Ted Gifford Director – Research Group Schneider National Inc Facility Location Problems At Schneider Logistics

General Background Subsidiary of Schneider National –Largest truckload carrier in North America, 2001 Revenue $2.4 B, 14,000 tractors/drivers, 42,000 trailers SLI operates in 38 countries, employs 1,200 associates Freight Management Services –Global, Multi-Modal Transportation - Manage $2.5 B purchased transportation Engineering Services –Network and Route Design –Facility Location –Supply Chain Engineering Payment Services & Bid Management –Pay $7.5 billion third party invoices –Combined Value Auctions

Facility Location Design at SLI distinct problems / year 8 – 10 engineers participate, 30 engineers total Problem sizeAverageMax –Mfg facilities –DC sites –Customers –Products Major Industries/Clients –Consumer Products – KC (US & Europe) –Manufacturing – Otis Elevator (US & Europe) –Automotive - Ford (US & Europe) & GM –Paper products – Polyone –Home Delivery (food) – Schwan’s

Problem Types

Operational Issues Most problems are one-off -- A specific client request which often has unique complications or side constraints. Time & budget constraints often argue against sophistication or pursuit of a mathematically elegant solution. Limited availability or poor quality of data is often the main challenge and greatest consumer of effort. Realistic cost models are often either complex or ill- defined. Estimates of actual costs are highly variable. Service requirements are often ambiguous. Cost/service tradeoffs are unclearly specified. Forecasts of expected demand exhibit considerable uncertainty.

Current Process Access Database / Excel with VB Add-ins –Basic user interface –Navigation per problem taxonomy –Data import & maintenance AMPL w/ CPLEX –MIP models Set Covering P-center Fixed Charge P-median Special algorithms

Problem Taxonomy - 1

Problem Taxonomy - 2

Use of off-the-shelf software Some experience with CAPS Logic Tools, SLIM 2000, I2 Strategist Common difficulties –Underlying model and MIP Formulation not visible to user –Limited flexibility – rigid parameter options –Often necessary to “trick” the system for some simple problems –Poor performance for problem structures not anticipated by the model Example - CAPS several hours Excel add-in 15 seconds High cost per use – not general enough to handle all problem types

Factors which tend to complicate problems Ill-defined criteria Inconsistent or missing data Large number of candidate facilities Single sourcing requirements Disjunctive constraints Reverse logistics – container flow back Echelon skipping Minimum flow constraints Piecewise linear (or worse) cost functions

Current Development Activities Monte Carlo Simulation – Risk Analysis –Probability Distributions for Cost & Demand Constraint Programming –ILOG Solver / Dispatcher /OPL –Local Search Heuristics –Constraint Propagation / Domain Reduction –Index variables over enumerated sets –Special Purpose Algorithms for Global Constraints Conversion of AMPL to OPL –Convert Excel Solver routines –Utilize global & set constraints –Hybrid optimization – Cooperative Solvers