An algorithm to compute time-balanced clusters for the delivery logistics problem

Adriana Menchaca-Méndez, Elizabeth Montero, Marisol Flores-Garrido, Luis Miguel-Antonio

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

An effective supply chain organization is fundamental for any manufacturing, distribution, retail or wholesale business. New technologies have made considerable improvements in the whole process of inventory management; Artificial Intelligence (AI) represents one of the best options for the industry and their search for more intelligent and robust logistics solutions. Based on a real-world scenario, we approach the challenge of defining delivery routes within a city such that the time they require to be traveled is approximately the same. Moreover, while the routes must ensure that drivers’ workload is time balanced and contract regulations can be met, they also must correspond to a customers’ partition (sectorization) according to well-defined, non-overlapping delivery areas. We introduce an approach to solve the problem through the algorithm HSAC (Hierarchical Simulated Annealing Clustering). The proposed algorithm first applies a divisive approach to the data, using simulated annealing at each step to create time-balanced partitions, and then solves the TSP problem to create optimal routes within the defined groups. Based on real data concerning two Mexican cities, our experimental results show that HSAC can solve the sectorization problem efficiently.

Original languageEnglish
Article number104795
JournalEngineering Applications of Artificial Intelligence
Volume111
DOIs
Publication statusPublished - May 2022

Keywords

  • Balanced clustering
  • Biobjective optimization
  • Simulated annealing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An algorithm to compute time-balanced clusters for the delivery logistics problem'. Together they form a unique fingerprint.

Cite this