Abstract
Heuristic algorithms are most commonly applied in a competitive context in which the algorithm is tested on well-known benchmarks of some problem application with the objective of obtaining better performance results than the state-of-the-art. Focusing on characterising heuristic algorithm behaviour to acquire insight and knowledge of how these solution procedures operate given a certain problem application, is a rarely applied research context. In this paper we strive to obtain a better understanding of heuristic performance. Based on an exploratory analysis of a large neighbourhood search algorithm applied on instances of the vehicle routing problem with time windows, we perform a detailed study on one of the detected patterns and seek to explain it. We learn that a regret operator functions best when it can take into account many and good alternatives, which is not the case when removing geographical clusters of customers. In the latter case some customers become isolated and have no feasible insertion option in one of the existing routes at the start of the repair phase. Their insertion is therefore postponed, but we show that it is beneficial for performance to assign them a higher priority through the creation of individual routes.
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Notes
- 1.
Multicollinearity may lead to unstable coefficient estimates. This makes it difficult to interpret results. Including all destroy or repair variables would lead to perfect multicollinearity. One variable of each needs to be left out as a reference value.
- 2.
The antilog of the arithmetic mean of log-transformed values is the geometric mean. For positively skewed data, like our experimental data, the geometric mean will be less than the arithmetic mean and often a good estimate of the original median.
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Acknowledgments
The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government - department EWI.
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Corstjens, J., Caris, A., Depaire, B. (2019). Explaining Heuristic Performance Differences for Vehicle Routing Problems with Time windows. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_14
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