Thesis Project

A Joint Optimization Model for Product Selection, Shelf Display, and Replenishment for Smart Vending Machine

• Pioneered a mixed integer non-linear programming model to optimize product selection, shelf display, and replenishment strategies for Smart Vending Machines (SVM), addressing the challenge of dynamic demand fluctuations.
• Enhanced the model by integrating stock-out cost considerations, a crucial factor in replenishment planning, to ensure a balanced approach between inventory holding costs and service level.
• Applied a Genetic Algorithm for solving the complex optimization problem, proving its effectiveness through extensive comparison with diverse datasets. This approach highlighted the algorithm's capability to achieve high profit margins while minimizing stock-outs.
• The study's findings contribute valuable insights into the operational excellence of SVMs, offering a robust framework for maximizing profit through informed product management and inventory strategies.