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A travelling thief problem (TTP) is a proxy to real-life problems such aspostal collection. TTP comprises an entanglement of a travelling salesmanproblem (TSP) and a knapsack problem (KP) since items of KP are scattered overcities of TSP, and a thief has to visit cities to collect items. In TTP, cityselection and item selection decisions need close coordination since thethief's travelling speed depends on the knapsack's weight and the order ofvisiting cities affects the order of item collection. Existing TTP solvers dealwith city selection and item selection separately, keeping decisions for onetype unchanged while dealing with the other type. This separation essentiallymeans very poor coordination between two types of decision. In this paper, wefirst show that a simple local search based coordination approach does not workin TTP. Then, to address the aforementioned problems, we propose a humandesigned coordination heuristic that makes changes to collection plans duringexploration of cyclic tours. We further propose another human designedcoordination heuristic that explicitly exploits the cyclic tours in itemselections during collection plan exploration. Lastly, we propose a machinelearning based coordination heuristic that captures characteristics of the twohuman designed coordination heuristics. Our proposed coordination basedapproaches help our TTP solver significantly outperform existingstate-of-the-art TTP solvers on a set of benchmark problems. Our solver isnamed Cooperation Coordination (CoCo) and its source code is available fromhttps://github.com/majid75/CoCo

Solving Travelling Thief Problems using Coordination Based Methods