A heuristic and lower bound for a multi-depot routing problem
Computers and Operations Research
A tabu search heuristic for the multi-depot vehicle routing problem
Computers and Operations Research
Vehicle routing with pick-up and delivery: tour-partitioning heuristics
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
Heuristic solutions to multi-depot location-routing problems
Computers and Operations Research
Component scheduling for chip shooter machines: a hybrid genetic algorithm approach
Computers and Operations Research
Hybrid genetic algorithm for multi-time period production/distribution planning
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
A hybrid genetic algorithm for the design of water distribution networks
Engineering Applications of Artificial Intelligence
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
A new Hybrid Electromagnetism-like Algorithm for capacitated vehicle routing problems
Expert Systems with Applications: An International Journal
Review article: A review of soft computing applications in supply chain management
Applied Soft Computing
New upper bounds for the multi-depot capacitated arc routing problem
International Journal of Metaheuristics
An evolutionary approach to the multidepot capacitated arc routing problem
IEEE Transactions on Evolutionary Computation
Efficient stochastic hybrid heuristics for the multi-depot vehicle routing problem
Robotics and Computer-Integrated Manufacturing
Structure of Multi-Stage Composite Genetic Algorithm (MSC-GA) and its performance
Expert Systems with Applications: An International Journal
A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem
Expert Systems with Applications: An International Journal
A distribution network optimization problem for third party logistics service providers
Expert Systems with Applications: An International Journal
Genetic algorithm with iterated local search for solving a location-routing problem
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A variable neighborhood search for the multi-depot vehicle routing problem with loading cost
Expert Systems with Applications: An International Journal
Journal of Mathematical Modelling and Algorithms
Enhancing the performance of hybrid genetic algorithms by differential improvement
Computers and Operations Research
Incremental multiple-scan chain ordering for ECO flip-flop insertion
Proceedings of the International Conference on Computer-Aided Design
Expert Systems with Applications: An International Journal
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The distribution of finished products from depots to customers is a practical and challenging problem in logistics management. Better routing and scheduling decisions can result in higher level of customer satisfaction because more customers can be served in a shorter time. The distribution problem is generally formulated as the vehicle routing problem (VRP). Nevertheless, there is a rigid assumption that there is only one depot. In cases, for instance, where a logistics company has more than one depot, the VRP is not suitable. To resolve this limitation, this paper focuses on the VRP with multiple depots, or multi-depot VRP (MDVRP). The MDVRP is NP-hard, which means that an efficient algorithm for solving the problem to optimality is unavailable. To deal with the problem efficiently, two hybrid genetic algorithms (HGAs) are developed in this paper. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method and the nearest neighbor heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different problem sizes. It is proved that the performance of HGA2 is superior to that of HGA1 in terms of the total delivery time.