96178 - Sustainable Operations and Logistics M

Learning outcomes

Students learn the general criteria and methods aimed at designing, managing and optimizing sustainable production and distribution systems, logistics and operations. Since fostering environmental sustainability in in industrial operations compels complex design and planning minset, students will learn a) how to formulate supply chain problems using linear and mixed-integer programming and b) how to develop a multi-decisions decision-support systems (DSS) for sustainable Industrial operations design and planning.

Course contents

Approach and Methodology

• Sustainable Operations models Engineering: Decisional framework based on Problem-Entity-Methods-Decisions-Performance; Classification of Sustainable Operations and related decisional Hierarchy.
• Operational DSS Design and Development: Collection and management of Production and Demand data; Linear solver; Languages for Mathematical Programming and Optimization (AMPL).
• Holistic Planning & Sustainability: Modeling production and distribution systems and operations as a unique, integrated sustainable industrial ecosystem.

Topics

Planning Sustainability throughout production and distribution systems covers five dimensions and stages, which together provide the class outline:

• Procurement planning: Suppliers selection, Sustainable Resource Requirement Planning, Environmental- and resilience-driven make-or-buy modeling.
• Manufacturing system optimization: Sustainable Manufacturing System Design; Production waste minimization; MTO and MTS with environmental consideration;
• Packaging Loading: Sustainable Packaging design and Pallet Loading Problem.
• Distribution, Warehousing and Transportation: Capacitated facility location problem with environmental function; Network design and demand allocation; Multi-objective optimization for supply chain network design; Multi-modal transportation and GHGs emissions minimization.
  
• Impact Mitigation strategies: Impact assessment, Off-set carbon emissions and fossil energy consumption. Multi-objective optimization.

 

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 operations 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 optimization models for the industrial operations and supply chain plannning.

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.

Previous knowledge in the fundamentals of Production Management and Industrial Plants, Industrial Logistics, Plant Services, Operations Research bases, Information Systems and Databases is expected but not an essential 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