, A., & Pedrycz, W. (2019). A hybrid intelligent approach to integrated fuzzy multiple depot capacitated green vehicle routing problem with split delivery and vehicle selection. IEEE Transactions on Fuzzy Systems, 28(6), 1155- 1166.
  • Mutinda Kitukutha, N., Vasa, L., & Oláh, J. (2021). The Impact of COVID-19 on the economy and sustainable e-commerce. Forum Scientiae Oeconomia, 9(2), 47- 72. doi: 10.23762/FSO_VOL9_NO2_3
  • Norouzi, N., Sadegh-Amalnick, M., & Tavakkoli-Moghaddam, R. (2017). Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption. Optimization Letters, 11, 121-134.
  • Qin, X., Liu, Z., & Tian, L. (2021). The optimal combination between selling mode and logistics service strategy in an e-commerce market. European Journal of Operational Research, 289(2), 639-651.
  • Ready, C. (2013). Environmental reporting guidelines: Including mandatory greenhouse gas emissions reporting guidance. Retrieved from https://www.gov.uk/government/publications/environmental-reporting-guidelines-including-mandatory-greenhouse-gas-emissions-reporting-guidance
  • Rita, P., Oliveira, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10), e02690.
  • Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360) (pp. 69-73). IEEE.
  • Sruthi, A., Anbuudayasankar, S. P., & Jeyakumar, G. (2019). Energy-efficient green vehicle routing problem. International Journal of Information Systems and Supply Chain Management (IJISSCM), 12(4), 27-41.
  • Tsang, Y. P., Wu, C. H., Lam, H. Y., Choy, K. L., & Ho, G. T. S. (2021). Integrating Internet of Things and multi-temperature delivery planning for perishable food E-commerce logistics: a model and application. International Journal of Production Research, 59(5), 1534-1556.
  • Úbeda, S., Faulin, J., Serrano, A., & Arcelus, F. J. (2014). Solving the green capacitated vehicle routing problem using a tabu search algorithm. Lecture Notes in Management Science, 6(1), 141-149.
  • United States. Environmental Protection Agency. Office of Policy. (1999). Inventory of US Greenhouse Gas Emissions and Sinks: 1990-1997. The Agency.
  • Vakulenko, Y., Shams, P., Hellström, D., & Hjort, K. (2019). Service innovation in e-commerce last mile delivery: Mapping the e-customer journey. Journal of Business Research, 101, 461-468.
  • van Lopik, K., Schnieder, M., Sharpe, R., Sinclair, M., Hinde, C., Conway, P., West, A., & Maguire, M. (2020). Comparison of in-sight and handheld navigation devices toward supporting industry 4.0 supply chains: First and last mile deliveries at the human level. Applied Ergonomics, 82, 102928.
  • Xu, X., Wang, C., Li, J., & Shi, C. (2019). Green Transportation and Information Uncertainty in Gasoline Distribution: Evidence from China. Emerging Markets Finance and Trade, 57(11), 1-19.
  • Yu, Y., Wang, S., Wang, J., & Huang, M. (2019). A branch-and-price algorithm for the heterogeneous fleet green vehicle routing problem with time windows. Transportation Research Part B: Methodological, 122, 511-527.
  • Yu, Y., Yu, C., Xu, G., Zhong, R. Y., & Huang, G. Q. (2020). An operation synchronization model for distribution center in E-commerce logistics service. Advanced Engineering Informatics, 43, 101014.
  • Yuen, K. F., Wang, X., Ma, F., & Wong, Y. D. (2019). The determinants of customers’ intention to use smart lockers for last-mile deliveries. Journal of Retailing and Consumer Services, 49, 316-326.
  • Zhang, X., Zhou, G., Cao, J., & Wu, A. (2020). Evolving strategies of e-commerce and express delivery enterprises with public supervision. Research in Transportation Economics, 80, 100810.
  • Zhou, M., Zhao, L., Kong, N., Campy, K. S., Xu, G., Zhu, G., Cao, X., & Wang, S. (2020). Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery. Journal of Retailing and Consumer Services, 52, 101911.
  • Zhu, L., & Hu, D. (2019). Study on the vehicle routing problem considering congestion and emission factors. International Journal of Production Research, 57(19), 6115-6129.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bf7e0002-7ef7-4181-b9a2-2774826abe7f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.