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Empty Railcar and Locomotive Distribution at NS

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Title: Empty Railcar and Locomotive Distribution at NS


1
Empty Railcar and Locomotive Distribution at NS
2
Agenda
  • Car distribution model (CDM)
  • Locomotive routing model (LRM)
  • Network planning and disruption management using
    OR models

3
Car Distribution Model (CDM)
  • CDM is a decision support system for car
    distributors and management.
  • Recommend optimal empty car distribution plan to
    assist car distributors.
  • Provide decision support reports using historical
    data.
  • Goal of efficient car distribution is to reduce
    empty transit time for a given load and increase
    the number of orders furnished using existing car
    fleet.
  • Provide what-if scenarios capability to test
    impacts of business rule and supply/demand
    changes

4
Car Cycle in CDM
Minimize time
Unload car
Move empty car
Move loaded car
Move empty car
Load car
M
T
W
Th
F
Sa
Su
M
T
W
Th
F
Sa
Su
5
Modeling the Business
  • Model uses a planning period of 3 weeks.
  • Car demand is a combination of orders placed and
    forecasted.
  • Car supply is projected from car movement events,
    and next earliest empty available times are
    computed from OPD (plan) and DTSR (train
    arrival).
  • Other car distribution business rules are
    modeled.
  • Unit train moves
  • Different car preferences with priorities
  • Customer priorities
  • Destination of orders
  • Cars going offline

6
Optimization frameworks
  • Using standard network transformation

Each link captures the marginal reward of an
additional car.
7
A car distribution problem
  • Using value function approximations, we may
    reposition cars before orders become known

8
Multistage problems
t
9
Multistage problems
10
Multistage problems
11
CDM Computational Approach
  • Formulates a decision flow problem in a
    time-space network.
  • In this network, arcs are decisions and nodes are
    resources.
  • Solves this using optimizing-simulator technology
    to maximize car utilization.
  • Invokes solution methods from dynamic programming
    with learning.

12
Mathematical Formulation
  • The full simulation problem is very large. So the
    problem is decomposed into manageable Network
    LP sub-problems over several time periods.
  • Minimize sub-problem objective function
  • Decision flow costs penalty costs - decision
    benefits.

13
Multiple Decisions on Resources
14
CDM Outputs
  • Car Movements (hold, reposition, loaded,
    empty-to-load, load-to-empty).
  • Order Decisions (hold, loaded move,
    empty-to-load, load-to-empty, dropped).
  • VFAs that portray the values of various moves at
    the end of optimization.
  • Many other stats and details
  • Mileage
  • Transit time
  • Cycle time
  • Order furnishing rate

15
Current Status
  • The car distribution model for open coil cars
    rolled out in January 2005. The improvement in
    car utilization has been significant.
  • There was a reduction in empty transit time of
    open coils cars in spite of an overall reduction
    in network velocity during the corresponding
    period.

16
Open Coil Statistics
17
Next Steps
  • Continue Implementation of CDM for the following
    car types
  • Covered coil cars in November 2005
  • Gondola cars in March 2006 .
  • Proposal for extending CDM for boxcars has
    received full support from NS management and
    capital fund has been allocated for boxcar in
    2006.
  • Enhance the model to be flexible and scalable so
    that it can be applied to boxcars.

18
Locomotive Routing Model (LRM)
  • A real-time system that
  • Assigns locomotives to trains over a planning
    horizon given a train schedule.
  • Uses complex business rules developed in
    co-ordination with the cage.
  • Uses the locomotive operating plan as input.
  • Produces a realistic flow of trains and
    locomotives for the given time horizon.
  • Uses optimization model to recommend locomotive
    repositioning and light engine moves to balance
    the surplus and deficit of locomotives.
  • Uses an optimization engine to improve locomotive
    utilization and reduce train delay.
  • Developed jointly by Norfolk Southern and Castle
    Lab of Princeton University.

19
Inputs
  • Train schedules
  • TSR/DTSR
  • Train tonnages and historical performance
  • Locomotives available for future assignment
  • Existing NS fleet
  • Locomotive Operating Plan
  • Inbound/outbound connection
  • Business Rules and Constraints
  • Power termination locations by train symbol
  • Special equipment (LSL,CAB,TOILET) requirements
    by train symbol
  • Special locomotive consist configuration (number
    of leaders, back-to-back) requirements by train
    symbol
  • Foreign Cycles and Coupled Trains
  • Locomotive tonnage ratings
  • Locomotive characteristics
  • Consist busting
  • FRA mandated maintenance

20
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24
Outputs
  • Locomotive assignment recommendations
  • Locomotive surplus/deficit by terminal, division
    and region
  • Consist on each train.
  • Train Delays due to locomotive unavailability.
  • Locomotive terminal dwells.
  • Locomotive operating plan compliance.
  • Locomotive Utilization.

25
Status and Next Steps
  • LRM started in 1996, but was put on hold in 2002
    due to
  • Lack of user support
  • Variations in train departures and arrivals
  • Poor visibility of on-demand (or unscheduled)
    trains
  • Currently, there are interests at NS to use LRM
    for strategic planning
  • Impact of train performance on locomotive
    utilization and requirements
  • Locomotive fleet sizing
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