Routing and Scheduling Cross-Town Drayage Operations

1. Routing and Scheduling Cross-Town Drayage Operations Mustafa SARAÇLAR Taha TAŞAN

2. J. B. Hunt Transportation Services Inc. • Headquartered in Lowell, Arkansas • US, Canada and Mexico • Job segments: • Dedicated contact service • Full-load dry van • Integrated capacity solutions • Intermodal transportation • In 2010, 56% of the total consolidated revenue intermodal tranportation

3. J. B. Hunt Transportation Services Inc. • «Cross-town» concept • Traditional transportation vs. Cross-town transportation

6. Cross-Town Transportation or «Rail Interchanges» • Could be performed by truck or rail, • In metropolitan cities, • «Ideal Transportation» concept: minimal empty miles • Minimum empty MOVES for our problem (proximity constraint) • Outbound volume vs. inbound volume (load imbalance) • «Bobtail» concept • Only between 10% and 50% of all moves (Maggiore et. al. 2007).

8. Cross-Town Drayage or «Rail Interchanges» (Cont’d) • Primary objective 1 is scheduling drivers for continuous loaded moves (satisfying transportation) • Difficult to perform «ideal transportation» • Third-party option • Primary objective 2 is maximization of driver utilization • Fixed number of drivers • Life quality of drivers • Increasing revenue per vehicle (driver)

9. Cross-Town Characteristics • Number of continuous-loaded moves • Traditional transportation vs. cross-town transportation « • Higher number of loads • Traffic constraints • «Load time windows» concept based on train schedules • Planning the internal and third-party moves is necessary (fixed number of drivers) • Constraints considered in the planning

10. Former Approaches Customized into Cross-Town Problem • Computationally efficient methodology

11. J. B. Hunt’s Current Methodology Not efficient! Regional and local transportation technique (2 or 3 moves/day) Cross-Town transportation technique (12 or more continuous moves/day)

12. The Model Objectives • Maximize the number of loads covered by the drivers • Minimize the total empty travel miles • Constraints: • Cover internally or third-party driver • Each driver must be assigned

13. Route Generation • Operationally feasible routes • Constraints; • Dispatch window constraint • ‘Driver’s total working time’ constraint • ‘Home depot’ constraint for driver

14. When load-time windows is ignored! Load Imbalance

15. Problem Complexivity and Difficulty Large number of loads, drivers, rail ramps e.g 500 loads, 12 continuous loaded moves => 4.13*1032possible routes Heuristic algorithims are required (Julia et al. 2005)

16. RouteGenerationHeuristic(Iterativeprocedurerepeatedforeachdriver)RouteGenerationHeuristic(Iterativeprocedurerepeatedforeachdriver) Feasible Move; The load must be available at the driver’s expected time of arrival Deliver the load to the destination ramp before scheduled delivery time

17. Bounds(Benchmarks): • Load Efficiency: • Selected load efficiency instead of distance-based metric • The difference in distance between ramps is negligible • Load Efficiency= 1.0  Perfect

18. Bounds: • DriverUtilization: • Fixedcost of the company drivers • Rail Schedules and Imbalances in supply and Demand Nodes Perfect Load Efficiency and Driver Utilization are not always possible

19. Mimicking Manual Operating Policies(Naïve Heuristic) Operational Planners had a list of preferred destination by descending priority Driver delivers a cross-town load at a rail ramp, planners look for the next load Best ramp on which to send the driver Ramp sequences generally reflect proximity to the originramp

20. Naïve Heuristic Process of searching for potential by descending sequence position Load is added to the drivers schedule If no available move is found, a bobtail move is required If no loads are available, driver must wait

21. Naïve Heuristic 2 MONTHS A move is added to the route sequence, potential loads are refreshed If a load is selected as part of the driver’s schedule, other drivers remove Until a driver’s schedule is filled Drivers must start and end their schedules at their home ramp

22. Balancing Heuristic Balance between origin and destination ramp pairs(Balance Score) Balance Score= Select the movement that has largest balance score Max balancing score=0  Bobtail

23. A Simple Example Four Cross-TownRamps Only one driver Driver’s day = Six time units  Load capacity=6 Begins and ends at ramp A Don’t enforce time window constraints

24. A Simple Example From

25. Naïve Heuristic Forramp (A,B,C,D) destinationsequencesare; A: B-C-D B: C-D-A C: D-A-B D: C-B-A

26. Naïve Heuristic

27. A Simple Example From

28. Balancing Heuristic

29. Numerical Results

30. Numerical Results

31. Numerical Results

33. Implementation and Challenges • Implemented balancing heuristic as a cross-town application(2010) • Change in a scheduled train arrival! • Estimated transit by time of day • Estimated dwell at ramp • Daytime-Nighttime characteristic

34. Impact & Success Manual Planning Automated and enhanced planning work IncreasedProductivity, Significantly fleet growing Improved sync between demand and supplyReduced Third Party Positive Financial Impact; $581,000 (Annual)

35. Examples From Turkey

36. Examples From Turkey

38. AcademicResearch from Turkey

39. References http://www.jbhunt.com/company/about/ http://iibfdergisi.gazi.edu.tr/index.php/iibfdergisi/article/view/143/134 http://www.marslogistics.com/en/mars_logistics_intermodal_transportation.aspx http://www.omsan.com.tr/en/sayfa.php?id=9&intermodal-transportation http://hurarsiv.hurriyet.com.tr/goster/haber.aspx?id=3571693&tarih=2005-11-28

40. ThankYou ! Questions ?