Abstract
The well-known traveling salesman problem (TSP) is concerned with determining the shortest route for a vehicle while visiting a set of cities exactly once. We consider knowledge and algorithm engineering in combinatorial optimization for improved solving of complex TSPs with Time Windows (TSPTW). To speed-up the exploration of the applied Nested Monte-Carlo Search with Policy Adaption, we perform beam search for an improved compromise of search breadth and depth as well as automated knowledge elicitation to seed the distribution for the exploration. To evaluate our approach, we use established TSPTW benchmarks with promising results. Furthermore, we indicate improvements for real-world logistics by its use in a multiagent system. Thereby, each agent computes individual TSPTW solutions and starts negotiation processes on this basis.
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