86478 - Production Management and Optimisation

Academic Year 2022/2023

  • Teaching Mode: Traditional lectures
  • Campus: Bologna
  • Corso: Second cycle degree programme (LM) in Advanced Automotive Engineering (cod. 9239)

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 systems (DSS) and tools for Industrial and manufacturing environments.

Course contents

  • Introduction and Background: The evolution of Manufacturing systems from the Industrial Revolution to Industry 4.0 (and days ahead).

Approach and Methodology

  • Production Management Engineering: Decisional framework based on Problem-Entity-Methods-Decisions-Performance; Classification of Manufacturing Problems and related Hierarchy.
  • DSS Design and Development: Collection and management of Manufacturing and Demand data and records; Fundamentals of Object-Oriented Programming (Visual Basic); Excel Solver; Languages for Mathematical Programming and Optimization (AMPL).

Topics and Applications

  • Demand Evaluation: Demand analysis, Frequency Analysis and Demand Probability Density Function building. 
  • Production Management: Resource Requirement Planning and Optimization problems; Make-or-Buy optimisation models; Requirement planning; Inventory Management with deterministic and Stochastic Demand; Lot-sizing Problem and News Vendor Problems.
  • Production Optimization: Optimization techniques for manufacturing systems and resources; Uncapacitated and Capacitated Scheduling methods for Production Systems; Setup time cycle optimisation models.

Readings/Bibliography

Dedicated bibliography covering the entire class programme (Slides, References, Training Code, Excel Files) will be uploaded step-by-step at the UniBo Virtuale Platform (virtuale.unibo.it). Additional readings are available upon request (but they are not mandatory).

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 2-3 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.

Backgrounds of Production Management Theory (Requirements Planning, Inventory Management, JIT and Kan ban systems, push vs. pull policies) and vocabulary (BOM, Task, working cycles, Resource) are recommended.

Office hours

See the website of Riccardo Accorsi

SDGs

Decent work and economic growth Industry, innovation and infrastructure Responsible consumption and production

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.