A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands. (30th January 2020)

• Record Type: Journal Article
• Authors: Raba, David; Estrada‐Moreno, Alejandro; Panadero, Javier; Juan, Angel A.
• Is Part Of: International transactions in operational research. Volume 27:Number 6(2020)
• Page Start: 2785
• Page End: 2816
• Publication Date: 2020-01-30
• Subjects: multi‐period inventory routing problem -- stochastic demands -- online data -- biased randomization -- simheuristics; Operations research -- Periodicals; 003
• DOI: 10.1111/itor.12776
• Languages: English
• ISSNs: 0969-6016

Abstract

Abstract: In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.