The Value of Demand Forecasting in Stochastic Last-Mile Fleet Sizing and Composition Planning
Name
Updated Paper Matthias Juan January 2025.pdf
Description
FIle updated in January 2025
Size
12.74 MB
Format
Adobe PDF
Checksum (MD5)
730c1dab4a7cfe8392d66b6023927413
Author(s)
Zinnenlauf, Philipp
Pina-Pardo, Juan C
Winkenbach, Matthias
Date Issued
July 3, 2024
Abstract
Customer demand constitutes a crucial source of uncertainty in designing and operating complex and costly urban last-mile distribution operations. To mitigate associated risks, companies are diversifying their last-mile delivery options, exploring new vehicle types, and engaging in varied contracting schemes, encompassing vehicle rentals and spot market capacity utilization. We introduce a sequential learning and optimization problem integrating demand forecasting into a tactical last-mile fleet composition problem under uncertainty. Specifically, we propose a novel forecasting infrastructure and several machine learning models to predict customer demand in the medium-term future with high granularity. These forecasting results are then integrated into a two-stage stochastic program to derive cost-optimal fleet compositions. A real-world case study focusing on an e-commerce retailer in São Paulo, Brazil, reveals the economic viability of stochastic fleet composition planning informed by highly accurate demand forecasts. Our results show that accurate
demand forecasts enable e-commerce retailers to make cost-minimizing tactical decisions about the size, vehicle type, and governance structure of the rented vehicle fleet. Furthermore, our framework underlines the importance of implementing integrated decisions, where a fleet composition design is interlinked with forecasting methods to mitigate uncertainties.
Subjects
last-mile delivery
demand forecasting
stochastic fleet planning
two-stage stochastic program
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