1. Intelligent Modeling for Decision Making
Katta G. Murty
Industrial and Operations Engineering
University of Michigan
Ann Arbor, Michigan USA

2. Operations Research (OR) Deals With Making Optimal Decisions
Main strategy: Construct math model for decision problem
List all relevant decision variables, bounds and constraints on them (from the way the system operates), objective function(s) to optimize
Solve model using efficient algorithm to find optimal solutions
Make necessary changes and implement solution

3. The gap between practice and theory and its bridge
Math Modeling
OR theory developed efficient algorithms to solve several single objective decision models
But practitioners find no model in OR theory fits their problem well
Real world problems usually multi-objective and lack nice structure of models discussed in theory, there is a big gap between theory and practice.
The gap between practice and theory and its bridge

4. Math Modeling (continued)
To get good results, essential to model intelligently using heuristic modifications, approximations, relaxations, hierarchical decomposition
Will illustrate this using work done at Hong Kong Container Port, and a bus rental company in Seoul

5. “Achieving Elastic Capacity Through Data-intensive Decision Support System (DSS)” Professor Katta G. Murty Industrial and Operations Engineering University of Michigan, Ann Arbor Hong Kong University of Science & Technology Work done at Hong Kong Container Port

6. Hong Kong International Terminals
The largest privately owned terminal in the world’s busiest container port
Operating under extremely limited space and the highest yard density yet achieving one of highest productivity amongst ports
Key Facilities
Quay Crane: 41
Yard Crane: 116
Internal Trucks: > 400
Yard Stacking Capacity: > 80,000 boxes (= 111 football stadiums)

7. The Container Storage Yard
Storage yard (SY). Containers in stacks high. RTGCs (Rubber Tired Gantry Cranes), stack and retrieve containers. SY divided into rectangular blocks.

8. Storage Block
RTGC has 7 rows in block between its legs. 6 for container storage, 7th for truck passing.

9. QCs on Dock
QCs unload containers, place them on ITs. ITs take them to SY for storage until consignee picks them. ITs bring export containers from SY to QCs to load into vessel.

10. The flow of outbound containers
SY=Storage Yard
Underneath each location or operation, we list the equipment that handles the containers there

11. Arrival, Storage and Retrieval of Import Containers
Flow of inbound containers

12. Top View of a Block B1 Being Served by an RTGC

13. Land Scarcity for Terminal Development in Hong Kong

14. The Highest Land Utilization Terminal in the World
CTB
Hamburg
HIT
Hong Kong
Pier T
Long Beach
Land Area / Number of Berth
Throughput (2003)
39.5 acre
25.1 acre
72.0 acre
2.3m TEU
6.4m TEU
1.2m TEU
HK handles more throughput with less land

15. Key Service Quality Metrics
Truck
Turnaround
Time
Quay
Crane
Rate
HIT
Reshuffle
rate
Vessel
Turnaround
Time

16. Objectives of the Study
Minimize congestion on terminal road system
Reduce internal truck cycle time
Increase yard crane productivity
Minimize reshuffling
Improve quay crane rate
Enhance vessel operating rate

17. Decision Problem Solved
D1: Route trucks and allocate storage spaces to arriving containers, to minimize congestion and reshuffling
HIT
HIT
HIT
HIT
HIT
HIT
Gate
Container Yard
Berth

18. Decision Problem Solved
D2: Optimize trucks allocation/quay crane to minimize quay crane, truck waiting time, number of trucks used, and number of trucks in yard
HIT
HIT

19. Decision Problem Solved
D3: Develop procedure to estimate truck requirement profile and optimum truck driver hiring scheme
No. of Trucks Required
Hour

20. Decision Problem Solved
D4: Optimize yard crane deployment to blocks to minimize crane time spent on the terminal road network

21. Decision Problem Solved /Under Study
D5: Allocate appointment times to external trucks to minimize turnaround time, and their number in yard during peak time and level workload

23. D1: Data for flow model to route trucks
Expected Number of Containers in Planning Period at Each Node, to Go to Various Destination Nodes
D1: Data for flow model to route trucks
HIT
HIT
HIT
Export
Export
Block 1
Block 2
Berth 1
HIT
HIT
Import
Import
HIT
Block 3
Block 4
HIT
HIT
Block 5
Block 6
Berth 2
Gate
Complex
Container Yard
Berth
Data on Blocks
B1: 40 Export Containers to Berth 1
10 Export Containers to Berth 4
20 Import Containers to Gate .
Data: 400 Export Containers
to go for Storage .
Data on Berths
Berth 1: 180 Import Containers to go for Storage .

24. Decision Variables in Multi-Commodity Flow Model for Routing Trucks
fij = total no. container turns flowing on arc (i, j) in planning period
 = max {fij: over all arcs (i, j)}
 = min {fij: over all arcs (i, j)}

25. Variation in Workload Over Time

28. Three Separate Policies
Equalize fill ratios in blocks
Truck dispatching policy
Storage space assignment in a block

29. Numerical Example for Fill Ratio Equalization
9 blocks, each with 600 spaces
ai = No. Containers in Block i, at period end if no new containers sent there
xi = Decision Variables, no. new containers sent to Block i during the period

30. LP Model to Determine Container Quota Numbers for Blocks.
Linear Programming formulation is:
Subject to

31. Numerical Example
Average stored containers/block = ( )/9 = 400 i ai xi
No. Remaining
Total
2570
1040
100 7
300
740
120 3
280
460
150 2
250
210
300 6
100
110
325 8 75 35
350 5 50
375 4 25
400 1
425 9
---

32. Innovations in Work on D1
First paper to study congestion inside container terminals
Controlling congestion by equalization fill ratios and truck dispatching
LP model for fill ratio equalization, its combinatorial solution
First paper to relate container stacking to bin packing
Hardware Developed: for real time monitoring and communication
OR Techniques: LP, IP, Combinatorial Optimization
Decision Frequency: Container quota numbers for 95 blocks each four hours; take few seconds

33. D2: Result from a Simulation Run
n = number Trucks/Quay Crane
h = number Containers to process in hatch = 30

34. Innovations in Work on D2
Recognize importance of reducing number of trucks to reduce congestion
Internal trucks pooling system, adopted worldwide
OR Techniques: Estimation, Queuing theory, simulation
Decision Frequency: One-time decision

35. D3: Truck Requirement Profile
h = number of containers unloaded, loaded in a hatch
(h) = average time minutes = h
(h) = standard deviation = h
Time allotted = (h) + (h)

36. Benefits from Work on D3
Estimate hourly truck requirements for planning
OR Techniques: Estimation, simulation, linear regression
Decision frequency: Daily; takes few minutes

37. D4: Crane Movement Between Blocks
Crane minutes to move
From Block
To block B6 B7 B8 B9 B1 20 25 35 30 B2 10 15 B3 B4 B5
Solved as transportation model, about once per two hours, typically size < 15x 15, takes few seconds

38. D5: Appointment Times for External Trucks to Pickup During Peak Hours
Optimal quota number for external trucks to pick up in each 30 minute interval determined by simulation
Appointment time booking system is automated telephone-based system

39. Benefits from Work on D5 Quota for half hour determined by simulation
Innovation: First terminal to introduce “booking” to reduce number of external trucks in peak hours & their turnaround time
Hardware Developed: Automated telephone-based booking system
OR Techniques Used: Estimating probability distributions, queuing theory, and simulation
Decision Frequency: One-time decision

40. Summary of Techniques Used
Problem
Techniques
Size
Frequency
Comp. Time D1
Route trucks, allocate storage
LP, combinatorial optimization, integer programming
Quota for 95 blocks
Every 4 hours
Few seconds
Truck dispatch
Heuristic rule
Each truck
Real time D2
Truck/Crane allocation
Queuing, estimation and simulation -
One-time D3
Procedure to estimate truck requirements
Estimation, simulation and linear regression
Estimate truck requirement profile
Planning
15 vessel schedules
Once a day
Few minutes D4
Crane movement
Estimation and network flows
<= 15 x 15
Once about 2 hours D5
Booking system
Estimation, queuing and simulation

41. Improvement in Key Quality Service Metrics
Internal Truck Turnaround Time
↓16%
HIT
External Truck Turnaround Time ↓30%
Quay Crane Rate ↑45%
Vessel Turnaround Time ↓30%
Vessel Operating Rate ↑47%

42. More Benefits Customers Staff Social
“Catch Up Port” in Asia
Shipping lines’ savings amount to US$65 million per year
Enhance overall customer satisfaction and loyalty
Reduce workload with increased productivity
Boost to staff morale
Avoid the construction of new berths which results in less pollution and adverse effects to the society

43. Business Benefits to HPH and Customers
Financial Benefits Summary
Savings
Key Improvement Areas
US$54 million
Improvement of internal tractor utilization
US$100 million
Handling cost reduction
Avoidance of building new facilities
US$65 million
Vessel turnaround time improvement
Total Annual Saving US$219 million

44. References
Katta G. Murty, Yat-Wah Wan, Jiyin Liu, Mitchell M. Tseng, Edmond Leung, Kam-Keung Lai, Herman W. C. Chiu, ``Hong Kong International Terminals Gains Elastic Capacity Using a Data-Intensive Decision Support System'', 2004 Edelman Contest Finalist Paper, to appear in Interfaces, January-February 2005.
2. Katta G. Murty, Jiyin Liu, Yat-Wah Wan, Richard Linn, ``A decision support system for operations in a container terminal'', to appear in Decision Support Systems, 2005; available online at
3. Katta G. Murty, Woo-Je Kim, ``Intelligent DMSS for Chartered Bus Allocation in Seoul, South Korea'', November 2004.