LogNiti Lab

The conventional approach to modelling last-mile distribution operations involves use of the discrete formulation method which renders a representative mathematical model necessitating use of sophisticated solution techniques. While such an intensive approach is justified when decision-makers require a precise plan to support operational planning, this level of precision is redundant for strategic planning, wherein available information is representative but not necessarily exact. To this end, continuous approximation (CA) method offers a practical alternative with use of continuous density functions that estimate parameters approximately, thus striking a balance between estimation accuracy and computational effort. However, typical CA-based routing frameworks assume a simplified distribution environment that significantly limits the capability of such frameworks to accurately model last-mile operations in real-world distribution environments, such as postal service, e-commerce, q-commerce, waste collection, emergency service etc. Thus, the objective of this work is to develop a holistic CA framework for the Vehicle Routing Problem (VRP) to facilitate strategic last-mile distribution planning within diverse logistic settings. Specifically, this study synthesizes a total of 1080 distribution environments, each with peculiar distribution structure and distinctive customer characteristics. Subsequently, it optimizes last-mile distribution for distribution environment instance using an Adaptive Large Neighborhood Search (ALNS) metaheuristic. Finally, this work develops a robust CA-based functional form through symbolic regression to precisely estimate the total distribution tour length across the various distribution environments. In doing so, this work supports strategic decision-making for a wide scope of logistic operators that may have representative but not necessarily exact information.

As freight demand grows, so do its negative externalities, such as health and environmental impacts. This study integrates a reduced-complexity air quality model (RCM) with a multimodal traffic assignment model to analyze the scenario in which trucks minimize their public health costs. The study compares the least health-cost path (LHCP) with the shortest, fastest, and least-emissions paths. Two LHCP scenarios with geofences around sensitive populations differed in travel time (TT), health costs, and community impacts. LHCP had 10.9% higher TT and 29% lower costs than the next lowest scenarios. Under the LHCP, within-scenario burdens increased for some communities (e.g., PM2.5 burden) but decreased for others (e.g., ozone burden). The other routing scenarios similarly produced differential effects across communities, highlighting tradeoffs. Geofences increased TT and health costs relative to the LHCP and produced different community impacts. This study provides new, actionable insights into methods to mitigate transportation externalities, particularly in communities.

As many e-retailers compete through increasingly consumer-focused services, urban freight faces a critical need for sustainable alternate last-mile distribution practices. While previous research has investigated the performance of various distribution strategies under different planning and network design scenarios, their performance under the dynamic and stochastic conditions inherent to consumer-focused services (e.g., expedited deliveries) is not well understood. This study introduces a dynamic-stochastic last-mile network design (DS-LMND) problem, formulated as a multi-echelon capacitated location routing problem with time-windows (ME-C-LRP-TW), to address this gap. Consequently, this work develops a Monte Carlo simulation–optimization framework integrated with an adaptive large neighborhood search (ALNS) metaheuristic to solve the problem. Using this framework, the authors configure the distribution structure and simulate last-mile operations for each distribution strategy. For these configured distribution structures, this work determines economic viability, environmental efficiency, and social equity – the three pillars of sustainability. Further, the authors determine the impact of demand uncertainties on the sustainability of last-mile distribution through the value of information (VI) and coefficient of variance (CV) metrics. In doing so, this work a) estimates the efficacy of conventional distribution strategy; b) confirms the competitiveness of electric delivery vehicles; c) evaluates the effectiveness of crowdsourced delivery services; d) advances the case for consolidation-based multi-echelon distribution strategies; e) establishes the rationale for customer pickups; and f) develops the use case for drone and robots, to cater to dynamic and stochastic demand with expedited shipping.