PhD Defense by Ilke Bakir

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Title: Large Scale Optimization Methods for Fleet Management in Long-Haul Transportation Networks

Advisor: Dr. Alan Erera


Committee Members:

Dr. Natashia Boland

Dr. Martin Savelsbergh

Dr. Alejandro Toriello

Dr. John-Paul Clarke


Date and time: Wednesday, November 1st, 3:00 PM.


Location: Groseclose Building, room 226A.




This thesis addresses modern challenges in transportation and logistics planning from both methodological and practical standpoints. First, we present a robust optimization model for facilitating empty repositioning of mobile resources in large-scale transportation networks, where demand is uncertain. We extend the "budget of uncertainty" idea to realistically represent empty repositioning settings, and propose a rolling horizon framework featuring this model. In the second part of this thesis, we introduce a novel integrated modeling approach for introducing alternative fuel trucks (AFTs) into long-haul diesel fleets, while taking into consideration strategic and operational aspects of the transportation network. We then provide exact and heuristic solution methodologies for finding optimal or good fleet replacement strategies using the integrated model. Finally, in the third study of this thesis, we provide a method for computing dual bounds for multistage stochastic mixed integer programming problems with a finite number of scenarios. These dual bounds, called “partition bounds”, are based on solving group subproblems on partitions of the scenario set. We develop a scenario set partition sampling method to obtain effective bounds, and demonstrate the power of this method against another sampling-based approach on a wide range of test instances.



  • Workflow Status:Published
  • Created By:Tatianna Richardson
  • Created:10/30/2017
  • Modified By:Tatianna Richardson
  • Modified:10/30/2017