IISE’s Outstanding Capstone Senior Design Competition & Award A Rapid-Fire “Elevator Pitch” overview of the Finalists for the 2019-2020 Outstanding ISE Capstone Sr. Design Awards D. Scott Sink, Ph.D., P.E. Moderator Chairperson, IISE Honors and Awards Committee Chairperson of the Committee: Steve Savoie Sr. Mgr. IE GM Webinars that Matter in Times of Turblence 9 September 2020
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IISE’s Outstanding Capstone Senior Design Competition & Award · IISE’s Outstanding Capstone Senior Design Competition & Award A Rapid-Fire “Elevator Pitch” overview of the
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Intermodal Dray Driver Scheduling under Daily Freight Imbalance
using Forecasting and Stochastic Optimization
Irina Britten, Lexxy Gentile, Wesley Nimmo
Power BI Dashboard
J.B. Hunt Intermodal Dray Operations Our Approach
Impact Analysis
Headquartered in Lowell, Arkansas, J.B. Hunt Transport Services, Inc., is a Fortune 500 company comprised of four divisions. Our interest is in their largest division, J.B. Hunt Intermodal (JBI). JBI provides freight delivery services to customers using a nationwide network of railways (trains) and trucking terminals (drayage). We are focused on Dray Operations, which is responsible for planning the deliveries and pickups of truck loads for each customer
We recommend a dray operations driver schedule based on forecasted demand, simulation of demand uncertainty, and a stochastic optimization model.
Stochastic Optimization of Driver Schedules
The stochastic optimization model determines the number of drivers to employ for the next 7 days and how to distribute them throughout the week. The optimization model minimizes the total costs of employing and outsourcing drivers while ensuring that demand is met for each day in each scenario/
Delivery
Customer
TerminalEmpty
Loaded
Pickup
Customer
TerminalLoaded
Empty
Freight Imbalance in Dray Operations
Using tours increases dray operations productivity, but freight imbalance – more pickups than deliveries or vice versa – makes planning tours difficult.
JBI asked us to develop a data-driven methodology for establishing
weekly dray operations driver schedules based on forecasted
demand and freight imbalance.
Triple exponential smoothing produces reliable 7-day pickup/delivery forecasts by accounting for observed trends and seasonality during the previous 6 weeks.
Forecasting Pickups and Deliveries
Simulating Demand Uncertainty
A Monte Carlo simulation samples from the error term of the forecasting models to create 30 different demand scenarios for the upcoming 7 days.
The Power BI dashboard pulls historical data, executes our methodology, displays the recommended schedule, and analyzes the results.
Applying our tool to 2019 data demonstrates potential saving of in the millions of dollars per year, reductions in outsourcing, and reduced needs for driver employment.
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