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Multi-objective, multi-attribute fleet sizing in a dynamic and stochastic environment: A data-driven approach
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DOI：10.1016/j.tre.2025.104585.png)
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Abstract
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• Proposes a workflow for generating fleet performance predictive models • Method samples operational settings in a way that recognizes seasonality • Method uses black-box tool to generate solutions and performance metrics for samples • Curated data set simulates operational context of a grocery home delivery service • Fitted regression models have strong predictive power
Keywords:
Fleet sizing
Multi-compartment vehicles
Tactical planning
Regression model
Workflow
Machine learning
Attended home delivery
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