The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics

TL;DR

This paper introduces a new variant of the Traveling Thief Problem with time windows, providing benchmarks and heuristics, and demonstrates the effectiveness of a novel heuristic approach through extensive experiments.

Contribution

It extends the TTP to include time windows, develops a new heuristic, and creates benchmark instances for this complex, real-world relevant problem.

Findings

The new heuristic outperforms existing approaches on benchmark instances.

Benchmark instances with time windows are successfully created and evaluated.

The adapted algorithms show varying performance, with the new heuristic generally superior.

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.