Modeling Logic .... Database. Locomotive. Simulation. Model. Terminal Events.
Shop Routing ... Locomotives also go to shops for breakdown maintenance.
Robust Locomotive Planning through Simulation/Optimization Ravindra K. Ahuja
Professor, University of Florida & President, Innovative Scheduling
[email protected]
Collaborators A partnership between two companies:
CSX Team Kamalesh Somani Andy John
Innovative Scheduling Team Ravindra K. Ahuja Artyom Nahapetyan Zeynep Sargut
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Outline of the Presentation Motivation of the Locomotive Simulation Optimizer Modeling Logic Several Case Studies Demonstration
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Motivation A large freight railroad in USA owns 4,000 – 8,000 locomotives and has no system that can help its Locomotive Planning division for planning decisions. Sample planning decisions: How many locomotives to order next year? If business goes down, how many locomotives to put into storage? Relationship between on-time train performance and number of locomotives in the system. Do we have shortage of shop capacity? If yes, where to add shop capacity for largest impact? Affect of changes in the locomotive policy.
Locomotive management wants a laboratory to experiment with different ideas before implentation. 4
Locomotive Power Plan A railroad uses Locomotive Power Plan to assign consists to trains. Number of locomotives types (consist) to each train Train-train connections at each station Locomotive terminal dwell times
Example of locomotive power plan: Train Q123: 2×[SD40] Train Q130: 3×[SD40] + 2×[AC44]: Deadhead Train Q140: 2×[AC60]
However, due to various events and disruptions, Power Plan cannot be complied and changes must be made to the plan.
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Capture Reality within Locomotive Planning
Interaction with Unscheduled Network
Train Delays, Cancellations, Extra Trains
Breakdowns, Q Maintenance & Shop Process
Locomotive Power Plan
Tactical Fueling and Servicing
Tactical Repositioning
Real-life events causing disruption to the locomotive plan. 6
Need for Simulation and Optimization We need to simulate locomotive movements across the network: Create disruptions as they happen in reality (simulation) Respond to these events intelligently as managers do (optimization) Assess network performance measures
Sample network performance measures: % of On-Time Train Originations % of On-Time Train Arrivals Locomotive Dwell Time at Terminals % of Out-of-Service Locomotives % of Delayed Trains and Delay Hours % Compliance to the Power Plan Locomotive Utilization
To build this system, we need to integrate simulation with optimization. 7
Optimization versus Simulation Optimization-based approaches in railroads: Very popular and extensively used to create plans. Create ideal plan compliable without disruptions.
Replanning or disaster planning approaches: As disruptions happen, plans must be modified. These are also optimization-based approaches
How to assess the goodness of planning and replanning approaches? Need to integrate both approaches within a single system.
We need to combine optimization with simulation.
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Optimization within Simulation Optimization Planning Planning Methods Methods
Simulation Optimization Create Disruptions as in Reality
Replanning Replanning Methods Methods
This provides an excellent framework to test the robustness of planning and replanning methods. A laboratory to conduct experiments and test methods and ideas. 9
Status of Optimization within Simulation This is a relatively new area or less explored area of research. Current simulation methods are mostly rule-based methods. Optimization is done on input parameter values and using simulation as a black box. Inputs Parameters
Simulation Simulation Method Method
Adjust Parameters
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outputs
Select Best Parameters
Optimization-Guided Simulation Partnership between Simulation and Optimization.
Optimization
Simulation
Optimization will guide the hands of simulation.
We need to build such systems for railroad planning and scheduling.
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Outline of the Presentation Overview of the Locomotive Simulation Optimizer
Modeling Logic Several Case Studies Demonstration
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Locomotive Simulation Optimizer: Overview Event Generators Train Events
Initial State - Trains - Locomotives - Terminals - Shops
Locomotive Events
Terminal Events
Locomotive Simulation Model
Shop Events
Output Database
Decision Engines Train Arrival/Departure Modules
Shop Routing Module
Terminal Assignment Module
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Tactical Repositioning Module
Initial State of the Resources Trains Trains run as per their schedule
Locomotives Use locomotive assignments as in the power plan Evenly spread quarterly maintenances
Terminals Use terminal inventory as in the power plan Distribute spare locomotives randomly among shop locations
Shops Empty
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LSO: A Discrete Event Simulation Simulation Counter
# of runs=0
YES
Statistics and Reports
NO
# of runs
