In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a problem of particular complexity. It is therefore appropriate to consider decision support tools that take into account the characteristics of the products, the needs and requirements of producers, sellers and final consumers. The paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. In particular, we propose a dynamic decision-making approach with a "moving" horizon that advances over time taking into account the more recent available information. This approach allows future decisions to be rescheduled in relation to new data, over a planning horizon of the same duration. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Southern Italy show the effectiveness of the proposed approach.

An age-based dynamic approach for distribution of perishable commodities with stochastic demands

Fattoruso, Gerarda;
2023-01-01

Abstract

In the operational, strategic and tactical decision-making problems of the agri-food supply chain, the perishable nature of the commodities can represent a problem of particular complexity. It is therefore appropriate to consider decision support tools that take into account the characteristics of the products, the needs and requirements of producers, sellers and final consumers. The paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. In particular, we propose a dynamic decision-making approach with a "moving" horizon that advances over time taking into account the more recent available information. This approach allows future decisions to be rescheduled in relation to new data, over a planning horizon of the same duration. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Southern Italy show the effectiveness of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11369/444433
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