Examples

Complete working examples demonstrating OptimShortestPaths capabilities.

Running Examples

All examples are located in the examples/ directory. Each has its own Project.toml for isolated dependencies.

To run an example:

cd examples/drug_target_network
julia --project=. -e "using Pkg; Pkg.develop(path=\"../..\"); Pkg.instantiate()"
julia --project=. drug_target_network.jl

Available Examples

1. Comprehensive Demo

Location: examples/comprehensive_demo/

Complete framework demonstration including:

Problem transformation philosophy
All three MCDA methods (weighted sum, ε-constraint, lexicographic)
Supply chain optimization
Performance benchmarking
Algorithm capabilities showcase

Generates: 7 publication-quality figures

Run:

cd examples/comprehensive_demo
julia --project=. comprehensive_demo.jl
julia --project=. generate_figures.jl  # Generate visualizations

2. Drug-Target Network

Location: examples/drug_target_network/

Analyzes drug-target binding affinities and selectivity:

COX1/COX2 selectivity analysis
Multi-objective cost-affinity-specificity optimization
Drug connectivity metrics
Binding affinity heatmaps

Key insights: Demonstrates how thermodynamic binding affinities map to graph distances.

3. Metabolic Pathway

Location: examples/metabolic_pathway/

Glycolysis pathway optimization (Embden-Meyerhof-Parnas):

ATP yield calculations
Byproduct analysis
Multi-objective pathway comparison
Pareto front visualization

Key insights: Shows bipartite metabolite-reaction network transformation.

4. Treatment Protocol

Location: examples/treatment_protocol/

Cancer treatment pathway optimization:

Multi-objective cost-time-quality-success trade-offs
Patient-specific protocol recommendations
Clinical decision tree analysis
Treatment sequence optimization

Key insights: Handles complex multi-criteria clinical decisions.

5. Supply Chain

Location: examples/supply_chain/

Multi-echelon logistics network:

3 factories → 4 warehouses → 5 distribution centers → 10 customers
22-node network optimization
Flow analysis and cost minimization
Network topology visualization

Key insights: Large-scale real-world graph optimization.

6. Generic Utilities Demo

Location: examples/generic_utilities_demo.jl

Demonstrates domain-agnostic utility functions:

find_shortest_path
calculate_distance_ratio
analyze_connectivity
find_reachable_vertices

Key insights: Shows how generic functions work on any graph.

Code Examples

Basic Shortest Path

using OptimShortestPaths

# Create graph
edges = [Edge(1, 2, 1), Edge(2, 3, 2), Edge(1, 3, 3)]
weights = [1.0, 2.0, 4.0]
graph = DMYGraph(3, edges, weights)

# Find shortest path
distance, path = find_shortest_path(graph, 1, 3)
# distance = 3.0, path = [1, 2, 3]

Multi-Objective Example

using OptimShortestPaths
using OptimShortestPaths.MultiObjective

# Create multi-objective graph
edges = [
    MultiObjectiveEdge(1, 2, [1.0, 10.0], 1),  # Cheap but slow
    MultiObjectiveEdge(2, 3, [2.0, 5.0], 2),   # Moderate
    MultiObjectiveEdge(1, 3, [5.0, 3.0], 3)    # Expensive but fast
]

# Build adjacency list
adjacency = [Int[] for _ in 1:3]
for (idx, edge) in enumerate(edges)
    push!(adjacency[edge.source], idx)
end

graph = MultiObjectiveGraph(
    3,                      # n_vertices
    edges,                  # edges
    2,                      # n_objectives
    adjacency,              # adjacency list
    ["Cost", "Time"]        # objective names
)

# Compute Pareto front
solutions = compute_pareto_front(graph, 1, 3)

# Find best trade-off
best = get_knee_point(solutions)
println("Best trade-off - Cost: $(best.objectives[1]), Time: $(best.objectives[2])")

Domain-Specific Example

using OptimShortestPaths

# Drug-target network with binding affinities
drugs = ["Aspirin", "Ibuprofen", "Celecoxib"]
targets = ["COX1", "COX2"]

# Binding affinity matrix (0-1 scale, higher = stronger binding)
affinity_matrix = [
    0.85  0.45;  # Aspirin
    0.30  0.90;  # Ibuprofen
    0.05  0.95   # Celecoxib (COX-2 selective)
]

network = create_drug_target_network(drugs, targets, affinity_matrix)

# Find path and calculate selectivity
distance, path = find_drug_target_paths(network, "Celecoxib", "COX2")
selectivity = calculate_distance_ratio(
    network.graph,
    network.drug_indices["Celecoxib"],
    network.target_indices["COX1"],
    network.target_indices["COX2"]
)
println("Celecoxib COX-2 selectivity: $(round(selectivity, digits=1))x")

Visualization Examples

Each example directory includes generate_figures.jl for creating publication-quality visualizations:

cd examples/comprehensive_demo
julia --project=. generate_figures.jl
# Generates 7 figures in figures/ directory

Figures use professional aesthetics:

300 DPI resolution
Bookman serif font
Nature/Science journal color palette
Publication-ready quality