Guided Ejection Search for the Job Shop Scheduling Problem
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A vehicle routing system to solve a periodic vehicle routing problem for a food chain in Hong Kong
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows
Computers and Operations Research
Arc-guided evolutionary algorithm for the vehicle routing problem with time windows
IEEE Transactions on Evolutionary Computation
Computers and Operations Research
Spatial, temporal, and hybrid decompositions for large-scale vehicle routing with time windows
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Computers and Operations Research
Computers and Operations Research
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Guided ejection search for the pickup and delivery problem with time windows
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
A powerful route minimization heuristic for the vehicle routing problem with time windows
Operations Research Letters
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Computers and Operations Research
Adaptive Path Relinking for Vehicle Routing and Scheduling Problems with Product Returns
Transportation Science
Computers and Operations Research
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The vehicle routing problem with time windows (VRPTW) is an important problem in logistics. The problem is to serve a number of customers at minimum cost without violating the customers' time-window constraints or the vehicle-capacity constraint. In this paper, we propose a two-stage algorithm for the VRPTW. The algorithm first minimizes the number of vehicles with an ejection pool to hold temporarily unserved customers, which enables the algorithm to go through the infeasible solution space. Then it minimizes the total travel distance using a multi-start iterated hill-climbing algorithm with classical and new operators including generalized ejection chains, which enable the algorithm to search a larger neighborhood. We applied the algorithm to Solomon's 56 VRPTW instances and Gehring and Homberger's 300 extended instances. The experimental results showed that the algorithm is effective and efficient in reducing the number of vehicles and is also very competitive in terms of distance minimization. The m-VRPTW is a variant of the VRPTW in which a limited number of vehicles is available. A feasible solution to m-VRPTW may contain some unserved customers due to the insufficiency of vehicles. The primary objective of m-VRPTW is to maximize the number of customers served. We extended our VRPTW algorithm to solve m-VRPTW and the experimental results showed consistently good performance of the algorithm when compared with other methods.