Efficient matheuristics to solve a rich production-routing problem.
Nenhuma Miniatura disponível
Data
2022
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
We present a rich production-routing problem having limited production and storage capacities at the plant,
limited storage capacity at the clients, a heterogeneous fleet subjected to a maximum riding time, and allowing
for back-orders to meet unfulfilled demands at penalty cost. As the problem scales quickly with the number of
customers, periods, products, and vehicles, three hybrid two-level decomposition approaches using a top-down
strategy were devised. The top tier determines the production and inventory levels, and the distribution of
goods via CPLEX, that is, it makes tactical decisions, while the bottom tier heuristically routes a heterogeneous
fleet in each period, that is, it makes operational decisions. The proposed methods rely on an iterated
local search framework that combines tailored perturbation schemes prioritizing either tactical or operational
decisions, or both. The main new feature of the algorithms is the adoption of an implicit cost that estimates
the delivery routing costs when making production, holding, and transportation decisions. This implicit cost
serves as an important guide to obtain improved solutions. The algorithms were tested over an extensive set
of instances, and the results demonstrated that all methods overcome CPLEX by obtaining more, better, and
faster solutions with much less computational effort. The devised heuristic, which prioritizes operational-level
decisions during the perturbation phase, attained the best overall results.
Descrição
Palavras-chave
Production-routing problem, Iterated local search, Hybrid methods, Matheuristics
Citação
REIS, A. F. da S. et al. Efficient matheuristics to solve a rich production-routing problem. Computers & Industrial Engineering, v. 171, artigo 108369, set. 2022. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0360835222004168>. Acesso em: 03 maio 2023.