Introduction to optimization models for decision-making. Methods and
models for the management of inventories; methods for production
planning; facility location; transport and logistics;
automatic classification
1. Knowledge of linear and mixed integer programming models and their applications in management
2. Knowledge of optimal network flow models
3. Knowledge of methods and models for invntory management
4. Knowledge of production planning methods
5. Knowledge of some machine learning tools
Prerequisites
Elementary notions of linear algebra
Teaching Methods
Front lectures
Type of Assessment
Oral exam (or an equivalent written examination) and a small practical applied project in machine learning
The exam consist in theoretical questions and numrical exercises prepared to verify knowledge of:
- optimization models (linear, integer network flow) and their applications
- methods and algorithms for inventory and production
- being able to solve productionn planning problems
- being able to use machine learning tools
Course program
1. Linear optimization models: linear models,
network flows, integer linear programming models, modeling techniques,
logical constraints. Outline of solution methods for linear problems
2. Elementary models for inventory management:
economic production lot, instantaneous and continuous supply,
large quantities discounts, finite horizon models.
Stochastic models for inventory management - single period
(newsvendor)
Introduction to the management of supply contracts
Wholesale supply, Buy Back contracts.
3. Models and methods for production planning
Model with deterministic and variable demand;
Wagner-Within algorithm, Zangwill algorithm for backorders;
4. Advanced models for production planning; alternative formulations, cutting planes methods
5. Methods and models for the automatic classification
Introduction machine learning techniques. Robust classification
robust through support vector machines (SVM)