86478 - Production Management and Optimisation

Learning outcomes

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

Course contents

• Introduction and Background: The evolution of Manufacturing systems from the Industrial Revolution to Industry 4.0 (and days ahead).
  
• Production System Design and Planning: Decisional framework based on Problem-Entity-Methods-Decisions-Performance; Classification of Manufcaturing Problems and related Hierarchy: Resource Requirement Planning and Optimization problems, Materials Requirement Planning, Demand-driven Planning Methods (Push vs Pull approach), Stock-driven Planning Methods.
• Production Management and Lot Sizing: Management system for stock and inventory planning; Economic order quantity and analytical models; Kan-Ban systems and Lean manufacturing for stock reduction; Lot-sizing Problems with deterministic and stochastic demand; News Vendor Problem and applications.
• Production Optimization: Optimization techniques for manufacturing systems and resources.
• Production Scheduling: Uncapaciteated and Capacitated Scheduling methods for Production Systems; Theoretical and Analytical models for industrial applications and different production layout configurations.
  
• Design and Development of Decision Support Systems for Manufacturing Problems: Collection and management of Manufacturing data and records; Fundamentals of Object-Oriented Programming (Visual Basic); Excel Solver; Languages for Mathematical Programming and Optimization (AMPL).

Readings/Bibliography

Dedicated bibliography covering the entire class programme (Slides, References, Training Code, Excel Files) will be uploaded step-by-step at the UniBo IOL Platform (iol.unibo.it). Specific info will be provided at the end of each lesson. Additional readings are available upon request (but they are not mandatory to pass this class).

Teaching methods

In presence.

The course is organized in frontal lectures in which the main topic are discussed and followed by numerical and practical applications. At the end of each topic, a training exercise is proposed and solved in class. Such approach underlines the applied nature of this discipline and aims at teaching the solving methodology beyond concrete problems from the industrial practice.

Lectures may include team working and case studies discussion to promote active partecipation and interactions among the attendants.

Assessment methods

The final exam lies on discussing a team-project based on developed Decision-support systems for manufacturing problems. Selfmade teams are made of 2 students. The project bases on an application written in Visual Basic and AMPL languages through the MS Excel Interface, and includes a paper document describing the rationale of the project, the model implemented, the input dataset, and the obtained results.

Part of the final score results from the partecipation of attendants during class. 

Previous computer science skills are not necessary, whilst proactivity and curiosity are broadly encouraged.

Office hours

See the website of Riccardo Accorsi