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Simulation - Coggle Diagram
Simulation
Advantages
Exploration of New Policies
Hypothesis Testing without commitment
Time Manipulation
Interaction Insights
Variable Importance
Bottleneck Identification
Experimenting possible scenarios which is useful in the design
Inappropriate Use
Solvable by logic or analytical tools
Limited Resource
Inaccurate Data
Low-Cost & Efficient Alternatives available
Cost of simulation outweighs savings/results
Insufficient/unrealiable data
Areas of Application
Logistics, Transportation, and Distribution
Aviation Logistics: Analysis of Passenger Flows in an Airport Terminal
Metrics of Success
Resource Utilization Rates
15% increase in utilization rates of check-in counters and baggage claims
Congestion & Terminal Occupancy
Optimized number of passengers processed per minute through terminal zones
Better identify critical bottlenecks and their causes
Operational Efficiency
Reduced passenger waiting times, queue lengths and total transit times by 15 minutes on average.
Challenges Faced during Implementation
Collection of data
Process interdependencies
Dynamic Variables
Difficulty in Modelling Human Behavior
Maritime Logistics: The Impact of Order Release Times
Metrics of success
Operational Efficiency
Reduced total processing time per order by 9.5%
Cut intermodal agency processing time by 35.6%
Reduced rail transport company processing time by 34.8%
Resource Utilisation
Reduced intermodal operator processing time by 6.2%
Optimized order release timing to 3 days before departure for best efficiency.
Improved deep sea carrier processing time by 5.0%
Process Reliability
Lowered order change rate by 10%
Prevented 5x cost increase by avoiding too-late (2-day) order release
Challenges Faced during Implementation
Complex Maritime Transport Chain
Data Availability
Model Validation & Dynamic Nature of System
Warehousing: Enhancing Efficiency in Robotic Warehousing
Metrics of Success
Throughput Efficiency
Throughput increased by 4% by batching similar orders
Number of Pods Required
Average pod required decreased by 40%
Pile-on Value
Pile-on value maximized by 60%
Challenges Faced during Implementation
Accurate RMFS system modeling
High-quality data requirements
Optimizing AGV navigation
Maritime Logistic: Optimising Warehouse for Ship Yard Operation
Challenges Faced during Implementation
Need for Simulation Accuracy
Addressing time-series dependencies in real-world operations.
Need for Real Time Data Intergration
Data variability, latency, and the risk of errors in the system.
Scalability Issues
More computing resources for multiple ship yard
Metric of Success
Resource Optimisation Metric
Improved ship dock utilisation by 90%
Cost Saving Metric
Avoided expansion costs
Model Reliability
95% simulation validation accuracy
Operational Efficiency
Shipping congestion reduction by 80%
Traffic Management:Toll Plaza Using A combination of Queuing and Simulation
Metrics of Success
Queue Lengths
Queue lengths reduced to 4.5 meters for electronic payments from 8.1 meters
Workload Balancing
ETC lane workload at 33%, cash lane at 31%, achieving optimal usage
Waiting Time
Electronic payments reduced to 2 seconds from 4 seconds with separate lanes
Capacity Validation
Successfully managed 12,000 vehicles/day and 543 vehicles/hour during peak times
Challenges Faced during Implementation
Complex Queuing and Traffic Flow
Data Availability
Computational Complexity
Manufacturing
Semiconductor Manufacturing
Construction Engineering
Military
Appropriate Use
Analytical Purposes
Observe output interactions and identify key variables
Verify analytic solutions
Experimentation
Test new designs or policies before implementation
Test machine capabilities
Training and Visualization
No disruption to daily operation
Ability to address complex systems
Disadvantages
Difficult Interpretation of Results
Time-Consuming and Expensive
Model Building Requires Special Training
Inefficiency Compared to Analytical Solutions