B2666 - Production Management and Optimization M

Learning outcomes

Students learn the general criteria and methods aimed at managing and optimizing production systems and operations, and improve their skills in developing decision-support systems (DSS) and tools for Industrial and manufacturing environments.

Course contents

• Introduction and Background: Manufacturing systems entities, processes and decisions.

Approach and Methodology

• Production Models Engineering: How to tackle a manufacturing decisional framework based on Entity-Knowledge-Decisions-Performance process; Classification of Manufacturing Problems and related Hierarchy.
• Manufacturing DSS Design and Development: Collection and management of Manufacturing and Demand data and records; Linear Programming (LP) Solver; Languages for Mathematical Programming and Optimization (AMPL).
• Environmental Sustainability: decision-making and manufacturing problems incorporate environmental constraints and targets.

Topics and Applications

• Production & Inventory Planning: Resource Requirement Planning and Optimization problems; Make-or-Buy optimisation models; Optimized Resource Requirement planning (O-RRP); Optimal Inventory Management; Optimized Master Production Schedule (O-MPS); Optimized Material Requirement Planning (O-MRP); Lot-sizing Problems; Integrated production & Inventory Management Optimization Problems (Multiple-Items, Varying Demand, Backlogs).
  
• Production Scheduling: Optimization techniques for manufacturing systems and resources; Uncapacitated and Capacitated Scheduling methods for Production Systems; Setup time cycle optimisation models. Optimal scheduling problemsfor different layout configurations.
  

Readings/Bibliography

Lecture notes, prepared by the instructor using a tablet, will be made available in PDF format after each lecture. Bibliographic materials and study resources—including slides, references, training code, and Excel files—will be provided throughout the course through the UniBo Virtuale platform (virtuale.unibo.it ). Additional texts for further study are available upon request and are intended as supplementary and in-depth materials.

Teaching methods

In-person lectures. Attendance is not compulsory but is recommended.

Programming exercises using the AMPL language will be carried out in class to facilitate the learning of the Observation–Modeling–Programming–Decision process used to address production problems in industrial manufacturing contexts.

Learning activities will also include an assigned Team Project, with group project-supervision sessions held in the classroom.

Assessment methods

The final examination consists of three parts:

a. Individual theoretical multiple-choice test on the production optimization models covered in class.

b. Group project involving two students. The project requires the design and development of a Decision Support System (DSS) application supporting multiple decision problems. The application must be implemented in AMPL and supplied with a dataset organized and structured by the group using MS Excel or MS Access, starting from an unstructured problem instance provided by the instructor. In addition to submitting the project files, students must prepare a concise report discussing the results obtained.

c. Individual in-class programming exercise using AMPL, involving the solution of a production problem based on a ready-to-use dataset. During the exercise, students may bring a printed copy of their typed notes from the course lectures.

Teaching tools

No prior computing knowledge is required. Previous coursework in the fundamentals of Operations Research is recommended but not compulsory. Proactivity and curiosity about the subject are strongly encouraged.

Although these topics will be briefly introduced during the lectures, basic knowledge of Production Management, requirements-based and inventory-based management methods and models, JIT and Kanban systems, push versus pull production, Production Logistics, and production-system terminology—such as BOM, task, manufacturing routing, resource, and related concepts—is considered a prerequisite.

The use of Generative AI is permitted for project code debugging, improving theoretical preparation, and self-training in coding. However, using Generative AI for preparing the project materials, including both AMPL files and the report, is prohibited and will result in penalties during the assessment.

Office hours

See the website of Riccardo Accorsi