# A Time-Dependent TSP Formulation for the Design of an Active Debris Removal Mission using Simulated Annealing

arXiv: https://arxiv.org/abs/1909.10427 · HTML: https://mlsanity.com/paper/1909.10427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous](https://mlsanity.com/paper/2409.16882/) (2024)
- [Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal](https://mlsanity.com/paper/2602.05091/) (2026)
- [Optimal Multi-Debris Mission Planning in LEO: A Deep Reinforcement Learning Approach with Co-Elliptic Transfers and Refueling](https://mlsanity.com/paper/2602.17685/) (2026)

## Abstract (arXiv)

This paper proposes a formulation of the Active Debris Removal (ADR) Mission Design problem as a modified Time-Dependent Traveling Salesman Problem (TDTSP). The TDTSP is a well-known combinatorial optimization problem, whose solution is the cheapest mono-cyclic tour connecting a number of non-stationary cities in a map. The problem is tackled with an optimization procedure based on Simulated Annealing, that efficiently exploits a natural encoding and a careful choice of mutation operators. The developed algorithm is used to simultaneously optimize the targets sequence and the rendezvous epochs of an impulsive ADR mission. Numerical results are presented for sets comprising up to 20 targets.