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
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
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
A Compressed-Annealing Heuristic for the Traveling Salesman Problem with Time Windows
INFORMS Journal on Computing
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Beam-ACO for the travelling salesman problem with time windows
Computers and Operations Research
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
A modified ant colony system for solving the travelling salesman problem with time windows
Mathematical and Computer Modelling: An International Journal
A General VNS heuristic for the traveling salesman problem with time windows
Discrete Optimization
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In combinatorial optimization it is not rare to find problems whose mathematical structure is nearly the same, differing only in some aspect related to the motivating application. For example, many problems in machine scheduling and vehicle routing have equivalent formulations and only differ with respect to the optimization objective, or particular constraints. Moreover, while some problems receive a lot of attention from the research community, their close relatives receive hardly any attention at all. Given two closely related problems, it is intuitive that it may be effective to adapt state-of-the-art algorithms-initially introduced for the well-studied problem variant-to the less-studied problem variant. In this paper we provide an example based on the travelling salesman problem with time windows that supports this intuition. In this context, the well-studied problem variant minimizes the travel time, while the less-studied problem variant minimizes the makespan. Indeed, the results show that the algorithms that we adapt from travel-time minimization to makespan minimization significantly outperform the existing state-of-the-art approaches for makespan minimization.