DOI: 10.25338/b8w93s
title: Coping with the rise of E-commerce generated home deliveries through innovative last-mile technologies and strategies

description: E-commerce has the potential to make urban goods flow economically viable,
 environmentally efficient, and socially equitable. However, as e-retailers
 compete with increasingly consumer-focused services, urban freight
 witnesses a significant increase in associated distribution costs and
 negative externalities particularly affecting those living close to
 logistics clusters. Hence, to remain competitive, e-retailers deploy
 alternate last-mile distribution strategies. These alternate strategies,
 such as those that include use of electric delivery trucks for last-mile
 operations, a fleet of crowdsourced drivers for last-mile delivery,
 consolidation facilities coupled with light-duty delivery vehicles for a
 multi-echelon distribution, or collection points for customer pickup, can
 restore sustainable urban goods flow. Thus, in this study, the authors
 investigate the opportunities and challenges associated with such
 alternate last-mile distribution strategies for an e-retailer offering
 expedited service with rush delivery within strict timeframes. To this
 end, the authors formulate a last-mile network design (LMND) problem as a
 dynamic-stochastic two-echelon capacitated location routing problem with
 time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large
 neighborhood search (ALNS) metaheuristic.
description: This work models a last-mile network design problem for an
 e-retailer with a capacitated two-echelon distribution structure - typical
 in e-retail last-mile distribution, catering to a market with a stochastic
 and dynamic daily customer demand requesting delivery within
 time-windows. Considering the distribution evnironment,
 this work formulates last-mile network design problem for this e-retailer
 as a dynamic-stochastic two capacitated location routing problem with
 time-windows. In doing so, this work splits the last-mile network design
 problem into its constituent strategic, tactical, and operational
 decisions. Here, the strategic decisions undertake long-term planning to
 develop a distribution structure with appropriate distribution facilities
 and a suitable delivery fleet to service the expected customer demand in
 the planning horizon. The tactical decisions pertain to medium-term
 day-to-day planning of last-mile delivery operations to establish
 efficient goods flow in this distribution structure to service the daily
 stochastic customer demand. And finally, operational decisions involve
 immediate short-term planning to fine-tune this last-mile delivery to
 service the requests arriving dynamically through the day.
 Note, the last-mile network design problem formulated as a
 location routing problem constitutes three subproblems encompassing
 facility location problem, customer allocation problem, and vehicle
 routing problem, each of which are NP-hard combinatorial optimization
 problems. To this end, this work develops an adaptive large neighborhood
 search meta-heuristic algorithm that searches through the neighborhood by
 destroying and consequently repairing the solution thereby reconfiguring
 large portions of the solution with specific operators that are chosen
 adaptively in each iteration of the algorithm, hence the name adaptive
 large neighborhood search. Further, considering the
 stochastic and dynamic nature of the delivery environment, this work
 develops a Monte-Carlo framework simulating each day in the planning
 horizon, with each day divided into 1-hr timeslots, and with each
 time-slot accepting customer requests for service by the end of the day.
 In particular, the framework assumes the e-retailer will delay route
 commitments until the last-feasible time-slot to accumulate customer
 requests and consequently assign them to an uncommitted delivery route.
 Note, a delivery route is committed once the e-retailer starts loading
 packages assigned to this delivery route onto the delivery vehicle
 assigned for this delivery route. At the end of every time-slot then, this
 framework assumes the e-retailer integrates the new customer requests by
 inserting these customer nodes into such uncommitted delivery routes in a
 manner that results in the least increase in distribution cost keeping the
 customer-distribution facility allocation fixed. Thus, the framework
 iterates through the time-slots with the e-retailer processing route
 commitments, accumulating customer requests, and subsequently integrating
 them into the delivery operations for the day.