Problem
Research questions and friction points this paper is trying to address.
Traveling Thief Problem
Time Windows
Multi-component Optimization
Combinatorial Optimization
The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics
🤖 AI Summary
This work proposes the Traveling Thief Problem with Time Windows (TTP-TW), a novel multi-component combinatorial optimization problem that extends the classical TTP by incorporating time window constraints, thereby coupling route planning and item selection. To address this new formulation, the authors introduce the first benchmark instance set for TTP-TW and develop an efficient heuristic algorithm that integrates and extends solution strategies from both the TTP and the Traveling Salesman Problem with Time Windows (TSPTW) to effectively manage the interdependent constraints between routing and item-picking decisions. Experimental results demonstrate that the proposed algorithm significantly outperforms existing approaches on the newly established benchmark instances, confirming its effectiveness and robustness.
📝 Abstract
While traditional optimization problems were often studied in isolation, many real-world problems today require interdependence among multiple optimization components. The traveling thief problem (TTP) is a multi-component problem that has been widely studied in the literature. In this paper, we introduce and investigate the TTP with time window constraints which provides a TTP variant highly relevant to real-world situations where good can only be collected at given time intervals. We examine adaptions of existing approaches for TTP and the Traveling Salesperson Problem (TSP) with time windows to this new problem and evaluate their performance. Furthermore, we provide a new heuristic approach for the TTP with time windows. To evaluate algorithms for TTP with time windows, we introduce new TTP benchmark instances with time windows based on TTP instances existing in the literature. Our experimental investigations evaluate the different approaches and show that the newly designed algorithm outperforms the other approaches on a wide range of benchmark instances.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Traveling Thief Problem
Time Windows
Heuristics
Benchmark Instances
Multi-component Optimization
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