Dynamic Forecast Demand Scenario Analysis for Automated Parcel Lockers

This document is a research article that presents a dynamic forecast demand scenario analysis aimed at designing an automated parcel lockers network in Pamplona, Spain, utilizing a simulation-optimization model. The study addresses disruptions in last mile delivery processes exacerbated by the SARS-CoV-2 pandemic, which has prompted the exploration of alternative urban logistics solutions. It focuses on self-collection delivery systems (SCDS) that enhance flexibility for courier companies and customers while reducing delivery times and gas emissions. The research integrates a System Dynamics Simulation Model (SDSM) to forecast e-commerce demand and proposes a bi-criteria Facility Location Problem (FLP) solved with an ε-constraint method. The findings indicate that the model can be scaled to other cities, providing insights into the optimization of automated parcel lockers. The paper includes a literature review, methodology, computational experiments, and results, highlighting the significance of APLs in urban logistics and their impact on customer behavior.