A Market-based Solution to the Multiple Traveling Salesmen Problem

Authors:
Elad Kivelevitch;Kelly Cohen;Manish Kumar
Affiliations:
School of Aerospace Systems, University of Cincinnati, Cincinnati, USA 45221;School of Aerospace Systems, University of Cincinnati, Cincinnati, USA 45221-0070;Department of Mechanical, Industrial and Manufacturing Engineering (MIME), University of Toledo, Toledo, USA 43606-3390
Venue:
Journal of Intelligent and Robotic Systems
Year:
2013

Citing 17
Cited 0

A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems

SIAM Review
Valuation of network computing resources

Market-based control
An equilibratory market-based approach for distributed resource allocation and its applications to communication network control

Market-based control
A market approach to operating system memory allocation

Market-based control
Economic models for allocating resources in computer systems

Market-based control
Metaphor or reality: a case study where agents BID with actual costs to schedule a factory

Market-based control
An adaptive memory heuristic for a class of vehicle routing problems with minmax objective

Computers and Operations Research
Introduction to Multiagent Systems

Introduction to Multiagent Systems
Traveling Salesman Problems with Profits

Transportation Science
Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms

Computers and Operations Research
On Scaling Multi-Agent Task Reallocation Using Market-Based Approach

SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)

The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Routing for Relief Efforts

Transportation Science
Generalized Online Routing: New Competitive Ratios, Resource Augmentation, and Asymptotic Analyses

Operations Research
Consensus-based decentralized auctions for robusttask allocation

IEEE Transactions on Robotics
Probability Collectives: A multi-agent approach for solving combinatorial optimization problems

Applied Soft Computing
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a market-based solution to the problem of assigning mobile agents to tasks. The problem is formulated as the multiple depots, multiple traveling salesmen problem (MTSP), where agents and tasks operate in a market to achieve near-optimal solutions. We consider both the classical MTSP, in which the sum of all tour lengths is minimized, and the Min-Max MTSP, in which the longest tour is minimized. We compare the market-based solution with direct enumeration in small scenarios, and show that the results are nearly optimal. For the classical MTSP, we compare our results to linear programming, and show that the results are within 1 % of the best cost found by linear programming in more than 90 % of the runs, with a significant reduction in runtime. For the Min-Max case, we compare our method with Carlsson's algorithm and show an improvement of 5 % to 40 % in cost, albeit at an increase in runtime. Finally, we demonstrate the ability of the market-based solution to deal with changes in the scenario, e.g., agents leaving and entering the market. We show that the market paradigm is ideal for dealing with these changes during runtime, without the need to restart the algorithm, and that the solution reacts to the new scenarios in a quick and near-optimal way.