Assessing the sustainability of last-mile distribution strategies to manage expedited shipping with dynamic and stochastic demand

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.