20.500.12556/DKUM-87098 Sustainable operations of last mile logistics based on machine learning processes 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. supply chain management real-time home delivery business modeling e-commerce time window organizacija časovnih verig realni čas dostava na dom poslovno modeliranje časovni okvir true false false Angleški jezik Slovenski jezik Članek v reviji 2024-02-19 10:07:52 2024-02-19 10:07:53 2024-02-20 03:11:59 0000-00-00 00:00:00 2022 0 0 str. 1-17 iss. 12, article no. 2524 Vol. 10 Dec. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-12-01 005.51:004.85 131755011 10.3390/pr10122524 2227-9717 https://www.mdpi.com/2227-9717/10/12/2524/htm 1 5e344ab2-cf06-11ee-afa3-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176252 Orsic,_Jereb,_Obrecht_Sustainable_Operations_of_Last_Mile_Logistics_Based_on.pdf Orsic,_Jereb,_Obrecht_Sustainable_Operations_of_Last_Mile_Logistics_Based_on.pdf 1 FAB64F8D09633F867E977D6DC521F348 dea321c4dbaa489fa56b5f3deb5fa5d008de29dae69836a7861c809bfa2ef40c 7445950e-cf09-11ee-afa3-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176253 Fakulteta za logistiko 0 0 0