OVERVIEW

The quantitative methods characteristic of operations research and management science, together with the soft skills relating to problem-solving and teamwork, are now universally recognised as indispensable tools for decision-making when analysing the operation of complex systems, such as production, logistics, transport and service systems. Those working in business management cannot therefore afford to ignore the knowledge and use of these tools, and in particular the process planning techniques and decision-support methods most useful to management.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims to provide the tools necessary to support management in operations planning decisions. Methods and models specific to management science, such as linear programming, scenario analysis, inventory management and forecasting methods, will be covered for single- and multi-criteria problems. The methods presented will be complemented by an introduction to the basic concepts of artificial intelligence and machine learning to support the analysis of scarce resources and the resulting managerial decisions.

The consolidation of the proposed methodologies is facilitated by the fact that all lessons are conducted entirely in a computer lab. Business cases will be developed and analysed, both individually and in groups, using the Excel spreadsheet as a decision-support tool, thereby also fostering teamwork and problem-solving skills.

AIMS AND LEARNING OUTCOMES

The main objective of the course is to provide students with the fundamental concepts of Operations Research that are most relevant to business operations planning. Models and methods for solving complex decision-making problems typical of management science will be presented, including through the use of software tools. The methods presented will be complemented by an introduction to the basic concepts of artificial intelligence and machine learning to support the analysis of scarce resources and the resulting managerial decisions. The expected learning outcomes, including the acquisition of soft skills, are:
• the ability to identify and analyse various business processes, identifying decision-making elements, objectives and operational constraints;
• consolidation of proficiency in the use of spreadsheets, such as Excel, and the solver component, for the formulation and resolution of planning problems;
• ability to use advanced software environments;
• problem-solving skills;
• teamwork skills;
• the ability to retrieve data using search engines and websites;
• the ability to independently propose and analyse case studies relating to problems discussed in class;
• public speaking skills.

PREREQUISITES

To successfully complete this course, students are expected to have a basic knowledge of the concepts covered in mathematics and statistics courses.

TEACHING METHODS

The course takes place in a computer lab, giving all students the opportunity to actively participate in lessons and work alongside the lecturer, combining theory with the definition, development and analysis of various models and case studies. The course includes group work and presentations by business representatives.

Students with disabilities, SLD or SEN 
Students with disabilities, with SLD or with SEN are reminded that, to request exam accommodations, they must first upload their certification to the University website at servizionline.unige.it<https://servizionline.unige.it/>, in the “Students” section. The documentation will be checked by the University’s Services for the Inclusion of Students with Disabilities and with SLD. 

At the beginning of the course, students are advised to contact the lecturer to agree on exam arrangements which, while respecting the learning objectives of the course, take individual learning needs into account. 

To request compensatory tools or dispensatory measures, students with disabilities or SLD must fill in the dedicated Webform available athttps://unige.it/disabilita-dsa, at least 7 working days before the exam. 

Students with SEN may instead send their request by e-mail to the lecturer, copying the Department Representative, Prof. Elena Lagomarsino, atinclusione.economia@unige.it<mailto:inclusione.economia@unige.it>, and the Inclusion Office atinclusione.studenti@info.unige.it<mailto:inclusione.studenti@info.unige.it>. 

Requests from students will be assessed by the lecturer and may be approved or rejected.

SYLLABUS/CONTENT

In line with the course objectives described above, the course content is as follows:
1.    Introduction to Operations Research. Introduction to decision-making problems. Overview of the main decision-support methods for management.
2.    Introduction to Linear Programming (LP). Basic prototypical planning problems: production planning (single- and multi-period), transport problems (single- and multi-level), service planning.
3.    Using Excel to formulate and solve LP problems. Analysis of case studies.  Analysis of solutions and identification of scarce resources. 
4.    Integrated analysis of production-distribution and inventory management problems.
5.    Introduction to inventory management. Determining the optimal inventory level. ABC classification. Solving and formulating case studies using Excel.
6.    An overview of the use of artificial intelligence tools integrated with spreadsheets to analyse information relating to operations planning problems.
7.    Forecasting methods. Time series analysis. Identification of seasonal factors. Demand forecasting methods using basic machine learning techniques.
8.    Introduction to multi-criteria decision-making methods. Definition of a multi-criteria problem. Analysis of case studies and resolution using ad hoc software.

•    During the course, there will be testimonials and presentations of  case studies.

RECOMMENDED READING/BIBLIOGRAPHY

Course materials (slides, articles, handouts, etc.):
•    Materials provided by the lecturer, slides and videos uploaded to Aulaweb and/or the course’s Teams platform.
•    Reference textbooks: F. S. Hillier, G. L. Lieberman. “Operations Research: Fundamentals”. McGraw Hill, 2010 (selected sections);

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

The lessons take place in the first semester.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of a written paper and an oral examination. Only students who pass the written paper with a mark of over 18/30 are eligible to sit the oral examination.

The oral examination will also be marked out of 30. The final mark will be determined by the average of the marks obtained in the written paper and the oral examination.

Up to a maximum of 3 points may also be added to the final mark, as determined by the lecturer during the oral examination, based on any clarifications regarding the conduct of the written examination and the marking of the written paper.

ASSESSMENT METHODS

In assessing students’ skills, consideration will be given not only to the results of the written examination and the interview, but also to their active participation in class. 

Students will be required to undertake a group project, which will be presented in class to the entire cohort.

For students who do not attend classes, the interview will also assess their ability to present their work and analyse the issues addressed.

FURTHER INFORMATION

The instructor analysed and considered the results of the teaching evaluation questionnaires relating to the previous academic year.

Please contact the instructor for any further information not included.