Solving Resource Allocation with Deterministic and Stochastic Optimization

Projects displaying advanced optimization techniques in finance and operations

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This repository highlights projects using deterministic models (linear, integer, and mixed programming) and stochastic/metaheuristic methods (simulated annealing, genetic algorithms, particle swarm optimization).

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0.1 Particle Swarm Optimization on Schwefel Benchmark Function

Problem Instructions: (View problem context)

Simulates swarm intelligence to explore a complex search space efficiently.

Particle Swarm Optimization on GitHub

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1 Heuristic & Metaheuristic Approaches for Neighborhood & Population Heuristics

Problem Instructions: (View problem context)

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1.1 Simulated Annealing

Uses iterative improvement with probabilistic acceptance of worse solutions.

Simulated Annealing on GitHub

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1.2 Genetic Algorithm

Applies crossover and mutation to evolve stronger solutions over generations.

Genetic Algorithm on GitHub

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2 Deterministic Approaches

2.1 Linear Optimization

Applies linear programming to optimize resource allocation under constraints.

Linear Optimization Example on GitHub
(View problem instructions)

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2.2 Generalized Network Flows: Modeling Decay

Extends flow optimization to capture diminishing effects over a network.

Generalized Network Flows: Modeling Decay on GitHub

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2.3 Mixed-Binary-Linear Optimization

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Combines integer and binary decision variables for complex allocation problems.

Mixed-Binary-Linear Optimization on GitHub