Undergraduate courses

Programming Methodology (DPEM 101) (Instructor: P. Fafalios)

Introduction to software technology and programming. Learning the C programming language up to the level of manipulating arrays and structures: variables, data types, constants, operators, user input and output, selection statements, iteration statements, functions, arrays, strings, structures.

Methodology of Operations Research (DPEM 102) (Instructor: E. Siskos)

Methodological framework of operations research. Introduction to graph theory with applications to project management. Inventory control. Wilson’s model and extensions. Introduction to linear programming. Multiple criteria decision making, Case studies.

Electronic Business (PEM 230) (Instructor: P. Fafalios)

Introduction to e-Business and e-Commerce. E-commerce business models. Digital marketing, advertising and web analytics. Internet technology. Developing an e-commerce presence. Web Technologies: HTML, CSS, Bootstrap. The online security environment (cybersecurity). Ethical issues, privacy, intellectual property, and governance.

Game Theory (DPEM 407) (Instructor: Y. Marinakis)

Introduction, Games with two players. Zero-sum games. Pure and mixed strategies. Matrix and bi-matrix games. Equilibria and saddle points. Minmax theorem. Solution of matrix games using linear programming. Solution of Bi-matrix Games using nonlinear programming. Nash equilibriums and Pareto points. Hierarchical games.  Stackelberg equilibria and disequilibria. Bi-level programming. Application to microeconomics: Cournot duopoly. Application to traffic planning: traffic assignment problem.

Laboratory: For a better understanding of the course, students are invited to carry out three laboratory exercises in C or Matlab programming language, implementing algorithms for solving Game Theory problems.

Combinatorial Optimization (DPEM 426) (Instructor: Y. Marinakis)

Mathematical models and applications of combinatorial optimization. Differences between linear and integer programming. Graphs and networks. Data structures for graphs and networks. Graph search. Shortest paths and discrete dynamic programming. Minimal spanning trees and greedy algorithms. Flow problems. Problem and algorithm complexity. Linear and Lagrangian relaxation. The branch-and-bound method. Local search. Heuristicand meta-heuristic algorithms. Approximation algorithms.

Laboratory: For a better understanding of the course, students are invited to carry out three lab exercises in C or Matlab programming language, implementing algorithms for solving Combinatorial Optimization problems.

Decision Support Systems (DPEM 324) (Instructor: E. Siskos)

Introduction to Information Systems and Information Technology. Decision Theory. Multicriteria Decision Analysis. Group Decision Making. Decision Support Systems (DSS). DSS Architectures. Human-Computer Interaction. Data Base Management Systems (DBMS). Structured modeling and model-based management systems. DSS Evaluation. Intelligent Methods for Decision Support. Intelligent and multicriteria DSS. Group and Negotiation DSS. Executive information systems. Executive support systems. Data warehouses & on-line analytical processing. Distributed DSSs & web-based DSSs. Spatial DSSs. DSS Applications in: management, marketing, industry, production, finance, health, environment etc.

Laboratory: Applications using DSS. DSS’s Development. Case Studies.

Quality Control (DPEM 405)

Introduction to quality and quality improvement methods. Concept and techniques for quality control. Basic categories of statistical quality control. Introduction to statistics. Acceptance sampling. Single, double and multiple sampling plans. Sequential sampling plans. Other acceptance sampling techniques. Introduction to statistical process control and control charts. Control charts for variables and attributes. Other statistical process quality-control techniques.

Small  and medium-sized enterprises (DPEM 433) (Instructors: K. Zopounidis and L. Krasadaki)

Small and medium-sized enterprises. Organisation and management of SMEs. SME legislation. Business initiatives. Creation of new businesses. Preparation of business plans. Project and resource management. Models for SME development. Accounting and costing of SMEs. SME financing. SME sustainability. Leadership. Innovation and SMEs. Innovative Ideas. Creativity, competition, market segmentation. New product design and development, sales promotion, SME evaluation, investment evaluation, strategy development and evaluation, financial analysis of investments.
 

Laboratory: use of a specific software platform for business games and development of marketing plans through market simulation.

Enterprise Resource Planning Systems (DPEM 435)

Introduction to Information Systems, Enterprise resource planning systems (ERPs), Customer Relationship Management Systems (CRMs), System architectures, components, modules and technical infrastructure of ERPs, System’s analysis and design, Business processes in ERPs, Business Process Reengineering, Specific ERP components (Manufacturing, Financials, Supply chain management, Warehouse Management, Distribution, Marketing, Sales, Human Resources Management, Logistics), Operations that ERP support, pros and cons of using ERPs, E-commerce and ERP, Business Intelligence and ERP, ERP and Data Warehouses – OLAP, Success factors of ERPs, Feasibility study of getting an ERP, Evaluating, selecting, installing, configuring and customizing an ERP, Production Planning through ERPs, Material requirements and resource planning (MRP I & MRP II), Demonstration of the MBS Navision ERP, MBS Navision CRM. Special issues: Enterprise Application Integration, Interoperability, Service-Oriented Computing, Web Services. 

Laboratory: Case studies/applications with SoftOne

Total Quality Management (DPEM 408)

Introduction to quality (definitions, history and importance, dimensions). Principles of Total Quality Management (TQM). TQM as a new culture. Quality management philosophies (Deming, Juran, Crosby, Ishikawa, Taguchi, Feigenbaum). Customer-satisfaction and customer-relationship management. Quality awards (Deming, EFQM, Malcolm Baldridge). Benchmarking. Tools for TQM (quality improvement, SPC, QFD, Taguchi techniques, etc.).Quality standards and quality assurance systems. Cost of quality.

Business Intelligence, Analytics and Big Data Analysis (PEM 518) (Instructor: P. Fafalios)

Introduction to Business Intelligence and Business Analytics. Data Science, Big Data, Big Data analytics technologies. Decision making under uncertainty and risk. Data and knowledge management. Data preprocessing. Data Warehouses and Online Analytical Processing (OLAP) systems. Data Mining and Machine Learning. Classification: decision trees, performance evaluation. Clustering: hierarchical algorithms, partitioning algorithms, density-based algorithms, measurement of clustering quality.

Design and Optimization in Supply Chain Management (DPEM 514) (Instructor: Y. Marinakis)

Role of supply chain management. Planning demand and supply in a supply chain. Applications and mathematical modeling. Algorithmic complexity. Traveling salesman problem, bin packing problem. Transportation and distribution of products in supply chain. Network design problem. Distribution channels. Route selection. Fleet-size problems. Vehicle-routing problem. Variants of the vehicle-routing problem (time windows, multicommodity, dial-a-ride, pickup and delivery prob- lems). Vehicle scheduling problem. Ship routing problem. Inventory routing problem: single-period inventory routing problem, multi-period inventory routing problem, infinite horizon inventory routing problem. Location problems. Covering problems. P-center and P-median problems. Capacitated and uncapacitated facility problems. Location routing problem. Integrated logistics. E-Supply chain management. Case studies (modeling, development and solution methodologies). 

Laboratory: For a better understanding of the course, students are invited to carry out a lab exercise in C or Matlab programming language, solving a real supply chain design and optimization problem.