The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics
Helen Yuliana Angmalisang, Frank Neumann
TLDR
This paper introduces the Traveling Thief Problem with Time Windows (TTPTW), provides new benchmarks, and proposes a superior heuristic.
Key contributions
- Introduces the Traveling Thief Problem with Time Windows (TTPTW), a real-world relevant variant.
- Develops new benchmark instances for TTPTW based on existing TTP data.
- Proposes a novel heuristic algorithm specifically for the TTPTW.
- Evaluates existing TTP/TSP-TW approaches and shows the new heuristic's superior performance.
Why it matters
This work addresses a critical gap by introducing time window constraints to the Traveling Thief Problem, making it more applicable to real-world logistics. The new benchmarks and a high-performing heuristic provide valuable tools for future research and practical applications in complex optimization.
Original 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.
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