Thermal Performance of Refrigerated Vehicles in the Distribution of Perishable Food

Abstract

ABSTRACT. The temperature of refrigerated products along the distribution process must be kept within close limits to ensure optimum food safety levels and high product quality. The variation of product temperature along the vehicle routing sequence is represented by non-linear functions. The temperature variability is also correlated with the time required for the refrigerated unit to recover after cargo unloading, due to the cargo discharging process. The vehicle routing optimization methods employed in traditional cargo distribution problems are generally based on the Travelling Salesman Problem with the objective of minimizing travelled distance or time. The thermal quality of routing alternatives is evaluated in this analysis with Process Capability Indices (PCI). Since temperature does not vary linearly with time, a Simulated Annealing algorithm was developed to get the optimal solution in which the minimum vehicle traveling distance is searched, but respecting the quality level expressed by a required minimum PCI value.

A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet

“…Hsu & Hung (2003) propose a model for the vehicle routing problem applied to refrigerated cargo transportation, minimizing transportation, inventory, and energy costs. In Novaes et al (2015), transporting refrigerated products focuses on ensuring that the products' temperature is preserved during the distribution process, minimizing its variability. In this case, the development of an algorithm based on simulated annealing is described to calculate the minimum distance of travel, considering the quality indicators based on the temperature variability along the route.…”

“…Different factors along the route could influence the temperature. Some examples of the factors affecting thermal conditions are the climate of the external environment, internal conditions of the load, insulation properties of the truck container, filtration of hot air, exposure time with the air from the external environment, relative humidity of the cargo, and quantity of refrigerated products inside the vehicle, among others (Novaes et al, 2015). For the transport of products with a refrigerated fleet, the energy consumption dedicated to cooling varies depending on various factors influenced by day.…”

Logistics network configuration for seasonal perishable food supply chains

“…Yakavenka, Mallidis, Vlachos, Iakovou and Eleni (2019) develop and employ a multi-objective perishable food LND sustainable model, the model incorporates trade-offs between three aspects of sustainability (cost, lead time and emission). The LND in the PFSC presents challenges, given the problems they face in the complex real systems (Novaes, Lima, De Carvalho & Bez, 2015), the multiplicity of decisions, scales, levels, periods, objectives and the interested parties (Miranda-Ackerman, Azzaro-Pantel & Aguilar-Lasserre, 2017).…”

A Multi-Objective Model for Sustainable Perishable Food Distribution Considering the Impact of Temperature on Vehicle Emissions and Product Shelf Life

“…Even small or infrequent deviations from recommended temperature settings can significantly reduce product shelf life [5]- [7] because increased temperature accelerates the growth rate of the microorganisms that are responsible for quality degradation in perishable foods [5], [8]. Although refrigerated vehicles' cargo is well-isolated, it can experience frequent exposure to increased temperature when the vehicle stops to make deliveries to other customers [2], [9]. As a result, an estimated 8-23% loss in perishable food quality occurs during the distribution process [10].…”

“…Hsu et al [31] calculated the volume of spoiled products based on the duration and ambient temperature of each delivery stop. Novaes et al [9] used commercial software to predict the temperature inside the shipping container, using timetemperature data to evaluate the quality of products at each delivery location via a statistical indicator in a traveling salesman problem.…”