Volume 10, Issue 1, 2017, Pages 1337 - 1344
Evolutionary Multi-objective Optimization for Multi-depot Vehicle Routing in Logistics
Authors
Xiaowen Bixiaowenbi2-c@my.cityu.edu.hk, Zeyu Hanzeyuhan2-c@my.cityu.edu.hk, Wallace K. S. Tangeekstang@cityu.edu.hk
Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR
Received 29 March 2017, Accepted 30 August 2017, Available Online 14 September 2017.
- DOI
- 10.2991/ijcis.10.1.94How to use a DOI?
- Keywords
- Multi-depot vehicle routing; multi-objective optimization; evolutionary algorithm; local search
- Abstract
Delivering goods in an efficient and cost-effective way is always a challenging problem in logistics. In this paper, the multi-depot vehicle routing is focused. To cope with the conflicting requirements, an advanced multi-objective evolutionary algorithm is proposed. Local-search empowered genetic operations and a fuzzy cluster-based initialization process are embedded in the design for performance enhancement. Its outperformance, as compared to existing alternatives, is confirmed by extensive simulations based on numerical datasets and real traffic conditions with various customers’ distributions.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
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TY - JOUR AU - Xiaowen Bi AU - Zeyu Han AU - Wallace K. S. Tang PY - 2017 DA - 2017/09/14 TI - Evolutionary Multi-objective Optimization for Multi-depot Vehicle Routing in Logistics JO - International Journal of Computational Intelligence Systems SP - 1337 EP - 1344 VL - 10 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.10.1.94 DO - 10.2991/ijcis.10.1.94 ID - Bi2017 ER -