Temperature control in refrigerated delivery vehicles is critical for
preserving product quality, yet existing approaches neglect critical
operational uncertainties, such as stochastic door opening durations and
heterogeneous initial product temperatures. We propose a framework to optimize
cooling policies for refrigerated trucks on fixed routes by explicitly modeling
these uncertainties while capturing all relevant thermodynamic interactions in
the trailer. To this end, we integrate high-fidelity thermodynamic modeling
with a multistage stochastic programming formulation and solve the resulting
problem using stochastic dual dynamic programming. In cooperation with industry
partners and based on real-world data, we set up computational experiments that
demonstrate that our stochastic policy consistently outperforms the best
deterministic benchmark by 35% on average while being computationally
tractable. In a separate analysis, we show that by fixing the duration of
temperature violations, our policy operates with up to $40$\% less fuel than
deterministic policies. Our results demonstrate that pallet-level thermal
status information is the single most crucial information in the problem and
can be used to significantly reduce temperature violations. Knowledge of the
timing and length of customer stops is the second most important factor and,
together with detailed modeling of thermodynamic interactions, can be used to
further significantly reduce violations. Our analysis of the optimal stochastic
cooling policy reveals that preemptive cooling before a stop is the key element
of an optimal policy. These findings highlight the value of sophisticated
control strategies in maintaining the quality of perishable products while
reducing the carbon footprint of the industry and improving operational
efficiency.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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