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Systems Modelling & Simulation – Labwork 1, Case Studies, Fundamentals…
Systems Modelling & Simulation – Labwork 1
Simulation
Definition
Imitation of real-world system over time
Artificial history for performance evaluation
Purpose of Simulation
Understand complex system interactions
Evaluate what-if scenarios
Support decision-making
Predict system performance
When Simulation is Appropriate
System too complex for analytical solutions
High uncertainty and variability
Risky or costly real-world experimentation
Human behavior involved
When Simulation is Not Appropriate
Simple problems solvable analytically
Insufficient or unreliable data
Time or budget exceeds benefits
Advantages
No disruption to real system
Time compression or expansion
Bottleneck identification
Supports system design and optimization
Disadvantages
Time-consuming model development
Requires specialized expertise
Results may be misinterpreted
Costly if poorly scoped
Simulation Types
Discrete Event Simulation (DES)
Agent-Based Modeling (ABM)
System Dynamics (SD)
Hybrid Simulation
Case Study 1 – Drone Charging Stations (Last-Mile Delivery)
Industry Problem
Limited drone battery capacity
Restricted delivery range
Uncertain order demand and payload
Why Simulation
Complex interaction of drones, batteries, and demand
Real-world testing is costly and unsafe
Operations Modeled
Drone flights
Battery discharge and charging cycles
Charging station placement
Drone fleet sizing
Performance Measures
Total system cost
Energy consumption
Delivery coverage
Service reliability
Simulation Approach
Agent-Based Modeling
System Dynamics
Simulation–Optimization
Challenges
Accurate energy consumption modeling
Integration of optimization and simulation
Achievements / Benefits
Optimal charging station locations
Reduced operational and energy costs
Improved last-mile delivery efficiency
Case Study 2 – Mixed-Fleet Warehouse Routing
Industry Problem
Warehouse congestion
Human and AGV coordination issues
Unpredictable human behavior
Why Simulation
Human–machine interaction is stochastic
Traffic conflicts cannot be solved analytically
Operations Modeled
Warehouse routing
Vehicle interactions
Traffic congestion
Performance Measures
Order execution time
Travel time delay
Warehouse throughput
Simulation Approach
Discrete Event Simulation
Agent-Based Modeling
Challenges
Modeling erratic human behavior
Coordinating mixed vehicle fleets
Achievements / Benefits
Improved routing strategies
Reduced congestion
Increased warehouse productivity
Case Study 3 – Subway Transfer Station Passenger Flow
Industry Problem
Severe congestion during peak hours
Inefficient passenger flow
Safety risks
Why Simulation
High passenger volume and dynamic crowd behavior
Real-world experimentation not feasible
Operations Modeled
Passenger arrivals
Walking behavior
Line transfers
Platform congestion
Performance Measures
Passenger density
Average walking speed
Waiting time
Passenger throughput
Simulation Approach
Discrete Event Simulation
Agent-Based Modeling
Real-world data integration
Challenges
Realistic crowd behavior modeling
Peak vs off-peak demand variation
Achievements / Benefits
Identification of congestion hotspots
Improved station layout and operations
Enhanced commuter experience
Case Study 4 – Disaster Relief Inventory Simulation
Industry Problem
Uncertain demand
Perishable supplies
Budget and funding constraints
Why Simulation
Highly dynamic and uncertain environment
Ethical and practical constraints on testing
Operations Modeled
Inventory replenishment
Camp demand (internal and external)
Population migration
Budget uncertainty
Performance Measures
Shortage cost
Deprivation cost
Inventory holding cost
Service level
Simulation Approach
Discrete Event Simulation
Open-source simulation engine
Challenges
Modeling perishability
Multi-class demand management
Funding timing uncertainty
Achievements / Benefits
Improved humanitarian inventory policies
Reduced waste and shortages
Enhanced disaster response effectiveness
Case Study 5 – PortalLite Distributed Ports Network
Industry Problem
Port congestion
High emissions
Limited accessibility in remote regions
Why Simulation
Uncertain maritime logistics operations
Complex intermodal transport interactions
Operations Modeled
Port operations
Ship and truck movements
Container order flows
Performance Measures
Total logistics cost
Carbon emissions
Return on investment
Delivery efficiency
Simulation Approach
Agent-Based Simulation
Mathematical Optimization
Challenges
High initial infrastructure cost
Coordination of multiple transport modes
Achievements / Benefits
Reduced urban congestion
Lower environmental impact
Improved accessibility
Long-term economic viability
Overall Value of Simulation to Business
Supports informed decision-making
Reduces operational risk and cost
Improves efficiency and service quality
Enables sustainable system design
Handles uncertainty and complexity
Provides competitive advantage
Case Studies
Fundamentals of Simulation Theory