OVERVIEW

The aim of the teaching unit is to provide students with basic knowledge of data analysis, forecasting and mathematical optimization methods to solve decision problems useful for the training of maritime personnel and, in particular, for the profession of engineer and deck officers.

AIMS AND CONTENT

LEARNING OUTCOMES

The teaching unit introduces the students to data analytics, forecasting and optimization models and methods to enforce problem solving skills and support the decision making process applied to problems arising in the maritime field. The basic theoretical part is complemented by laboratories that introduce the use of specific software for data analysis and solution of real problems of practical interest. In particular, the focus will be on route planning, cargo loading and stowage, flow management and network optimization problems.

AIMS AND LEARNING OUTCOMES

The teaching unit aims at presenting the subject in its theoretical, methodological and applicative aspects in order to provide students with knowledge of the applicable models and methodologies to be used to elaborate and analyze data and solve different types of problems. The lab activities aim at providing the student with the knowledge and skills to use specific software to solve the practical problems faced.

The practical problems addressed in the course concern problems of interest for the profession of engineer and deck officers such as: stowage and loading of different types of ships, route planning, crew scheduling, flow of people and in emergency situations, search and rescue techniques, inventory and warehouse management.

With reference to learning outcomes, at the end of the teaching unit the students must have acquired competences that allow them to understand, describe and solve different types of real problems in maritime trasports, develop models and methods and use optimization software environments with a certain mastery of reference. Specifically,  students will:

• Know and understand the main optimization tools and methods that allow them to identify the best decision to be taken in different application contexts in maritime field.
• Know the main elements of a decision problem in terms of decision makers involved, type of data available, variables, constraints and objectives.
• Apply the acquired knowledge to describe, formalize and solve real problems, even different from those proposed during the lessons, using the correct models and optimization methods learned.
• Use both the conceptual and operational knowledge acquired with independent assessment and critical reasoning skills, developing original models applicable in the working context in which they will operate.
• Use  correct terminology and technical language to clearly communicate the main elements of the decision-making process and of the optimization method used.
• Have the ability to apply the main topics of the discipline in the context in which they will work.

Students will also have acquired the following transversal skills:

• Ability to communicate effectively in written and oral form, critical thinking, ability to use, process and evaluate different sources and information, argumentative skills.
• Ability to identify own abilities, focus, handle complexity, think critically, make decisions, work independently, ask for support, handle stress.
• Ability to manage social interactions, cooperative attitude, constructive communication in different environments, ability to respect others and their needs, willingness to overcome prejudices, express and understand different points of view, conflict management, ability to build trust, empathy.
• Awareness with respect to own learning strategies, organization and evaluation of personal learning according to what is understood and learned, understanding of own needs and ways of developing skills, ability to identify and pursue learning goals.
• The teaching unit provides the theoretical tools for achieving the competencies required by the individual functions under the STCW Convention. Specifically:

  • Functions 1,2,3, of Table A-II/1 and A-II/2 of the STCW Convention (deck officer).With particular attention to: Cargo handling and stowage at the operational level and Cargo handling and stowage at the management level

  • Function 1,2,3 of the Table  A-III/6 of the STCW Convention (electrotechnical officer).  Function 3 of the Table  A-III/2, with particular attention: Manage safe and effective maintenance and repair procedures, Planning maintenance, Planning repairs

  • Function 1 of the Table  A-III/1 e A-III/2 of the STCW Convention (marine engineer) with particular attention to Engine-room resource management:  Knowledge of engine-room resource management principles, including allocation, assignment, and prioritization of resources, Plan and schedule operations

• The teaching unit contributes to the enhancement of soft skills, particularly literacy-functional competence (advanced level), personal and social competence (advanced level), and learning to learn (advanced level), and contributes to the attainment of the related Open Badges.

PREREQUISITES

Mathematics and Algebra

TEACHING METHODS

The teaching unit is organised according to a blended learning model that integrates in-person lectures, synchronous online lectures delivered through the Microsoft Teams platform, guided practical exercises, hands-on laboratory sessions, e-tivities and virtual classrooms, with the aim of progressively developing both theoretical knowledge and practical skills in Data Analytics and Optimization Methods for decision support.

The teaching unit is divided into two modules, delivered during the first and second semesters, respectively. The first module focuses on descriptive data analysis and forecasting methods, while the second introduces the main optimization models and methods. In both modules, theoretical instruction is continuously integrated with guided practical exercises, mathematical modelling activities and the analysis of real case studies, enabling students to apply the methodologies learned to decision-making problems arising in the maritime sector.

Teaching Delivery (TEL-DE): 72 hours

Teaching delivery activities include:

• in-person lectures, mainly devoted to introducing the main topics of the course, conducting selected practical exercises and consolidating students' learning;
• synchronous online lectures delivered through the Microsoft Teams platform, focusing on the theoretical and methodological aspects of the course and on guided practical exercises;
• 24 hours of in-person laboratory sessions, organised into six laboratories dedicated to the formulation and solution of real-world case studies using Excel Solver and optimization software. Laboratory activities cover Linear Programming, Transportation Problems, Network Optimization models (Shortest Path, Minimum Cost Flow and Project Management), and Integer Programming with applications to the maritime sector, including cargo stowage and crew scheduling.

The distribution of activities between in-person and synchronous online teaching may vary throughout the two semesters according to organisational and educational requirements. The detailed timetable will be published on the course AulaWeb page.

Interactive Teaching (TEL-DI)

The teaching unit includes interactive learning activities aimed at consolidating the knowledge acquired during lectures and developing students' analytical, modelling and problem-solving skills. These activities will be carried out with the support of the teaching tutor and will include:

• individual and group e-tivities, consisting of guided exercises, mathematical modelling activities, dataset analysis and case studies using Excel and optimization software;
• self-assessment quizzes, available on AulaWeb with automatic feedback, designed to monitor students' progress and reinforce the main theoretical and practical concepts covered during the course;
• virtual classrooms, scheduled throughout the semester and coordinated by the teaching tutor, dedicated to discussing exercises, answering students' questions, analysing case studies and providing feedback on the activities carried out.

The teaching unit adopts an integrated teaching approach that combines theoretical lectures, guided practical exercises, computer laboratory sessions, case study analysis and mathematical modelling activities applied to real decision-making problems. Active and student-centred learning methodologies—including Problem-Based Learning, Team-Based Learning, Flipped Classroom and Learning by Doing—will be adopted to promote the development of both disciplinary and transferable skills identified in the learning outcomes of the teaching unit and contributing to the achievement of the related Open Badges.

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the lecturer and the School's disability representative at the beginning of the course to agree on appropriate teaching arrangements that, while respecting the learning objectives of the teaching unit, take into account individual learning needs. Contact details for the lecturer and the School's disability representative are available at the following link: Comitato di Ateneo per l'inclusione delle studentesse e degli studenti con disabilità o con DSA | UniGe | University of Genoa.

SYLLABUS/CONTENT

The teaching unit is organised into two modules, Analytics (first semester) and Optimization (second semester), structured into six thematic blocks. In each block, theoretical instruction is integrated with practical exercises, laboratory activities and interactive learning activities aimed at developing students' skills in data analysis, mathematical modelling and problem solving. The organisation of the teaching and interactive learning activities is described in the Teaching Methods section.

Analytics Module (First Semester)

Thematic Block 1 – Introduction to Data Analytics and Decision Support Models

Contents:

• Introduction to the teaching unit.
• Introduction to Data Analytics and Operations Research.
• Descriptive, predictive and prescriptive models.
• Phases of a data analytics study.
• Introduction to the use of Excel for data analytics.

Learning activities: in-person lectures, introductory practical exercises and interactive learning activities.

Thematic Block 2 – Data Analysis and Representation

Contents:

• Descriptive statistics.
• Probability and uncertainty.
• Data analysis and representation using Excel.

Learning activities: synchronous lectures, guided practical exercises and interactive learning activities.

Thematic Block 3 – Trend Analysis and Forecasting Models

Contents:

• Time series and trend analysis.
• Forecasting models: linear regression and moving average.
• Data analysis and forecasting using Excel.

Learning activities: synchronous lectures, practical exercises, interactive learning activities and a final practical session.

Optimization Module (Second Semester)

Thematic Block 4 – Introduction to Mathematical Modelling and Linear Programming

Contents:

• From real-world problems to mathematical models.
• Decision variables.
• Objective function and constraints.
• Introduction to Linear Programming.
• Resource allocation and transportation problems.

Learning activities: theoretical lectures (in person and/or synchronous online), guided practical exercises, laboratory activities and interactive learning activities.

Thematic Block 5 – Network Optimization

Contents:

• Graphs: properties, definitions and basic terminology.
• Shortest Path Problem.
• Minimum Spanning Trees, Maximum Flow and Minimum Cost Flow problems.
• Network techniques for Project Management.
• Applications to route planning, flow management and project scheduling.

Learning activities: theoretical lectures (in person and/or synchronous online), practical exercises, laboratory activities and interactive learning activities.

Thematic Block 6 – Integer Programming and Applications

Contents:

• Introduction to Integer Programming.
• Binary variables and logical constraints.
• Mathematical modelling of decision problems.
• Maritime applications, with particular reference to cargo stowage and crew scheduling.

Learning activities: theoretical lectures (in person and/or synchronous online), practical exercises, final laboratory session and interactive learning activities.

RECOMMENDED READING/BIBLIOGRAPHY

The text books and any supplementary materials will be communicated at the beginning of the course and published in the course Aulaweb page.

All teaching materials, including lecture slides, exercises, datasets and additional learning resources, will be made available through the course AulaWeb page.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/corsi/11929/studenti-orario

Starting on I semester - annual teaching unit 

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The achievement of the expected learning outcomes is assessed through a written exam/quiz (for the theoretical part) and the completion of a project—possibly in groups—for the practical and applied part. For attending students, some group-based lab activities (Team-Based Learning, TBL) will be proposed and assessed as an alternative to the project work for the practical component. The dates of these assessed activities will be published on Aulaweb at the beginning of each semester. Additional individual activities carried out during lab sessions and asynchronous assignments completed at home will be evaluated as bonus points in the final grade. The written exam and the project work/TBL account for 90% of the final grade, while participation in lab activities and home assignments contribute 10% to the final evaluation.

Working students and students with certified SLD (Specific Learning Disorders), disability or other special educational needs (BES) are advised to contact the teacher at the beginning of the course to agree on teaching and examination arrangements so to take into account individual learning patterns, while respecting the teaching objectives.

ASSESSMENT METHODS

The written test is aimed at assessing the degree of knowledge of the theoretical topics discussed in class. While the capacity of critical evaluation and reasoning and the ability to apply the acquired knowledge are assessed through the project work and/or the Lab activities. 

The overall assessment enables verification of the achievement of the expected learning outcomes related both to the theoretical understanding of the models and to the ability to apply them to the formulation and solution of real decision-making problems.

FURTHER INFORMATION

The teaching unit is available on Aulaweb.

For any additional information not included in the teaching unit description, please contact the lecturer.