Dynamic discretization discovery for solving the Continuous Time Inventory Routing Problem with Out-and-Back Routes

Felipe Lagos, Natashia Boland, Martin Savelsbergh

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

In time dependent models, the objective is to find the optimal times (continuous) at which activities occur and resources are utilized. These models arise whenever a schedule of activities needs to be constructed. A common approach consists of discretizing the planning time and then restricting the decisions to those time points. However, this approach leads to very large formulations that are intractable in practice. In this work, we study the Continuous Time Inventory Routing Problem with Out-and-Back Routes (CIRP-OB). In this problem, a company manages the inventory of its customers, resupplying a single product from a single facility during a finite time horizon. The product is consumed at a constant rate (product per unit of time) by each customer. The customers have local storage capacity. The goal is to find the minimum cost delivery plan with out-and-back routes only that ensures that none of the customers run out of product during the planning period. We study the Dynamic Discovery Discretization algorithm (DDD) to solve the CIRP-OB by using partially constructed time-expanded networks. This method iteratively discovers time points needed in the network to find optimal continuous time solutions. We test this method by using randomly generated instances in which we find provable optimal solutions.

Idioma originalInglés
Número de artículo105686
PublicaciónComputers and Operations Research
Volumen141
DOI
EstadoPublicada - may. 2022
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Dynamic discretization discovery for solving the Continuous Time Inventory Routing Problem with Out-and-Back Routes'. En conjunto forman una huella única.

Citar esto