Operations Research

Credits
6
Types
Specialization complementary (Computing)
Requirements
  • Prerequisite: PE
Department
EIO
In the environment of complex organizations of large and medium-range in the industry, government and business, results of decisions that may affect its operation / performance is of utmost importance for the management. Operations Research is a discipline aimed at providing tools for preparation, analysis and efficient resolution of these systems using models which can quantitatively measure the results of the decisions of the leadership of organizations. Today, integration is key for this class of systems to aid decision making within the different information systems that can operate in organizations. The course begins by presenting a case study with which to illustrate these concepts and continues with an exhibition of models established in the Operations Research techniques and their efficient resolution. During the course students develop and solve one of these models adapted to the needs of the real case of an organization and evaluate and discuss their interaction with information systems present in it.

Teachers

Person in charge

  • Esteve Codina Sancho ( )

Others

  • Bhumika Ashvinbhai Patel ( )
  • Joan Garcia Subirana ( )

Weekly hours

Theory
2
Problems
1
Laboratory
1
Guided learning
0
Autonomous learning
6

Competences

Transversal Competences

Teamwork

  • G5 - To be capable to work as a team member, being just one more member or performing management tasks, with the finality of contributing to develop projects in a pragmatic way and with responsibility sense; to assume compromises taking into account the available resources.
  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.
  • CTR3 - Capacity of being able to work as a team member, either as a regular member or performing directive activities, in order to help the development of projects in a pragmatic manner and with sense of responsibility; capability to take into account the available resources.

Entrepreneurship and innovation

  • G1 - To know and understand the organization of a company and the sciences which govern its activity; capacity to understand the labour rules and the relation between planning, industrial and business strategies, quality and benefit. To develop creativity, entrepreneur spirit and innovation tendency.
  • CT1 - Know and understand the organization of a company and the sciences that govern its activity; have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit. Being aware of and understanding the mechanisms on which scientific research is based, as well as the mechanisms and instruments for transferring results among socio-economic agents involved in research, development and innovation processes.
  • CTR1 - Capacity for knowing and understanding a business organization and the science that rules its activity, capability to understand the labour rules and the relationships between planning, industrial and commercial strategies, quality and profit. Capacity for developping creativity, entrepreneurship and innovation trend.

Appropiate attitude towards work

  • G8 [Avaluable] - To have motivation to be professional and to face new challenges, have a width vision of the possibilities of the career in the field of informatics engineering. To feel motivated for the quality and the continuous improvement, and behave rigorously in the professional development. Capacity to adapt oneself to organizational or technological changes. Capacity to work in situations with information shortage and/or time and/or resources restrictions.
    • G8.3 - To be motivated for the professional development, to face new challenges and the continuous improvement. To have capacity to work in situations with a lack of information.
  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.
  • CTR5 - Capability to be motivated by professional achievement and to face new challenges, to have a broad vision of the possibilities of a career in the field of informatics engineering. Capability to be motivated by quality and continuous improvement, and to act strictly on professional development. Capability to adapt to technological or organizational changes. Capacity for working in absence of information and/or with time and/or resources constraints.

Reasoning

  • G9 - Capacity of critical, logical and mathematical reasoning. Capacity to solve problems in her study area. Abstraction capacity: capacity to create and use models that reflect real situations. Capacity to design and perform simple experiments and analyse and interpret its results. Analysis, synthesis and evaluation capacity.
  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..
  • CTR6 - Capacity for critical, logical and mathematical reasoning. Capability to solve problems in their area of study. Capacity for abstraction: the capability to create and use models that reflect real situations. Capability to design and implement simple experiments, and analyze and interpret their results. Capacity for analysis, synthesis and evaluation.

Sustainability and social commitment

  • G2 - To know and understand the complexity of the economic and social phenomena typical of the welfare society. To be capable of analyse and evaluate the social and environmental impact.
  • CT2 - Capability to know and understand the complexity of economic and social typical phenomena of the welfare society; capability to relate welfare with globalization and sustainability; capability to use technique, technology, economics and sustainability in a balanced and compatible way.
  • CTR2 - Capability to know and understand the complexity of the typical economic and social phenomena of the welfare society. Capacity for being able to analyze and assess the social and environmental impact.

Third language

  • G3 - To know the English language in a correct oral and written level, and accordingly to the needs of the graduates in Informatics Engineering. Capacity to work in a multidisciplinary group and in a multi-language environment and to communicate, orally and in a written way, knowledge, procedures, results and ideas related to the technical informatics engineer profession.
  • CT5 - Achieving a level of spoken and written proficiency in a foreign language, preferably English, that meets the needs of the profession and the labour market.

Effective oral and written communication

  • G4 - To communicate with other people knowledge, procedures, results and ideas orally and in a written way. To participate in discussions about topics related to the activity of a technical informatics engineer.

Information literacy

  • G6 [Avaluable] - To manage the acquisition, structuring, analysis and visualization of data and information of the field of the informatics engineering, and value in a critical way the results of this management.
    • G6.3 - To plan and use the necessary information for an academic essay (for example, the final project of the grade) using critical reflection about the used information resources. To manage information in a competent, independent and autonomous way. To evaluate the found information and identify its deficiencies.
  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
  • CTR4 - Capability to manage the acquisition, structuring, analysis and visualization of data and information in the area of informatics engineering, and critically assess the results of this effort.

Autonomous learning

  • G7 - To detect deficiencies in the own knowledge and overcome them through critical reflection and choosing the best actuation to extend this knowledge. Capacity for learning new methods and technologies, and versatility to adapt oneself to new situations.

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
  • CB7 - Ability to integrate knowledge and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.
  • CB1 - That students have demonstrated to possess and understand knowledge in an area of ??study that starts from the base of general secondary education, and is usually found at a level that, although supported by advanced textbooks, also includes some aspects that imply Knowledge from the vanguard of their field of study.
  • CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
  • CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
  • CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.
  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy
  • CB10 - Possess and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context.

Transversals

  • CT1 - Entrepreneurship and innovation. Know and understand the organization of a company and the sciences that govern its activity; Have the ability to understand labor standards and the relationships between planning, industrial and commercial strategies, quality and profit.
  • CT2 - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
  • CT3 - Efficient oral and written communication. Communicate in an oral and written way with other people about the results of learning, thinking and decision making; Participate in debates on topics of the specialty itself.
  • CT4 - Teamwork. Be able to work as a member of an interdisciplinary team, either as a member or conducting management tasks, with the aim of contributing to develop projects with pragmatism and a sense of responsibility, taking commitments taking into account available resources.
  • CT5 - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
  • CT6 - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

Gender perspective

  • CT6 - An awareness and understanding of sexual and gender inequalities in society in relation to the field of the degree, and the incorporation of different needs and preferences due to sex and gender when designing solutions and solving problems.

Technical Competences

Common technical competencies

  • CT1 - To demonstrate knowledge and comprehension of essential facts, concepts, principles and theories related to informatics and their disciplines of reference.
  • CT2 - To use properly theories, procedures and tools in the professional development of the informatics engineering in all its fields (specification, design, implementation, deployment and products evaluation) demonstrating the comprehension of the adopted compromises in the design decisions.
  • CT3 - To demonstrate knowledge and comprehension of the organizational, economic and legal context where her work is developed (proper knowledge about the company concept, the institutional and legal framework of the company and its organization and management)
  • CT4 - To demonstrate knowledge and capacity to apply the basic algorithmic procedures of the computer science technologies to design solutions for problems, analysing the suitability and complexity of the algorithms.
  • CT5 - To analyse, design, build and maintain applications in a robust, secure and efficient way, choosing the most adequate paradigm and programming languages.
  • CT6 - To demonstrate knowledge and comprehension about the internal operation of a computer and about the operation of communications between computers.
  • CT7 - To evaluate and select hardware and software production platforms for executing applications and computer services.
  • CT8 - To plan, conceive, deploy and manage computer projects, services and systems in every field, to lead the start-up, the continuous improvement and to value the economical and social impact.

Technical competencies

  • CE1 - Skillfully use mathematical concepts and methods that underlie the problems of science and data engineering.
  • CE2 - To be able to program solutions to engineering problems: Design efficient algorithmic solutions to a given computational problem, implement them in the form of a robust, structured and maintainable program, and check the validity of the solution.
  • CE3 - Analyze complex phenomena through probability and statistics, and propose models of these types in specific situations. Formulate and solve mathematical optimization problems.
  • CE4 - Use current computer systems, including high performance systems, for the process of large volumes of data from the knowledge of its structure, operation and particularities.
  • CE5 - Design and apply techniques of signal processing, choosing between different technological tools, including those of Artificial vision, speech recognition and multimedia data processing.
  • CE6 - Build or use systems of processing and comprehension of written language, integrating it into other systems driven by the data. Design systems for searching textual or hypertextual information and analysis of social networks.
  • CE7 - Demonstrate knowledge and ability to apply the necessary tools for the storage, processing and access to data.
  • CE8 - Ability to choose and employ techniques of statistical modeling and data analysis, evaluating the quality of the models, validating and interpreting them.
  • CE9 - Ability to choose and employ a variety of automatic learning techniques and build systems that use them for decision making, even autonomously.
  • CE10 - Visualization of information to facilitate the exploration and analysis of data, including the choice of adequate representation of these and the use of dimensionality reduction techniques.
  • CE11 - Within the corporate context, understand the innovation process, be able to propose models and business plans based on data exploitation, analyze their feasibility and be able to communicate them convincingly.
  • CE12 - Apply the project management practices in the integral management of the data exploitation engineering project that the student must carry out in the areas of scope, time, economic and risks.
  • CE13 - (End-of-degree work) Plan and design and carry out projects of a professional nature in the field of data engineering, leading its implementation, continuous improvement and valuing its economic and social impact. Defend the project developed before a university court.

Especifics

  • CE1 - Develop efficient algorithms based on the knowledge and understanding of the computational complexity theory and considering the main data structures within the scope of data science
  • CE2 - Apply the fundamentals of data management and processing to a data science problem
  • CE3 - Apply data integration methods to solve data science problems in heterogeneous data environments
  • CE4 - Apply scalable storage and parallel data processing methods, including data streams, once the most appropriate methods for a data science problem have been identified
  • CE5 - Model, design, and implement complex data systems, including data visualization
  • CE6 - Design the Data Science process and apply scientific methodologies to obtain conclusions about populations and make decisions accordingly, from both structured and unstructured data and potentially stored in heterogeneous formats.
  • CE7 - Identify the limitations imposed by data quality in a data science problem and apply techniques to smooth their impact
  • CE8 - Extract information from structured and unstructured data by considering their multivariate nature.
  • CE9 - Apply appropriate methods for the analysis of non-traditional data formats, such as processes and graphs, within the scope of data science
  • CE10 - Identify machine learning and statistical modeling methods to use and apply them rigorously in order to solve a specific data science problem
  • CE11 - Analyze and extract knowledge from unstructured information using natural language processing techniques, text and image mining
  • CE12 - Apply data science in multidisciplinary projects to solve problems in new or poorly explored domains from a data science perspective that are economically viable, socially acceptable, and in accordance with current legislation
  • CE13 - Identify the main threats related to ethics and data privacy in a data science project (both in terms of data management and analysis) and develop and implement appropriate measures to mitigate these threats
  • CE14 - Execute, present and defend an original exercise carried out individually in front of an academic commission, consisting of an engineering project in the field of data science synthesizing the competences acquired in the studies

Technical Competences of each Specialization

Information systems specialization

  • CSI2 - To integrate solutions of Information and Communication Technologies, and business processes to satisfy the information needs of the organizations, allowing them to achieve their objectives effectively.
    • CSI2.1 - To demonstrate comprehension and apply the management principles and techniques about quality and technological innovation in the organizations.
    • CSI2.2 - To conceive, deploy, organize and manage computer systems and services, in business or institutional contexts, to improve the business processes; to take responsibility and lead the start-up and the continuous improvement; to evaluate its economic and social impact.
    • CSI2.6 - To demonstrate knowledge and capacity to apply decision support and business intelligence systems.
  • CSI3 - To determine the requirements of the information and communication systems of an organization, taking into account the aspects of security and compliance of the current normative and legislation.
    • CSI3.5 - To propose and coordinate changes to improve the operation of the systems and the applications.
  • CSI4 - To participate actively in the specification, design, implementation and maintenance of the information and communication systems.
  • CSI1 - To demonstrate comprehension and apply the principles and practices of the organization, in a way that they could link the technical and management communities of an organization, and participate actively in the user training.

Software engineering specialization

  • CES1 - To develop, maintain and evaluate software services and systems which satisfy all user requirements, which behave reliably and efficiently, with a reasonable development and maintenance and which satisfy the rules for quality applying the theories, principles, methods and practices of Software Engineering.
  • CES2 - To value the client needs and specify the software requirements to satisfy these needs, reconciling conflictive objectives through searching acceptable compromises, taking into account the limitations related to the cost, time, already developed systems and organizations.
  • CES3 - To identify and analyse problems; design, develop, implement, verify and document software solutions having an adequate knowledge about the current theories, models and techniques.

Information technology specialization

  • CTI1 - To define, plan and manage the installation of the ICT infrastructure of the organization.
  • CTI2 - To guarantee that the ICT systems of an organization operate adequately, are secure and adequately installed, documented, personalized, maintained, updated and substituted, and the people of the organization receive a correct ICT support.
  • CTI3 - To design solutions which integrate hardware, software and communication technologies (and capacity to develop specific solutions of systems software) for distributed systems and ubiquitous computation devices.
  • CTI4 - To use methodologies centred on the user and the organization to develop, evaluate and manage applications and systems based on the information technologies which ensure the accessibility, ergonomics and usability of the systems.

Computer engineering specialization

  • CEC1 - To design and build digital systems, including computers, systems based on microprocessors and communications systems.
  • CEC2 - To analyse and evaluate computer architectures including parallel and distributed platforms, and develop and optimize software for these platforms.
  • CEC3 - To develop and analyse hardware and software for embedded and/or very low consumption systems.
  • CEC4 - To design, deploy, administrate and manage computer networks, and manage the guarantee and security of computer systems.

Computer science specialization

  • CCO1 - To have an in-depth knowledge about the fundamental principles and computations models and be able to apply them to interpret, select, value, model and create new concepts, theories, uses and technological developments, related to informatics.
    • CCO1.3 - To define, evaluate and select platforms to develop and produce hardware and software for developing computer applications and services of different complexities.
  • CCO2 - To develop effectively and efficiently the adequate algorithms and software to solve complex computation problems.
    • CCO2.4 - To demonstrate knowledge and develop techniques about computational learning; to design and implement applications and system that use them, including these ones dedicated to the automatic extraction of information and knowledge from large data volumes.
  • CCO3 - To develop computer solutions that, taking into account the execution environment and the computer architecture where they are executed, achieve the best performance.

Academic

  • CEA1 - Capability to understand the basic principles of the Multiagent Systems operation main techniques , and to know how to use them in the environment of an intelligent service or system.
  • CEA2 - Capability to understand the basic operation principles of Planning and Approximate Reasoning main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.
  • CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA5 - Capability to understand the basic operation principles of Natural Language Processing main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA6 - Capability to understand the basic operation principles of Computational Vision main techniques, and to know how to use in the environment of an intelligent system or service.
  • CEA7 - Capability to understand the problems, and the solutions to problems in the professional practice of Artificial Intelligence application in business and industry environment.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.
  • CEA9 - Capability to understand Multiagent Systems advanced techniques, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA10 - Capability to understand advanced techniques of Human-Computer Interaction, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA11 - Capability to understand the advanced techniques of Computational Intelligence, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA14 - Capability to understand the advanced techniques of Vision, Perception and Robotics, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP1 - Capability to solve the analysis of information needs from different organizations, identifying the uncertainty and variability sources.
  • CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.
  • CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.
  • CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.
  • CEP7 - Capability to respect the legal rules and deontology in professional practice.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Direcció i gestió

  • CDG1 - Capability to integrate technologies, applications, services and systems of Informatics Engineering, in general and in broader and multicisciplinary contexts.
  • CDG2 - Capacity for strategic planning, development, direction, coordination, and technical and economic management in the areas of Informatics Engineering related to: systems, applications, services, networks, infrastructure or computer facilities and software development centers or factories, respecting the implementation of quality and environmental criteria in multidisciplinary working environments .
  • CDG3 - Capability to manage research, development and innovation projects in companies and technology centers, guaranteeing the safety of people and assets, the final quality of products and their homologation.

Especifics

  • CTE1 - Capability to model, design, define the architecture, implement, manage, operate, administrate and maintain applications, networks, systems, services and computer contents.
  • CTE2 - Capability to understand and know how to apply the operation and organization of Internet, technologies and protocols for next generation networks, component models, middleware and services.
  • CTE3 - Capability to secure, manage, audit and certify the quality of developments, processes, systems, services, applications and software products.
  • CTE4 - Capability to design, develop, manage and evaluate mechanisms of certification and safety guarantee in the management and access to information in a local or distributed processing.
  • CTE5 - Capability to analyze the information needs that arise in an environment and carry out all the stages in the process of building an information system.
  • CTE6 - Capability to design and evaluate operating systems and servers, and applications and systems based on distributed computing.
  • CTE7 - Capability to understand and to apply advanced knowledge of high performance computing and numerical or computational methods to engineering problems.
  • CTE8 - Capability to design and develop systems, applications and services in embedded and ubiquitous systems .
  • CTE9 - Capability to apply mathematical, statistical and artificial intelligence methods to model, design and develop applications, services, intelligent systems and knowledge-based systems.
  • CTE10 - Capability to use and develop methodologies, methods, techniques, special-purpose programs, rules and standards for computer graphics.
  • CTE11 - Capability to conceptualize, design, develop and evaluate human-computer interaction of products, systems, applications and informatic services.
  • CTE12 - Capability to create and exploit virtual environments, and to the create, manageme and distribute of multimedia content.

Computer graphics and virtual reality

  • CEE1.1 - Capability to understand and know how to apply current and future technologies for the design and evaluation of interactive graphic applications in three dimensions, either when priorizing image quality or when priorizing interactivity and speed, and to understand the associated commitments and the reasons that cause them.
  • CEE1.2 - Capability to understand and know how to apply current and future technologies for the evaluation, implementation and operation of virtual and / or increased reality environments, and 3D user interfaces based on devices for natural interaction.
  • CEE1.3 - Ability to integrate the technologies mentioned in CEE1.2 and CEE1.1 skills with other digital processing information technologies to build new applications as well as make significant contributions in multidisciplinary teams using computer graphics.

Computer networks and distributed systems

  • CEE2.1 - Capability to understand models, problems and algorithms related to distributed systems, and to design and evaluate algorithms and systems that process the distribution problems and provide distributed services.
  • CEE2.2 - Capability to understand models, problems and algorithms related to computer networks and to design and evaluate algorithms, protocols and systems that process the complexity of computer communications networks.
  • CEE2.3 - Capability to understand models, problems and mathematical tools to analyze, design and evaluate computer networks and distributed systems.

Advanced computing

  • CEE3.1 - Capability to identify computational barriers and to analyze the complexity of computational problems in different areas of science and technology as well as to represent high complexity problems in mathematical structures which can be treated effectively with algorithmic schemes.
  • CEE3.2 - Capability to use a wide and varied spectrum of algorithmic resources to solve high difficulty algorithmic problems.
  • CEE3.3 - Capability to understand the computational requirements of problems from non-informatics disciplines and to make significant contributions in multidisciplinary teams that use computing.

High performance computing

  • CEE4.1 - Capability to analyze, evaluate and design computers and to propose new techniques for improvement in its architecture.
  • CEE4.2 - Capability to analyze, evaluate, design and optimize software considering the architecture and to propose new optimization techniques.
  • CEE4.3 - Capability to analyze, evaluate, design and manage system software in supercomputing environments.

Service engineering

  • CEE5.1 - Capability to participate in improvement projects or to create service systems, providing in particular: a) innovation and research proposals based on new uses and developments of information technologies, b) application of the most appropriate software engineering and databases principles when developing information systems, c) definition, installation and management of infrastructure / platform necessary for the efficient running of service systems.
  • CEE5.2 - Capability to apply obtained knowledge in any kind of service systems, being familiar with some of them, and thorough knowledge of eCommerce systems and their extensions (eBusiness, eOrganization, eGovernment, etc.).
  • CEE5.3 - Capability to work in interdisciplinary engineering services teams and, provided the necessary domain experience, capability to work autonomously in specific service systems.

Specific

  • CEC1 - Ability to apply scientific methodologies in the study and analysis of phenomena and systems in any field of Information Technology as well as in the conception, design and implementation of innovative and original computing solutions.
  • CEC2 - Capacity for mathematical modelling, calculation and experimental design in engineering technology centres and business, particularly in research and innovation in all areas of Computer Science.
  • CEC3 - Ability to apply innovative solutions and make progress in the knowledge that exploit the new paradigms of Informatics, particularly in distributed environments.

Generic Technical Competences

Generic

  • CG1 - Identify and apply the most appropriate data management methods and processes to manage the data life cycle, considering both structured and unstructured data
  • CG2 - Identify and apply methods of data analysis, knowledge extraction and visualization for data collected in disparate formats
  • CG3 - Define, design and implement complex systems that cover all phases in data science projects
  • CG4 - Design and implement data science projects in specific domains and in an innovative way
  • CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
  • CG6 - Capacity for general management, technical management and research projects management, development and innovation in companies and technology centers in the area of Computer Science.
  • CG7 - Capacity for implementation, direction and management of computer manufacturing processes, with guarantee of safety for people and assets, the final quality of the products and their homologation.
  • CG8 - Capability to apply the acquired knowledge and to solve problems in new or unfamiliar environments inside broad and multidisciplinary contexts, being able to integrate this knowledge.
  • CG9 - Capacity to understand and apply ethical responsibility, law and professional deontology of the activity of the Informatics Engineering profession.
  • CG10 - Capacity to apply economics, human resources and projects management principles, as well as legislation, regulation and standardization of Informatics.

Objectives

  1. Knowing the basic methodology and scope of Operations Research
    Related competences: CSI1, G8.3,
    Subcompetences:
    • Distinguish different stages in a project which consists of Operations Research
    • Role of Operations Research models within decision support systems
    • Role of data collection and processing of the necessary information to formulate a model of Operational Research
  2. Learn simple models of O.R., and special solutions
    Related competences: CCO2.4, CSI3.5, G8.3,
    Subcompetences:
    • Learn simple models of linear programming: problems of production and mixtures
    • Knowledge of simple models in nonlinear programming: problem of the maximum volume of a cylinder.
    • Learn simple models of integer linear programming: knapsack problem and fixed charge problems
  3. Understand and identify the components of an optimization problem
    Related competences: CCO1.3, CCO2.4,
    Subcompetences:
    • Distinguish between decision variables and parameters of an optimization problem
    • Knowing how to use algebraic languages for the representation of optimization problems for the definition and resolution models based on optimization
    • Knowing the central role of an optimization problem as a tool in decision-making processes
  4. Identification of objectives in a decision process. Learn how to express constraints, both linear and nonlinear, to meet the conditions for decision variables in the model. To formulate multiobjective programming models and goal programming models.
    Related competences: CSI2.6, CSI1, CSI2.1,
    Subcompetences:
    • Formulation of linear and nonlinear constraints in a model
    • Identify the multiple objectives that may be involved in a model of decision making and its relationship to linear programming models
    • Identification of decision variables and model parameters
    • For problems with two objectives, namely to determine the Pareto optimality frontier
    • Understand and interpret the results and information provided by a model with multiple objectives
    • Knowing the basic formulation of multi-objective problem
    • Being able to define linear programming models suitable for a decision support system and translate them using algebraic manipulation languages,
  5. Understanding the structure and properties of linear and non-linear programming problems
    Related competences: CCO2.4, CSI1,
    Subcompetences:
    • Know the distinguishing characteristics of the problems with nonlinearities
    • To know simple linear programming models: the problem of production, the mixing problem
    • Use of Languages ​​manipualció algebraic and spreadsheet. Identify types of solutions provided by algebraic manipulation languages ​​for linear programming problems
    • Know the difference between local and global optimum
    • Knowing the shape of a standard linear programming problem. Slack and excess variables
    • Know how to calculate the basic feasible solutions of a linear programming problem
    • To know the types of solutions that can have a linear programming problem: unique solutions, alternative solutions, infeasible problems, unbounded problems
  6. Understand and apply the simplex method to solve linear programming problems
    Related competences: CCO2.4,
    Subcompetences:
    • Purpose of reduced costs . Recognize a basic solution as optimal solution of a problem of linear programming. Recognize when there are alternative optimal
    • Make iterations of the simplex method. Concept base change. Calculating reduced costs
    • Concept of Basis. Understand and distinguish between basic and non-basic variables
  7. Know how to solve linear programming problems in which variables are associated to a graph. networks flow problems.
    Related competences: CCO2.4, CSI2.2,
    Subcompetences:
    • Knowing the structure of basic solutions of flow problems on networks. Costs associated with the nodes of trees and dual variables. Calculation of reduced cost coefficients. Cases of one or more items.
    • Application of minimal paths algorithms. (Dijkstra algorithm and labels correcting algorithms)
    • To know the formulation of flow problems in bipartite graphs. Knowing the formulation of the problem at minimal cost.
    • Understand the role of link-node incidence matrix
    • Flow problems on networks with capacities to associate arches. Max Flow min Cut theorem
  8. Understand and apply basic techniques for solving linear problems with integer variables
    Related competences: CCO1.3, CCO2.4,
    Subcompetences:
    • Know and be able to apply the algorithm of Branch and Bound
    • Learn the basic models of coating in the form of integer linear programming problem
    • Learn to formulate policies as logical constraints in integer linear programming model
  9. Understand and identify the inputs and outputs of Operations Research models underlying various information systems and decision support systems described in the practical sessions.
    Related competences: G6.3, CCO1.3, CSI1, CSI2.2, CSI3.5,
    Subcompetences:
    • Knowing the properties of the Operational Research models seen in the practical sessions.
    • Given a set of requirements of an organization, to examine whether patterns seen in the Operations Research sessions are sufficient to meet these needs. Identify gaps and absences in the modeling.
    • Given certain requirements of an organization in relation to a decision support system, adapt and / or extend the Operations Research models seen in the sessions to meet the requirements.
  10. Being able to apply heuristic methods for integer linear programming problems
    Related competences: CSI2.6, CCO1.3, CCO2.4,
    Subcompetences:
    • Apply exchange heuristics for the traveling salesman problem
    • Apply heuristics for plant location problems
  11. Know and be able to apply different kinds of metaheuristics seen in the course
    Related competences: CSI2.6, CCO1.3, CCO2.4,
    Subcompetences:
    • Knowing how to apply the technique of simulated annealing to solve routing problems
    • Learn applying taboo search technique to solve integer linear programming
  12. Being able to effectively use information resources in O.R.
    Related competences: G6.3,
    Subcompetences:
    • Learn to recognize and use appropriate information to perform a job
    • Knowing the type of information that a source can provide
    • Analysis and synthesis of a particular source of information and value in relation to achieving a goal (realization of a work task or project)
  13. Having proper attitude and motivation towards work
    Related competences: G8.3,
    Subcompetences:
    • Motivation for liability, the quality of one's own work and professional realization
    • Adapting to the lack of information and material and temporal constraints
    • Ability to adapt to organizational changes, technology and teamwork

Contents

  1. Introduction to modeling decisions:
    The modeling in the process of decision making. Models of Operations Research. The cycle of operations research methodology
  2. Continuous programming. Properties and methods
    Characteristics of optimization problems. Formulation of optimization problems. Techniques of mathematical programming. Formulation of problem PL. Troubleshooting PL. The geometry of the PL. The simplex method: basic feasible solutions and extreme points. Sensitivity analysis. Introduction to the presence of nonlinearities in the models.
  3. Continuous programming models and systems to support decision making
    Examples of LP problems: production planning; investment problem, transportation problems, mixture problems, inventory problems. Network flow problems. Multi-objective problems. Programming objectives. Presence of non-linearities in models.
  4. Integer Linear Programming
    Integer Linear programming problem properties. Some problems ple: the problem of scheduling workers, problems with routing problems fixed cost and location algorithms PLE: secant planes; Branch & Bound algorithm
  5. Heuristic methods for solving ILP problems
    Constructive heuristics: Greedy methods. Local search. Metaheuristics: beyond local optima. The method of simulated annealing. Tabu search. Genetic algorithms. Aplications of heuristics to routing and other problems.
  6. Search and evaluation of information for conducting a task in O.R.
    Browsers academics. Databases and electronic journals. Assessment Information
  7. Motivation and attitude to work in O.R.
    Motivation for liability, the quality of their work and professional realization. Ability to adapt to organizational changes, technological. Teamwork. Adapting the lack of information and material limitations and time

Activities

Activity Evaluation act


Block 1. Presentation of the objectives of the basic models of IO and IO

Monitoring of exposures and review the material proprocionat for the corresponding session. Assimilation of the role of optimization problems as a source of modeling.
  • Theory: Understanding the objectives of the Operational Research as discipline. Understanding the stages of formulation of a methodological model. Validation of a model. Presentation of a case study. Description and analysis of several case studies involved
  • Autonomous learning: Reading and study material prior to the sessions of theory
Objectives: 1 3 2
Contents:
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
1h

Analysis of information sources

Analysis and evaluation of information provided by certain references (software packages / references that can provide solutions to coursework.
  • Theory: Assessment of value and lack of information sel.leccionada.
  • Autonomous learning: Identification of value and information gaps towards the end of the course work
Objectives: 12
Contents:
Theory
0.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Block 2. Continuous optimization models and systems to aid decision making

Follow the models exhibited in the theory sessions. Resolution of monitored and modeling exercises. In the lab sessions, training in the use of algebraic representation languages.
  • Theory: Description of linear programming models and presence of nonlinearities. Exhibition of the principle of Pareto optimality. Minimization of the norm L1. Exhibition of the program objectives and the worst possible case
  • Problems: Formulation of problems and modeling case study
  • Autonomous learning: Reading and study material prior to meetings of theory. Preparation and reading material for laboratory exercises
Objectives: 1 3 4
Contents:
Theory
4h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Using search engines referrals, BD and Electronic

Search for publications of certain writers in relation to coursework. Viewing videos http://bibliotecnica.upc.edu/habilitats/eines-de-cerca-dinformacio http://bibliotecnica.upc.edu/habilitats/l039estrategia-de-cerca # 4
  • Theory: We provide certain authors and topics related to Employment Course
  • Autonomous learning: Using search engines and initial analysis of references
Objectives: 12
Contents:
Theory
0.5h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Evaluation of the search for references in relation to course work

Delivery report with the 5 most significant references and details of the search tools used to find them
Objectives: 12
Week: 4
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Block 3. Continuous programming problems

Tracking theory classes with the support of teaching materials produced specifically. Assimilation of basic concepts feasible optimal basis, optimal local and global. Ability to perform the steps of the simplex algorithm. Individual and monitored resolution of problems. Ability to define linear and non linear models using algebraic lenguages in the lab sessions
  • Theory: Characterization of linear programming problems. Basic properties of linear programming problems. The concept of feasible region. Optimal unique alternative. Concept vertex of a polyhedral region. Examples. Bases and basic solutions of the simplex algorithm. Development sessions of basic algebraic theory formulation. Examples of iterations of the simplex algorithm. Method of artificial variables. Presence of nonlinearities. Characteristics of the solutions.
  • Problems: Troubleshooting graphically in two dimensions. Iterations of the simplex algorithm. Troubleshooting with simple algebraic language and concepts to advance laboratory classes
  • Autonomous learning: Work by students with educational material and collection problems. Preparation of lab sessions. Exercises by its own.
Objectives: 5 6
Contents:
Theory
5h
Problems
3h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Attitude and motivation toward work. A1

Students discuss laboratory exercises delivered according to guidelines contained in a section.
  • Laboratory: Quality assessment exercises
  • Autonomous learning: Preparation and adoption by the student
Objectives: 13
Contents:
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
3h

Evaluation of information sources

Delivery of a report of the evaluation
Objectives: 12
Week: 6
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Assessing motivation and attitude towards work. A1

Using rubrics
Objectives: 13
Week: 7
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Block 4. Network Flow Problems

Make simplex iterations for the problem of min-cost. application of minimal paths algorithms. implementation of the max-flow algorithm min.cut
  • Theory: Exhibition of the min-cost model. Application of the simplex algorithm. Exhibition and derivation of minimal paths algorithms. Illustration of theorem max-flow min-cut
  • Problems: Exercises and tests monitoring methods and algorithms exposed
  • Autonomous learning: Review of material presented in classes of theory test preparation and monitoring. Exercise has to own.
Objectives: 1 7
Contents:
Theory
3h
Problems
4h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Evaluation of a lab 1

Handed a questionnaire completed by the end of the session. This questionnaire will go.
Objectives: 3 5 6 4 7 2
Week: 8
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Part 1

Test problems for units of 1,2,3 and 4 of the course and the corresponding block 8-related objectives associated blocks 1,2,3 and 4.
Objectives: 1 3 5 6 4 7 2
Week: 8
Type: theory exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Block 5. Integer linear programming modeling

Acquire the ability to model using binary variables of type logical conditions. Taking as reference the models presented in the theory sessions in order to undertake their own development and modeling
  • Theory: Exhibition of models covering and partition of sets and the methodology to reflect condition of type logical with integer variables. Exhibition of the fixed charge models.
  • Problems: Modelling of problems with integer variables / binaries inside a collection of problems
  • Laboratory: Formulation, implementation and resolution of a model previously specified in a script lab and variants proposed. Analysis of results
  • Autonomous learning: Reading and studying the material presented in the theory sessions. Resolution individual fitness modeling. Solving the models using algebraic modeling languages. Preparation and reading material for the lab sessions
Objectives: 1 9
Contents:
Theory
2h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
8h

Attitude and motivation toward work. A2

Analysis of the changes proposed by the teacher at Work Course and proposed changes to be made in a limited time. Discussion with other working groups of the adequacy of the solutions adopted
  • Laboratory: Collaborative learning session
  • Autonomous learning: Analysis of proposed changes in the teacher work year. Preparation prior to the meeting
Objectives: 13
Contents:
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
5h

Assessing motivation and attitude towards work. A2

Delivery of final report to the collaborative session parenentatge
Objectives: 13
Week: 11
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Block 6. Integer Linear Programming Problems

Assimilation of the concepts of branching and quoting. Make iterations of the Branch and Bound algorithm with small problems.
  • Theory: Exhibition programaciói basic properties of linear integer problems and the concept of linear relaxation. Illustration of operation of the branch and bound algorithm.
  • Problems: Resolution of small integer linear programming problems.
  • Autonomous learning: Reading and study material for theory sessions. Preparation exercises for class of problems
Objectives: 8
Contents:
Theory
2h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Attitude and motivation toward work. A3

Oral presentation of course work in a limited time (10min to the working group)
  • Laboratory: Presentation of course work and discussion
  • Autonomous learning: Preparation for the student
Objectives: 13
Contents:
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
5h

Assessing motivation and attitude towards work. A3

Oral presentation
Objectives: 13
Week: 12
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Block 7. Heuristic methods for integer linear programming problems. Metaheuristics

Understand the main principles of construction heuristic solutions. Learn to build algorithms based on metaheuristics described. Simulated annealing method, tabu search, greedy search.
  • Theory: Heuristic methods for plant location problems and traveling salesman. Methods of exchange. Construction of solutions. Christofides heuristics. Method of simulated annealing. Taboo Search, Greedy Search
  • Problems: Resolving cases of small hand, applying the heuristics views.
  • Autonomous learning: Monitoring of the exposed material and preparation of material for the lab sessions
Objectives: 9
Contents:
Theory
4h
Problems
2h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Evaluation of lab 2

Handed a questionnaire completed by the end of the session. This questionnaire will go.
Objectives: 4 8 9 10 11 2
Week: 13
Type: assigment
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Laboratory 1 and 2

Reading the previous questionnaire and preparation of practice. Execution of the exercise and delivery of completed questionnaire
  • Laboratory: Making classroom practice sessions on PCs. Using algebraic modeling language representation. Resolution and analysis solutions Construction of a metaheuristic algorithm based on a procedure seen in class
  • Guided learning: Development of practice under supervision
  • Autonomous learning: Preparation for student
Objectives: 3 5 6 7 8 10
Contents:
Theory
0h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
4h

Block 8. Course work.

Assimilating the different stages of formulation, analysis and testing of an optimization model as part of a system to support decision making. Analysis of performance and computational tools used in the performance of the developed model. Development of skills associated to this subject. Students will make up work groups (2 students)
  • Theory: Sessions support and clarification of concepts needed for the tasks of formulating and solving a case study. Development of generic skills associated with the subject.
  • Laboratory: Sessions for the implementation of the formulations of the models. Development of generic skills of the subject.
  • Autonomous learning: Preparation of material. Study and analysis of a small case study. Study and reinforcing concepts displayed the contents of the course
Objectives: 1 3 4 7 9 10 11
Contents:
Theory
2h
Problems
0h
Laboratory
6h
Guided learning
0h
Autonomous learning
8h

Assessment Course work

A model will be proposed to the students for its developement along the course. Specific lab sessions will be used for monitoring this activity. Specific objectives: - Development of a model based on optimization problems as part of a system to aid decision making. - Analyze the performance of the computational model developed for use in the environment of proper systems to aid decision making
Objectives: 1 3 4
Week: 14
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Part 2

It consists of test problems for blocks 5,6 and 7 of the subject and the corresponding block 8 related blocks 5.6 and 7.
Objectives: 8 9 10 11
Week: 14
Type: theory exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Final Exam

It covers all blocs of the subject
Objectives: 2 1 3 5 6 4 7 8 9 10 11
Week: 15 (Outside class hours)
Type: theory exam
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
6h

Teaching methodology

Learning is done following the methodology of cases, from problems in the area of ​​Operations Research. From these problems will develop the knowledge necessary formal theory classes, classroom and exhibition, and its application in laboratory classes, so that will strengthen the assimilation of various concepts. Used software available on the UPC (AMPL,OPL/Studio Excel).

Evaluation methodology

See Addenda for the academic year 2020-21

NT = Mark for Theory
NL = Mark for Laboratory sessions. This mark will consist of the marks obtained in the two lab exercises, each one of them weighting 50% of NL
NTC = Mark for Laboratory Labour Course
NC = Mark for skills

N= 0.45*NT + 0.2*NL + 0.25 * NTC + 0.1*NC

If 0.5* NExP1 + 0.5*NExP2 > = 5 then no need to submit the final exam

NT = Max (NExF, 0.5 * NExP1 + 0.5N*ExP2)

NExF Note of the final exam,

NExP1, NExP2 Notes of partial exams 1 and 2.

Mark NC will depend on the degree reached at the skills assigned to the subject and the mark will be an average of the marks obtained at each of the skills. (There are two skills, C1, C2. Then
mark NC will obtained as NC = 0.5*NC1 + 0.5*NC2

For a given skill i there is the following matching between the level obtained at that skill and the mark NC1, NC2 involved in the final mark

A level A is equivalent to a mark NC1 (or NC2) that will be between 8,5 and 10
A level B is equivalent to a mark NC1 (or NC2) that will be between 6.5 and <8,5
A level C is equivalent to a mark NC1 (or NC2) that will be between 5 and < 6.5
A level D is equivalent to a mark NC1 (or NC2) that will be between 0 and <5

Marks for skills are obtained through activities carried out in bloc 8 (Laboratory Labour Course) and Lab sessions.

Marks NC1, NC2 for skills assigned to this subject will obey to the following expression:

NCi = 0.25 * NTC + 0.10*NL + Specific Activities for the skill; i=1,2

Bibliography

Basic:

Complementary:

Web links

Previous capacities

Students must have sufficient knowledge of algebra to assimilate the methods exposed algoritmcs should also be able to read English at a technical level