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A mathematical modeling approach to improving locomotive utilization at a freight railroadPowerPoint Presentation

A mathematical modeling approach to improving locomotive utilization at a freight railroad

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A mathematical modeling approach to improving locomotive utilization at a freight railroad

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A mathematical modeling approach to improving locomotive utilization at a freight railroad

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A mathematical modeling approach to improving locomotive utilization at a freight railroad

Kuo and Nicholls

- Rail has lost business to other modes in the past but is recapturing lost business
- Fuel efficiency advantage
- Computerized scheduling and routing
- Upgrading of equipment, terminals, etc.
- Improved railcar identification system
- M&A for scale economies

- This paper discusses one approach which Conrail has taken to improve efficiency

Background

- Conrail (at the time of study)
- 11,700-mile rail network
- Over 2,000 engines

- Challenges
- Efficiently position train crews and engines
- 12-hour on-duty constraint
- Return home or lodging after 12 hours
- Geographic imbalances of locomotive availability due to variable traffic pattern
- “Light” engine moves are necessary
- Minimize light engine moves

Purpose

- Develop a math model to minimize cost of light engine moves
- Cost savings can be large because
- Engines value $1.1 billion
- Current operation is based on expert judgment

- Difference from previous studies
- Schedule assumed to repeat on a 7-dat cycle (not 24 hours)
- Cost of light engine moves emphasized (not treated as sub-problem)

Model

- Minimize the cost of light engine move
- Fixed cost = labor cost, taxi cost, lodging cost, over-mileage cost
- Variable cost = fuel cost
- Decision variables
- Distribution of engines among yards at the start of each week
- Necessary light engine moves between yards

- Constraints
- Engine (horsepower) requirements
- No more than 15 light engine moves per day
- Other “common sense” conditions

Illustrative Application

- Data
- Three-yard data (from Conrail)
- Assumed closed system
- 16 available engines (minimum needed)
- 105 decision variables, 106 constraints

- Results
- Minimized cost = $4,920.22
- Current method = $6,233.97
- Saving of $1,313.75 (about 21%)
- In reality, cost savings can be larger (more opportunities for savings)

Sensitivity Analysis

- Increased the available engines from 16 to 17
- Investigate if increasing the fleet size is better (trade off between fleet size and light move)
- Minimized cost = $3,823.26 (saving of $1,096.96)
- Equivalent to $57,000 per year
- Worth increasing the fleet size?
- Acquisition cost of an engine = $1.5 million
- Can be used for 30 years
- In reality the savings can be larger

Conclusion and limitation

- Cost saving potential
- Can learn from airline industry
- But be aware of limitations
- Engines are often exchanged among carriers
- Crews do not always stay at hotels (go home, “held-away-from-home” cost
- Train schedules change constantly over time
- Only the scheduled trains are considered
- One type of engine is assumed
- Maintenance downtime is ignored

Discussion questions

- What are implications of this study to railroads?
- Are railroads doing better job than airlines or motor carriers (in efficiency)?
- Is the proposed model usable in the field?
- What are pros and cons of railroads (as opposed to other mdoes)?
- What are the future of railroads? What should they do to increase the share of business?