Online optimization in routing and scheduling

In this thesis we study online optimization problems in routing and scheduling. An online problem is one where the problem instance is revealed incrementally. Decisions can (and sometimes must) be made before all information is available. We design and analyze (polynomial-time) online algorithms for a variety of problems. We utilize worst-case competitive ratio (and relaxations thereof), asymptotic and Monte Carlo simulation analyses in our study of these algorithms. The focus of this thesis is on online routing problems in arbitrary metric spaces. We begin our study with online versions of the Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP). We then generalize these basic problems to allow for precedence constraints, capacity constraints and multiple vehicles. We give the first competitive ratio results for many new online routing problems. We then consider resource augmentation, where we give the online algorithm additional resources: faster servers, larger capacities, more servers, less restrictive constraints and advanced information. We derive new worst-case bounds that are relaxations of the competitive ratio.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Wagner, Michael R. (Michael Robert), 1978-

Advisor dc:contributor.advisor

• Patrick Jaillet.

• Operations Research Center.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/36225

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/36225