A sampling-based heuristic for tree search applied to grammar induction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A New Heuristic for the Traveling Salesman Problem with Time Windows
Transportation Science
Linear Time Dynamic-Programming Algorithms for New Classes of Restricted TSPs: A Computational Study
INFORMS Journal on Computing
A Hybrid Exact Algorithm for the TSPTW
INFORMS Journal on Computing
Ant Colony Optimization
A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows
INFORMS Journal on Computing
Computers and Operations Research
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Optimization of the nested Monte-Carlo algorithm on the traveling salesman problem with time windows
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Expert Systems with Applications: An International Journal
New State-Space Relaxations for Solving the Traveling Salesman Problem with Time Windows
INFORMS Journal on Computing
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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The travelling salesman problem with time windows is a difficult optimization problem that arises, for example, in logistics. This paper deals with the minimization of the travel-cost. For solving this problem, this paper proposes a Beam-ACO algorithm, which is a hybrid method combining ant colony optimization with beam search. In general, Beam-ACO algorithms heavily rely on accurate and computationally inexpensive bounding information for differentiating between partial solutions. This work uses stochastic sampling as a useful alternative. An extensive experimental evaluation on seven benchmark sets from the literature shows that the proposed Beam-ACO algorithm is currently a state-of-the-art technique for the travelling salesman problem with time windows when travel-cost optimization is concerned.