A Mixed-Integer Conic Program for the Multi-Agent Moving-Target Traveling Salesman Problem

The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a shortest path for an agent that starts at a stationary depot, visits a set of moving targets exactly once, each within one of their respective time windows, and returns to the depot. In this paper, we introduce a new Mixed-Integer Conic Program (MICP) formulation for the Multi-Agent Moving-Target Traveling Salesman Problem (MA-MT-TSP), a generalization of the MT-TSP involving multiple agents. Our approach begins by restating the current state-of-the-art MICP formulation for MA-MT-TSP as a Nonconvex Mixed-Integer Nonlinear Program (MINLP), followed by a novel reformulation into a new MICP. We present computational results demonstrating that our formulation outperforms the state-of-the-art, achieving up to two orders of magnitude reduction in runtime, and over 90% improvement in optimality gap.

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Publication Details

Published
2025-08-17
DOI
https://doi.org/10.1109/case58245.2025.11163754
Citations
1
Primary Topic
Vehicle Routing Optimization Methods
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article
Field-Weighted Citation Impact
1.24
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article

A Mixed-Integer Conic Program for the Multi-Agent Moving-Target Traveling Salesman Problem

Zhongqiang Ren, Sivakumar Rathinam, Howie Choset, Allen George Philip
1 citations
Vehicle Routing Optimization Methods
1.24
article

A Mixed-Integer Conic Program for the Multi-Agent Moving-Target Traveling Salesman Problem

Zhongqiang Ren, Sivakumar Rathinam, Howie Choset, Allen George Philip
article en
1 citations

Abstract

The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a shortest path for an agent that starts at a stationary depot, visits a set of moving targets exactly once, each within one of their respective time windows, and returns to the depot. In this paper, we introduce a new Mixed-Integer Conic Program (MICP) formulation for the Multi-Agent Moving-Target Traveling Salesman Problem (MA-MT-TSP), a generalization of the MT-TSP involving multiple agents. Our approach begins by restating the current state-of-the-art MICP formulation for MA-MT-TSP as a Nonconvex Mixed-Integer Nonlinear Program (MINLP), followed by a novel reformulation into a new MICP. We present computational results demonstrating that our formulation outperforms the state-of-the-art, achieving up to two orders of magnitude reduction in runtime, and over 90% improvement in optimality gap.

Carnegie Mellon University (US), Texas A&M University (US)
Openalex Percentile: Top 15%
Vehicle Routing Optimization Methods
1.24
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