1. Truck set-up scheduling for the Elgin, IL facility Vijay Viswanathan, Graduate Student Dr. Jaejin Jang, Associate Professor Department of Industrial Engineering

2. Overview of Presentation Introduction Detailed Problem Description Objective of Project Assumptions The Scheduling Algorithm Simulation Input Summary of Results Conclusion

3. Introduction Wisconsin Lift Truck corporation sells, rents, services trucking equipment like … –Aerial lift and construction equipment –Cranes –Lift Trucks –Industrial Cleaning Equipment –Loaders –Pallet Trucks –Personnel Carriers

4. Introduction They have seven locations in Wisconsin and two locations in Illinois Our contact for this project is –Kristine Rauch Industrial Engineer, WLT, Brookfield, WI

5. Receive Truck StockSold to Customer Inventory WIP Schedule for Set-Up Stage Delivery (Corp run or Branch truck) Set-Up (Bay Area)

6. Problem Description Bay has maximum capacity of 25 –5 stations with capacity of 5 trucks each 4 are considered WIP 1 truck is being worked on 5 techs available for scheduling –one tech assigned to each of the 5 bays

7. Problem Description Cont’d 4 set-up types 19-Used Truck Setup –3 types of New Truck Setups 20-N.T SET-UP 36-NEW RTL SET-UP 76-I/C NT SETUP

8. Tech 1 Tech 2 Tech 3 Tech 4 Tech 5 Schematic of the Truck Set-up Bays

9. Scheduling Objective Minimize the time it takes for stock and new customer truck set ups to get from WIP to the delivery area Minimize tardiness to ensure timely delivery of trucks to customers as much as possible

10. Assumptions There will be no preemption during setup, once a job is started, it is completed –If we cannot finish a truck without stopping in the middle of processing, we do not start the truck Once a tech has completed a setup they will start the next set up right after Both stock and customer trucks are taken together as number of stock trucks is negligible 6 hour work day Receive Truck Schedule for Set-Up Truck set for delivery Set-Up (Bay Area)

11. Scope for scheduling 2 areas where scheduling can be done –When picking trucks from the infinite arrival area into one of the 5 WIPs –When selecting one truck for set-up in each bay station

12. Raw Data Analysis Data From: Jan 2005 – May 2006 i.e.; 16 months –Job Type 76 – 515 jobs –Job Type 36 – 43 jobs –Job Type 19 – 97 jobs –Job Type 20 – 449 jobs –Total Significant Jobs: 1104 % of Job Type 76 – 46.65% % of Job Type 43 – 3.89% % of Job Type 19 – 8.79% % of Job Type 20 – 40.67% Total No. of Trucks - 1974 No. of Stock Trucks [IMH RENTAL] - 89 [4.5%] No. of Sold Trucks – 1885 (95.5%)

13. Simulation Input Data –Setup times for each job type Total labor $$ billed on each truck available Tech billed at $97 per hour –Distributions for inter-arrival times, by job type Used ARENA Input Analyzer module to fit each of these into standard probability distributions

14. More Simulation Inputs.. Due dates were assigned as an attribute to every job arriving –Due date distribution also determined from invoice data Results were used to design an ARENA simulation so that different scheduling algorithms could be implemented and their performance evaluated.

15. Scheduling LOGIC Experimented with 4 different hybrid scheduling algorithms. Scheduling Algorithms used are a simple combination of standard algorithms with modifications made to suit this problem. –LST and SPT Process trucks in both WIP and bay queues in order of lowest value of the sum of Slack time and Processing time. In doing so, we anticipate minimizing flowtime as also reducing slack. –LST OR SPT A modification of the above algorithm where incoming trucks are processed as per the least slack time, if (slack >0), else SPT is used to select trucks from the queues.

16. Scheduling Logic –EDD With Priority Modification of the EDD algorithm to incorporate a priority mechanism for new truck types over old truck types Selection rule is – least value of (due date/priority), where priority is “50” for a new truck type and “10” for a used truck type (type 19) –Shortest Processing Time Plain ol’ SPT in least order of processing time

17. Simulation run Parameters Replication Length – 180 Days Hours/day – 6 hrs Warm up period – 10 Days Number of Replications -4

18. Simulation Results [ALL UNITS DAYS] JOB TYPE 20 RULE Combination Avg. FlowtimeMax. FlowtimeAvg. LatenessMax. Lateness LST_SPT5.3319.48517.441832 LST_OR_SPT5.5331.75638.971681.74 EDD_PRIO7.3441.28393.221705.47 SPT7.0959.86422.551507.55 JOB TYPE 19 [USED TRUCK] RULE Combination Avg. FlowtimeMax. FlowtimeAvg. LatenessMax. Lateness LST_SPT2.6516.14406.021104 LST_OR_SPT5.8431.12466.451373.9 EDD_PRIO7.3829.55501.921550.34 SPT4.8756.84618.111527.85

19. Conclusion The combination of LST and SPT works best to minimize overall flow time (but not tardiness). If either LST/SPT is used depending on whether the job is slack or not, this leads to a significant reduction in max. lateness, compared to using LST + SPT in all cases. SPT scheduling yields the least max. lateness. A priority based scheme predictably decreases the avg. lateness for new truck set-ups. Future Work Idea –An Intelligent scheduler that can decide on the right scheduling algorithm based on a reasonable guess of whether incoming jobs will be late or not.

20. Thank you! Questions/Comments?