A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm
The green open vehicle routing problem with time windows has been widely studied to plan routes with minimal emissions in third-party logistics. Due to the NP-hardness, the performance of the general heuristics significantly degrades when dealing with large-scale instances. In this paper, we propose a membrane-inspired hybrid algorithm to solve the problem. The proposed algorithm has a three-level structure of cell-like nested membranes, where tabu search, genetic operators, and neighbourhood search are incorporated. In particular, the elementary membranes (level-3) provide extra attractors to the tabu search in their adjacent level-2 membranes. The genetic algorithm in the skin membrane (level-1) is designed to retain the desirable gene segments of tentative solutions, especially using its crossover operator. The tabu search in the level-2 membranes helps the genetic algorithm circumvent the local optimum. Two sets of real-life instances, one of a Chinese logistics company, Jingdong, and the other of Beijing city, are tested to evaluate our method. The experimental results reveal that the proposed algorithm is considerably superior to the baselines for solving the large-scale green open vehicle routing problem with time windows.
Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm
The green open vehicle routing problem with time windows has been widely studied to plan routes with minimal emissions in third-party logistics. Due to the NP-hardness, the performance of the general heuristics significantly degrades when dealing with large-scale instances. In this paper, we propose a membrane-inspired hybrid algorithm to solve the problem. The proposed algorithm has a three-level structure of cell-like nested membranes, where tabu search, genetic operators, and neighbourhood search are incorporated. In particular, the elementary membranes (level-3) provide extra attractors to the tabu search in their adjacent level-2 membranes. The genetic algorithm in the skin membrane (level-1) is designed to retain the desirable gene segments of tentative solutions, especially using its crossover operator. The tabu search in the level-2 membranes helps the genetic algorithm circumvent the local optimum. Two sets of real-life instances, one of a Chinese logistics company, Jingdong, and the other of Beijing city, are tested to evaluate our method. The experimental results reveal that the proposed algorithm is considerably superior to the baselines for solving the large-scale green open vehicle routing problem with time windows.
Solving the Green Open Vehicle Routing Problem Using a Membrane-Inspired Hybrid Algorithm
Yunyun Niu (author) / Zehua Yang (author) / Rong Wen (author) / Jianhua Xiao (author) / Shuai Zhang (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Solving the Multi-Depot Green Vehicle Routing Problem by a Hybrid Evolutionary Algorithm
| DOAJ | 2020
Dynamic Vehicle Routing Problem Using Hybrid Ant System
| British Library Conference Proceedings | 2003
Dynamic vehicle routing problem using hybrid ant system
| IEEE | 2003
Solving QoS Multicast Routing Problem Based on Heuristic Ant Algorithm
| British Library Online Contents | 2002