Cosolver2B: An Efficient Local Search Heuristic for the Travelling Thief Problem

TL;DR

This paper introduces Cosolver2B, an efficient local search heuristic for the NP-hard Traveling Thief Problem, demonstrating improved solutions over existing methods in realistic routing scenarios.

Contribution

The paper presents a novel local search heuristic, Cosolver2B, specifically designed for the Traveling Thief Problem, enhancing solution quality compared to RLS and EA.

Findings

Cosolver2B outperforms RLS and EA in solution quality.

New best solutions were identified for benchmark TTP instances.

The heuristic shows promising efficiency in complex routing problems.

Abstract

Real-world problems are very difficult to optimize. However, many researchers have been solving benchmark problems that have been extensively investigated for the last decades even if they have very few direct applications. The Traveling Thief Problem (TTP) is a NP-hard optimization problem that aims to provide a more realistic model. TTP targets particularly routing problem under packing/loading constraints which can be found in supply chain management and transportation. In this paper, TTP is presented and formulated mathematically. A combined local search algorithm is proposed and compared with Random Local Search (RLS) and Evolutionary Algorithm (EA). The obtained results are quite promising since new better solutions were found.