The Generalized Location Routing Problem with Profits for planetary surface exploration and terrestrial applications

• Main Author: Ahn, Jaemyung
• Other Authors: Olivier L. de Weck.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2008
• Subjects: Aeronautics and Astronautics.
• Online Access: http://hdl.handle.net/1721.1/43077

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008. === Includes bibliographical references (p. 143-149). === As the scale of space exploration gets larger, planning of planetary surface exploration becomes more complex and campaign-level optimization becomes necessary. This is a challenging profit maximization problem whose decisions encompass selection of bases, technological options, routes, and excursion methods under constraints on a route, a mission, and a whole campaign. The Generalized Location Routing Problem with Profits (GLRPP) is developed in this thesis as a framework to solve this campaign optimization problem. A mathematical formulation for the GLRPP is developed and two solution methods to solve the GLRPP - a single phase method and a three-phase method - are presented. Numerical experiments for these two solution methods are carried out and their performance in terms of efficiency and effectiveness are analyzed. Two case studies are carried out. The first case study is a global Mars surface exploration campaign optimization. Problem instances for 100 potential bases and 1000 potential exploration sites are successfully solved using a three-phase solution method. A methodology to express the incremental value of a technology using exploration profits is demonstrated to evaluate an orbiting depot and in-situ resource utilization (ISRU). The second case study is a college football recruiting problem. A GLRPP instance is created out of the NCAA football division I-A schools and airports from which the schools can be reached. The problem is successfully solved using the three-phase solution method within a very small optimality gap. === by Jaemyung Ahn. === Ph.D.