Investment Strategy Optimization - Group 7

Overview

This project uses advanced optimization algorithms to tackle two key problems:

1. Investment Strategy Optimization:

   • Objective: Maximize the Sharpe ratio for investments in three companies.
   • Method: Implemented the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to iteratively adjust investment allocations and maximize risk-adjusted returns.

2. Queueing Model Optimization:

   • Objective: Minimize the average waiting time in a GI/GI/1 queueing system.
   • Method: Utilized a Stochastic Approximation (SA) algorithm to optimize service times, ensuring efficient customer service and reduced waiting times.

Group Members

• Jiakang Huang
• Hongkai Liu
• Shengzhe Ji
• Jiahe Jiang
• Chenghao Cao

Supervisor

• Dr. Bernd Heidergott

Teaching Assistants

• Franssen Christian
• Zehao Li

Key Methods and Analysis

• Simultaneous Perturbation Stochastic Approximation (SPSA): Efficiently optimized complex, noisy functions by estimating gradients with minimal computational cost.
• Stochastic Approximation (SA): Improved queueing performance through iterative parameter adjustments based on stochastic models.
• Simulation and Statistical Analysis: Conducted extensive simulations to validate algorithm performance, analyze convergence, and ensure robust results.

How to Run

1. Clone the repository.
2. Open the Jupyter Notebook.
3. Run the code cells to see the simulation results.

Repository Link

GitHub Repository