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Optimization of Dynamic Trip Plannin g Problem for Railroads

Optimization of Dynamic Trip Plannin g Problem for Railroads. Yazhe Feng Kimberly Ellis, Ph.D. Yasemin Merzifonluoglu , Ph.D. Center for Excellence in Logistics and Distribution Conference Dallas, TX October 20, 2010. Project Overview.

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Optimization of Dynamic Trip Plannin g Problem for Railroads

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  1. Optimization of Dynamic Trip Planning Problem for Railroads YazheFeng Kimberly Ellis, Ph.D. YaseminMerzifonluoglu, Ph.D. Center for Excellence in Logistics and Distribution Conference Dallas, TX October 20, 2010

  2. Project Overview • US railroads ship millions of shipments over their railroad networks annually. • To prevent shipments from being reclassified at every yard through which they pass, railroads group several shipments together to form a block. • Innovative Scheduling has developed a decision support system for the railroad blocking problem. • The focus of this project is the dynamic trip planning problem.

  3. Problem Description Given a blocking plan, train schedule, and block-to-train assignment, the dynamic trip planning problem assigns shipments to blocks and blocks to particular trains so that all operational constraints are satisfied and the total cost of transportation is minimized.

  4. Dynamic Trip Planning Problem

  5. Project Activities • Developed network representations • Train-block network

  6. Project Activities • Developed network representations • Train-block network • Time-space and Train-block network

  7. Project Activities • Developed large scale network flow model • Arc-based formulation • Conventional model • Large size! • Path-based formulation • Modeled for a set of feasible paths • Size reduced!

  8. Project Activities • Developed a sequential solution approach • Send waybills sequentially Train capacity is violated Find the (possible) minimum cost block path Determine the (possible) fastest train route Due date is not met

  9. Project Activities • Developed an alternative solution approach • Send all waybills together along their optimal block path and train route • Identify the train segment where the capacity constraint is violated, rearrange the waybills on that train segment • Prevent potential repetitive operations

  10. Project Activities • Implemented both sequential approach and alternative approach in C++ • Conducted analysis using data provided by Innovative Scheduling

  11. Case Scenario • Data provided by Innovative Scheduling for: • 3738 stations • 6146 blocks • 2259 trains • 455,888 waybills

  12. Case Scenario ResultsCost Savings

  13. Summary • Interesting, challenging, and realistic transportation problem. • Quality solutions need to be obtained quickly for large scale problems. • Both the sequential and alternative algorithms performed well.

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