IBM Container Transport Optimization Dr. Juergen Koehl, IBM Research and Development, Boeblingen

1. Juergen Koehl, IBM Container Logistics 19 March 2010 IBM Container Transport OptimizationDr. Juergen Koehl, IBM Research and Development, Boeblingen

2. 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

3. 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?

4. Empty Container Re-positioning Links Nodes • The model: • 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

5. Empty Container Re-positioning Links Nodes • The model: • 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

6. 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

7. Integration into the Customer IT Integrated, CustomizedWeb GUI ODM Studio (Planner) ODM Optimization Server ODM Scenario Database Existing Application ODM Repository OPL Studio Developer GUI uses component Data communication 7

8. Benefits of the Solution • Highly automated planning process • Exploit low-cost operations alternatives and counter-intuitive 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

9. 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