EURO 2024 Copenhagen
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2587. Real-Time Temperature Control for Refrigerated Trucks: A Multistage Stochastic Programming Approach

Invited abstract in session WD-39: Analysis of Stochastic Models II, stream Stochastic Modelling.

Wednesday, 14:30-16:00
Room: 35 (building: 306)

Authors (first author is the speaker)

1. Francesco Giliberto
Operations Analytics, Vrije University

Abstract

In this work, we study the problem of transporting temperature-sensitive products in refrigerated trucks, where maintaining temperatures within specified ranges is crucial to prevent quality degradation. Innovative routing models are characterized by several limitations, such as the lack of a direct link between temperature and the variables influencing its dynamics and the assumption of deterministic scenarios. Our work introduces a novel model that treats temperature as a dynamic variable influenced by multiple factors, including cooling unit operation, and incorporates uncertainties such as door opening durations and initial product temperatures. Our approach involves optimizing a high-frequency control policy for cooling units by casting the problem as a multistage stochastic program, with each client stop representing a stage. This method poses computational challenges due to the extensive number of stages and the complexity of thermodynamic modeling. To address this, we employ the Stochastic Dual Dynamic Programming algorithm and validate our methodology through a case study with real-world data from a specialized Dutch logistics company. The findings demonstrate that our strategy surpasses both deterministic lookahead strategy and current industry practice in maintaining optimal product temperatures while minimizing energy consumption. This advancement holds significant potential for enhancing the efficiency and sustainability of refrigerated transport logistics.

Keywords

Status: accepted

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