A Hybrid PSO-SA Algortihm for Multi-Product Perishable Inventory Replenishment

Описание: Heuristic Optimization Final Project – A Hybrid PSO-SA Algorithm for Multi-Product Perishable Inventory Replenishment Under Buget and Storage Constraint

This video presents our final project for the Heuristic Optimization course. Our study focuses on solving a single-period multi-product perishable inventory optimization problem by minimizing the total inventory cost while satisfying budget and storage capacity constraints.

The project compares several optimization approaches, including:

1. Economic Order Quantity (EOQ)
2. Random Search
3. Genetic Algorithm (GA)
4. Simulated Annealing (SA)
5. Particle Swarm Optimization (PSO)
6. Hybrid Particle Swarm Optimization – Simulated Annealing (PSO-SA)

The optimization model considers four cost components:

Purchase Cost
Holding Cost
Shortage Cost
Spoilage Cost

The computational experiments evaluate algorithm performance under multiple operational scenarios, including Base, Tight Budget, Tight Storage, and Relaxed conditions. Performance is assessed using total cost, computational runtime, convergence behavior, statistical analysis, and inventory replenishment policies.

University: Yuan Ze University
Course: Heuristic Optimization
Project Title: A Hybrid PSO-SA Algorithm for Multi-Product Perishable Inventory Replenishment Under Buget and Storage Constraint

Group Members:
Jeremy Brian Pratama / 1145457
Vanna / 1145448
Htar / 1145444