Juergen Koehl, IBM Container Logistics
19 March 2010
IBM Container Transport Optimization
Dr. Juergen Koehl, IBM Research and Development, Boeblingen
© 2010 IBM Corporation
Introduction
 Container transport volumes are increasing considerably over time
 Many operations planning models do not scale with this increase
 Efficiency is gained with sophisticated decision support systems
 The optimization potential of multi-modal transportation costs comes
– from the possibility to choose between different transport modes
– combine import and export of containers to minimize the empty tour segments
 In the case of empty container re-positioning additional saving come from the
different to/from location options as well as the accuracy increase of the forecast
© 2010 IBM Corporation
Challenges
 What is the cost optimal combination of transport modes and carriers?
 What is the trade off between cost and service level?
 How much equipment do I need? And where? And when?
 How do I react to disruptions of the operations? How do build a robust plan?
 Can I accept this transport request?
© 2010 IBM Corporation
Empty Container Re-positioning
 The model:
Links
Nodes
– Input:
• Network: Node and Links
• Scheduled and on demand connections
• Demand: Per container type
– Decision variables are the choice of
• Mode, carrier, time, location,
number and type of containers
– Constraints:
• Transport capacities
• Container demand for each location
• Storage capacities
• Business rules
– Cost function to be minimized:
• Transport costs
• Storage costs
• Equipment costs
© 2010 IBM Corporation
Empty Container Re-positioning
 The model:
Links
Nodes
– Input:
• Network: Node and Links
• Scheduled and on demand connections
• Demand: Per container type
– Decision variables are the choice of
• Mode, carrier, time, location, number
• and type of containers
– Constraints:
• Transport capacities
• Container demand for each location
• Storage capacities
• Business rules
– Cost function to be minimized:
• Transport costs
• Storage costs
• Equipment costs
Linear Model
∑ size(c) ≤ cap
Minimize
Transport cost
+ storage cost
+ handling cost
© 2010 IBM Corporation
The solution: IBM ILOG Optimization Decision Manager (ODM)
 ODM is the industry leading platform for developing custom optimization-based
planning and scheduling applications.
 CPLEX is the leading optimization engine for linear optimization problems
© 2010 IBM Corporation
Integration into the Customer IT
Integrated, Customized
Web GUI
ODM Studio (Planner)
Existing Application
ODM Optimization Server
ODM Repository
OPL Studio Developer
GUI uses component
Data communication
© 2010 IBM Corporation
7
Benefits of the Solution
 Highly automated planning process
 Exploit low-cost operations alternatives and counterintuitive low-cost decisions
 Supports strategic analysis:
– Optimize the number of containers needed
– Reduces the size of the container fleet
 Reduced annual logistics expenses of empty
container repositioning by 5 – 15%
 Implemented in ILOG industry standard optimization
technology
– Provides an optimization platform for future
solutions
 Supports incremental planning to react to disruption
 Fully customized to the existing IT Infrastructure,
business process and requirements
© 2010 IBM Corporation
Other Applications of Container Transport Optimization
 Overseas container transport and re-positioning
 Re-positioning of empty rail cargo freight cars:
~ 100 different types of freight cars
~ 1000 ‘nodes’ or freight train depots
Scheduled cargo train connections
 Container transport in the automotive industry
 Forecasting of container demand
© 2010 IBM Corporation