The document discusses railroad planning and optimization for freight operations in India. It notes the large scale of freight transportation by rail and the need to maximize efficiency given high capital costs. The solution framework involves collecting static and dynamic data on nodes, routes, rakes, and containers then using an optimization engine to allocate routes, rakes, and volumes monthly to maximize capacity utilization and minimize empty runs while meeting business rules. It provides an example network flow illustration to explain modeling transportation flows between nodes.

1. Railroad Planning & Optimization
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2. Table of Contents
• Industrial Requirement
• Our Approach
• Understanding Flow Network
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3. Industrial Requirements – Operating At A Large Scale
• Privatization of container rail operations in India, has created an overall freight
market (size of ~3bn tonnes) by trying to shift volumes from road to rail
• With 500 rakes expected to be operational in FY12/13, around 16 players are
offering integrated, value-added logistics solutions with last mile connectivity. This
includes few key players like Concor, Arshiya, Gateway Distriparks, etc.
Source of above: IDFC SSKI Sector Report, December 2009
• Given the capital investment in this segment is high, logistics players need to
operate with minimum resources yielding maximum capacity utilization
• Routes will overlap with each other, and under dynamic loading situation at each
nodes, forms a classic “Time and Capacity Constrained Routing Problem”
• Minimizing the empty run will be a tough nut to crack
• Practical conditions such as – customized containerization, maintenance cycle adds
to the planning difficulties
• Traditionally, railroad planning is being done using more than one methodologies
– MILP, Heuristics, NP-Hard. This requires solid conceptual understanding & real-
time tested algorithm
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4. Rail Planning Optimization Solution Framework
INPUT STATIC DATA INPUT DYNAMIC DATA
NODE ROUTE • Monthly forecasted loading data at each node
Location Route Id • Material freight rate on each route
Type (ICD/Port etc.) Source • Starting Rake & container availability data at
Max Rakes per Month Destination
each node
NODE ACTIVITIES Intermediate Nodes
• Capacity change for the next month
Activity (dwelling, Distance • Previous month transaction to update certain
customs etc.) time/cost parameters
Travel Time
Cost
Max Load Allowed
Time + Business Rules
Double Stacking Allowed
RAKE (such as maintenance
Rake Id
Max Rake Length run of 6,000 km)  Monthly Route-Rake
Type ROUTE FREIGHT allocation
Length Route Id  Number of rakes
Capacity (MT) Material required at each node
No of Rakes Freight Amount  Transportation volume,
CONTAINER From Date cost & time estimates
Container Id End Date
Optimization  Capacity utilization
Type Engine
 Capacity deficit
Material Specific
Capacity (MT)
No of Container
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5. Understanding Flow Network
•In this example, nodes (could be port,
IDC locations etc.) are shown
3
x32 •Each node has interconnections with the
other nodes
x23
2 •All Nodes have demand-supply estimates
x13
x24 x42
x31
x21 x12 •Once demand and supply or flow in/out is
x45 achieved for all nodes, we try to calculate
5 4 the number of movements between each
x51 x54 nodes, in the given set A of origin-
x15 destination (O-D) pairs
1
• In the equation below, no of movements
Figure: Network flow illustration
is represented by m and x is the flow
volume between i-j (O-D). This basically
becomes part of the optimization
objective function
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