LEARNING OUTCOMES

The Course introduces to optimization models and methods for the solution of decision problems, with particular attention to models and problems arising in Maritime. In particular the focus will be on route planning, cargo loading and stowage, flow management.

AIMS AND LEARNING OUTCOMES

The course 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 solve different types of problems. The laboratory aims 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 course 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, at the end of the course, the 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.
• Having acquired a correct terminology and technical language to clearly communicate the main elements of the decision-making process and of the optimization method used.
• Developed learning skills that will enable them to study and apply the main topics of the discipline in the context in which they will work.

PREREQUISITES

Mathematics and Algebra

TEACHING METHODS

Lectures, computer labs using application software, small group activities on case studies and project development.

In the case of still lacking health emergency situation, the updated distance and/or blended teaching methods will be communicated on the Aulaweb website of the course (registration is required and recommended).

Working students and students with certified SLD (Specific Learning Disorders), disability or other special educational needs 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.

SYLLABUS/CONTENT

In each part of the course the theoretical part, aimed at providing the basic methodological contents, will be flanked by the practical laboratory, carried out in the computer lab, through the use of appropriate software environments (excel, lindo) used to formalize and solve the practical problems addressed.

Part I:

• Introduction to Operations Research and problem solving: from real problems to mathematical models.
• Classification of models and optimization methods. Unconstrained and constrained optimization, linear, non-linear and integer programming.
• Introduction to linear programming (LP).
• The transportation problem.
• Using Excel and Lingo for formulating and solving LP optimization problems.

Part II:

• Network optimization problems.
• Graphs: properties, definitions and basic terminology of network
• Shortest path problem and optimal path models
• Maximum flow and minimum cost flow problem
• Network models and techniques for project management
• Using Excel and Lingo for formulating and solving network optimization problems
• Route planning, search and rescue techniques, flow of people in emergency situations, drydoc project management

Part III:

• Integer programming (IP) and binary programming (BIP).
• Use of binary variables and logical constraints.
• Set covering, set partitioning and set packing problems
• Using Excel and Lingo for formulating and solving IP an BIP problems
• Ship Stowage and loading; crew scheduling and movement.

Part IV:

• Forecasting methods, regression models and moving average techniques.
• Inventory theory and warehouse management. The economic lot size model.
• Using Excel for forecasting and for solving inventory models.

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.