Tailoring Transportation Planning Decisions to Diverse Urban Environments

Abstract

As the world's population continues to urbanize, providing efficient and effective logistics service to customers in urban areas has become an increasingly important, but difficult task. Recent vehicle routing studies suggest that logistics planners face numerous difficulties during decision‐making due to certain complexities associated with the urban environment, but to date, specific challenges have remained unidentified. Using a multiple case study methodology, this research examines several environmental characteristics that influence vehicle routing decisions across eight U.S. urban centers, and contrasts decisions firms make when creating solutions to address these factors. Cross‐case synthesis uncovered patterns connecting specific environmental characteristics with variations on traditional vehicle routing decisions. Results allow us to construct a theoretical framework and propositions to guide managers when tailoring urban logistics strategies to urban environments having different combinations of structural characteristics.

Inductive research in last‐mile delivery routing: Introducing the Re‐Gifting heuristic

“…The trade‐off between efficiency and service is more pronounced in urban areas because there are numerous interacting factors making it difficult for drivers to serve customers (Rose et al, 2020) and nearly impossible to accurately predict system behavior (Beauregard, 2021; Healey, 2006). Vehicle routing research reflects the complications associated with serving customers in complex and uncertain systems.…”

“…After assigning stops to a route (see Appendix 1 for the complete technique), each set of stops is treated as a traveling salesman problem (TSP) using the following steps. (1) To minimize service failures and related costs, the delivery vehicle always services the furthest customer from the origin first, a common approach (Boyer, 1961; McPhee et al, 2015; Rose et al, 2020) among vehicle routing algorithms, and exactly follows the observed technique of practitioners. The vehicle then begins its return to the origin, adding stops along the way.…”

“…At its most basic, a method, called the “far‐point” (McPhee et al, 2015) or “out to in” heuristic (Rose et al, 2020), underlies this new heuristic, which attempts to maximize customer service and minimize failure costs in complex and uncertain urban environments. Interviews with last‐mile drivers and dispatchers revealed a basic technique with one dispatcher explaining they “just go [to the furthest] point out and work our way back in.…”

“…This research contributes to the literature by extending prior inductive research (McPhee et al, 2015; Rose et al, 2020) to develop a vehicle routing heuristic, inspired by a technique observed in practice that simply and inexpensively reduces the probability and impact of service failures. Further, we test this new heuristic against other simple algorithms regularly utilized by the small firms that accomplish so much of the last‐mile deliveries using classic benchmark problems with our results showing the conditions in which the proposed algorithm is most suitable.…”

Data‐Driven Driver Dispatching System with Allocation Constraints and Operational Risk Management for a Ride‐Sharing Platform

“…By exploiting stochastic information, Bent and Van Hentenryck (2004) propose a method that continuously generates routing plans for scenarios including known and future requests, then chooses one by a consensus function. A multiple‐case study is conducted by Rose, Mollenkopf, Autry, and Williams (2019), which examines several environmental characteristics that influence vehicle routing decisions across eight U.S. urban centers, and contrasts decisions firms make when creating solutions to address these factors.…”