Multi-objective shipment consolidation and dispatching problem

Büyükdeveci Ö., Özpeynirci S., Özpeynirci Ö.

Computers and Operations Research, cilt.169, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 169
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.cor.2024.106728
  • Dergi Adı: Computers and Operations Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Shipment consolidation, Augmented epsilon-constraint method, Variable neighborhood search
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

In recent years, with the increase in global production and demand, transportation problems have become a widely studied area, and studies focus on providing high-quality service at the lowest cost. This study considers a bi-objective shipment consolidation and dispatching problem with the objectives of minimizing the total cost and the total distance. To the best of our knowledge, this is the first study to include both objectives in this problem. Additionally, different from the literature, where usually predefined routes are assumed, we incorporate the routing decisions in our model. In order to create a non-dominated solution set, a multi-objective mixed integer linear programming model is developed and augmented ϵ-constraint method is used to generate the non-dominated frontier. However, this approach is not capable of finding the non-dominated solution set in a reasonable time, even for small-sized instances, and therefore, we propose a multi-objective variable neighborhood search heuristic. To measure the performance of the proposed approach, a computational experiment is conducted on randomly generated instances available in the literature. The experimental results indicate that the multi-objective variable neighborhood search heuristic performs efficiently in reasonable time.