# A weighted-sum method for solving the bi-objective traveling thief problem

arXiv: https://arxiv.org/abs/2011.05081 · HTML: https://mlsanity.com/paper/2011.05081/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [The Traveling Thief Problem with Time Windows: Benchmarks and Heuristics](https://mlsanity.com/paper/2604.06724/) (2026)

## Abstract (arXiv)

Many real-world optimization problems have multiple interacting components. Each of these can be NP-hard and they can be in conflict with each other, i.e., the optimal solution for one component does not necessarily represent an optimal solution for the other components. This can be a challenge for single-objective formulations, where the respective influence that each component has on the overall solution quality can vary from instance to instance. In this paper, we study a bi-objective formulation of the traveling thief problem, which has as components the traveling salesperson problem and the knapsack problem. We present a weighted-sum method that makes use of randomized versions of existing heuristics, that outperforms participants on 6 of 9 instances of recent competitions, and that has found new best solutions to 379 single-objective problem instances.