Sustainable Operations of Last Mile Logistics Based on Machine Learning Processes

被引:2
|
作者
Orsic, Jerko [1 ]
Jereb, Borut [2 ]
Obrecht, Matevz [2 ]
机构
[1] Epilog Doo, Ljubljana 1000, Slovenia
[2] Univ Maribor, Fac Logist, Celje 3000, Slovenia
关键词
supply chain management; real-time; home delivery; business modeling; e-commerce; time window;
D O I
10.3390/pr10122524
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The last-mile logistics is regarded as one of the least efficient, most expensive, and polluting part of the entire supply chain and has a significant impact and consequences on sustainable delivery operations. The leading business model in e-commerce called Attended Home Delivery is the most expensive and demanding when a short delivery window is mutually agreed upon with the customer, decreasing possible optimizing flexibility. On the other hand, last-mile logistics is changing as decisions should be made in real time. This paper is focused on the proposed solution of sustainability opportunities in Attended Home Delivery, where we use a new approach to achieve more sustainable deliveries with machine learning forecasts based on real-time data, different dynamic route planning algorithms, tracking logistics events, fleet capacities and other relevant data. The developed model proposes to influence customers to choose a more sustainable delivery time window with important sustainability benefits based on machine learning to predict accurate time windows with real-time data influence. At the same time, better utilization of vehicles, less congestion, and fewer failures at home delivery are achieved. More sustainable routes are selected in the preplanning process due to predicted traffic or other circumstances. Increasing time slots from 2 to 4 h makes it possible to improve travel distance by about 5.5% and decrease cost by 11% if we assume that only 20% of customers agree to larger time slots.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Towards sustainable last-mile logistics: A decision-making model for complex urban contexts
    Silva, Vasco
    Amaral, Antonio
    Fontes, Tania
    SUSTAINABLE CITIES AND SOCIETY, 2023, 96
  • [42] Towards sustainable last-mile logistics? Investigating the role of cooperation, regulation, and innovation in scenarios for 2035
    Plazier, Paul
    Rauws, Ward
    Neef, Robin
    Buijs, Paul
    RESEARCH IN TRANSPORTATION BUSINESS AND MANAGEMENT, 2024, 56
  • [43] A viability study using conceptual models for last mile drone logistics operations in populated urban cities of India
    Gabani, Parth Rameshbhai
    Gala, Umang Bipin
    Narwane, Vaibhav S.
    Raut, Rakesh D.
    Govindarajan, Usharani Hareesh
    Narkhede, Balkrishna E.
    IET COLLABORATIVE INTELLIGENT MANUFACTURING, 2021, 3 (03) : 262 - 272
  • [44] A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines
    Andoh, Eugenia Ama
    Yu, Hao
    ANNALS OF OPERATIONS RESEARCH, 2023, 328 (01) : 75 - 105
  • [45] A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines
    Eugenia Ama Andoh
    Hao Yu
    Annals of Operations Research, 2023, 328 : 75 - 105
  • [46] A Crowdsourcing Approach for Sustainable Last Mile Delivery
    Giret, Adriana
    Carrascosa, Carlos
    Julian, Vicente
    Rebollo, Miguel
    Botti, Vicente
    SUSTAINABILITY, 2018, 10 (12)
  • [47] Machine Learning for Data-Driven Last-Mile Delivery Optimization
    Özarık S.S.
    Costa P.D.
    Florio A.M.
    Transportation Science, 2024, 58 (01) : 27 - 44
  • [48] Machine Learning-Based Analysis of Sustainable Biochar Production Processes
    Cosgun, Ahmet
    Oral, Burcu
    Gunay, M. Erdem
    Yildirim, Ramazan
    BIOENERGY RESEARCH, 2024, 17 (04) : 2311 - 2327
  • [49] Measuring Disruptions in Last-Mile Delivery Operations
    Munoz-Villamizar, Andres
    Solano-Charris, Elyn L.
    Reyes-Rubiano, Lorena
    Faulin, Javier
    LOGISTICS-BASEL, 2021, 5 (01):
  • [50] Decoding cargo bikes' potential to be a sustainable last-mile delivery mode: an operations management perspective
    Michalakopoulou, Kalliopi
    Yaroson, Emilia Vann
    Chatziioannou, Ioannis
    TRANSPORTATION PLANNING AND TECHNOLOGY, 2024,