Intelligent Modeling as long as Decision Making The flow of outbound containers HK h in addition to les more throughput with less l in addition to 39.5 acre 25.1 acre

Intelligent Modeling as long as Decision Making The flow of outbound containers HK h in addition to les more throughput with less l in addition to 39.5 acre 25.1 acre www.phwiki.com

Intelligent Modeling as long as Decision Making The flow of outbound containers HK h in addition to les more throughput with less l in addition to 39.5 acre 25.1 acre

Villarreal, Phil, Movie Critic has reference to this Academic Journal, PHwiki organized this Journal Intelligent Modeling as long as Decision Making Katta G. Murty Industrial in addition to Operations Engineering University of Michigan Ann Arbor, Michigan 48109-2117 USA murty@umich.edu Operations Research (OR) Deals With Making Optimal Decisions Main strategy: Construct math model as long as decision problem List all relevant decision variables, bounds in addition to 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 in addition to implement solution 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 in addition to lack nice structure of models discussed in theory, there is a big gap between theory in addition to practice. The gap between practice in addition to theory in addition to its bridge

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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, in addition to a bus rental company in Seoul “Achieving Elastic Capacity Through Data-intensive Decision Support System (DSS)” Professor Katta G. Murty Industrial in addition to Operations Engineering University of Michigan, Ann Arbor Hong Kong University of Science & Technology Work done at Hong Kong Container Port Hong Kong International Terminals The largest privately owned terminal in the world’s busiest container port Operating under extremely limited space in addition to 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)

The Container Storage Yard Storage yard (SY). Containers in stacks 4 – 6 high. RTGCs (Rubber Tired Gantry Cranes), stack in addition to retrieve containers. SY divided into rectangular blocks. Storage Block RTGC has 7 rows in block between its legs. 6 as long as container storage, 7th as long as truck passing. QCs on Dock QCs unload containers, place them on ITs. ITs take them to SY as long as storage until consignee picks them. ITs bring export containers from SY to QCs to load into vessel.

The flow of outbound containers SY=Storage Yard Underneath each location or operation, we list the equipment that h in addition to les the containers there Arrival, Storage in addition to Retrieval of Import Containers Flow of inbound containers Top View of a Block B1 Being Served by an RTGC

L in addition to Scarcity as long as Terminal Development in Hong Kong The Highest L in addition to Utilization Terminal in the World HK h in addition to les more throughput with less l in addition to 39.5 acre 25.1 acre 72.0 acre 2.3m TEU 6.4m TEU 1.2m TEU Key Service Quality Metrics Vessel Turnaround Time Quay Crane Rate Reshuffle rate

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 Decision Problem Solved D1: Route trucks in addition to allocate storage spaces to arriving containers, to minimize congestion in addition to reshuffling Gate Container Yard Berth HIT HIT Decision Problem Solved D2: Optimize trucks allocation/quay crane to minimize quay crane, truck waiting time, number of trucks used, in addition to number of trucks in yard

Decision Problem Solved D3: Develop procedure to estimate truck requirement profile in addition to optimum truck driver hiring scheme No. of Trucks Required Hour Decision Problem Solved D4: Optimize yard crane deployment to blocks to minimize crane time spent on the terminal road network Decision Problem Solved /Under Study D5: Allocate appointment times to external trucks to minimize turnaround time, in addition to their number in yard during peak time in addition to level workload

Expected Number of Containers in Planning Period at Each Node, to Go to Various Destination Nodes Gate Complex Container Yard Berth Data: 400 Export Containers to go as long as Storage Data on Blocks B1: 40 Export Containers to Berth 1 10 Export Containers to Berth 4 20 Import Containers to Gate Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Data on Berths Berth 1: 180 Import Containers to go as long as Storage Berth 1 Berth 2 Export Import Export Import D1: Data as long as flow model to route trucks Decision Variables in Multi-Commodity Flow Model as long as 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)}

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Variation in Workload Over Time

Three Separate Policies Equalize fill ratios in blocks Truck dispatching policy Storage space assignment in a block Numerical Example as long as 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 LP Model to Determine Container Quota Numbers as long as Blocks . Linear Programming as long as mulation is: Subject to

Business Benefits to HPH in addition to Customers Financial Benefits Summary Total Annual Saving US$219 million 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 as long as operations in a container terminal”, to appear in Decision Support Systems, 2005; available online at www.sciencedirect.com 3. Katta G. Murty, Woo-Je Kim, “Intelligent DMSS as long as Chartered Bus Allocation in Seoul, South Korea”, November 2004.

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