Decision Support and Operations Research

Concepts and techniques to construct and implement an effective computer-based decision support systems (DSS) including problem solving - decision-making process, types of DSS (Data/Model/ Knowledge DSS), model building and its languages, model selection/integration/execution, types of computer based information systems - system development life cycle - systems analysis and integrated computer-based DSS design methodologies, real life case studies of integrated DSS - foundations of futures studies - future studies methodologies, such as; Scenario approach, environmental scanning, and futures wheel - strategic foresight - software tools and integrated DSS software packages will be stressed used throughout the course.

main components of the field of computational intelligence (CI) such as evolutionary and fuzzy computation - emphasis will be made on the application of CI techniques to real life optimization problems - related heuristic techniques such as Ant Algorithms, Genetic Algorithms, Tabu search, Simulated Annealing are covered the advantages and limitations as well as the guidelines for selecting the most efficient approach for various types of problems implementation of CI techniques for various problems will be stressed throughout the course.

Formulation of problems into linear and integer programs, the graphical solution approach, the simplex method, two-phase method, dual simplex algorithm, revised simplex, duality in linear programming, sensitivity analysis, post-optimal analysis, parametric linear programming, decomposition technique, interior-point method, integer programming algorithms - economic interpretation of solutions - real life applications such as the traveling salesman, transportation and assignment problems real life case studies and state of art solver software will be used throughout the course.

Definitions and properties of convex functions and sets, optimality conditions of unconstrained and constrained nonlinear programming problems - line search methods without using derivatives, line search methods using derivatives, multidimensional search methods without using derivatives - multidimensional search methods using derivatives - penalty and barrier functions methods - and methods of feasible directions real life case studies and state of art solver software will be used throughout the course.

Dynamic programming, stochastic models, queuing theory - multistage decision-making - recursive equations - forward and backward recursion - state variables in dynamic programming -Markov chains and analysis, Markov decision process -random walk - Poisson process - truncated Poisson process, pure birth process - pure death process and birth and death process queuing systems covering single and multi-stage queuing models, queuing network models real life case studies and state of art solver software will be used throughout the course.

Fundamentals of simulation as a modeling technique - emphasis is on the value of simulation as an experimental tool to support problem solving and decision making concepts of building a complete simulation study through stochastic discrete event simulation procedures (event, activity, and process based models). Input data analysis, different time advance mechanisms, validation and verification, output data analysis - using output to support decision making are studied through a number of mathematical, industrial and business applications. statistical methods in simulation experiments implementing different simulation models using computer programming is stressed throughout the course -introduction to software tools for simulation is given concepts of random number generation and sampling are discussed and used.

Theoretical and practical aspects related to building data driven models - linear models for regression and classification - kernel machines, neural networks, emphasis will be placed on practical computational complexity and programming aspects of such models - exposition will be made of the problem of over fitting, the bias-variance dilemma - model regularization and model validation.

Introduction to a variety of areas in operations and supply chain management, various operations management decision including service and product design, quality management, process and capacity, facility location, layout design, scheduling, and inventory management real life case studies and state of art software will be used throughout the course.

Knowledge Representations: Predicate Calculus - Structured Representations, Network Representations. State Space Search: simple search - heuristic search - reasoning with uncertain or incomplete knowledge constraints satisfaction problem.

Strategy games, rationality, dominance relations - bargaining theory, non-cooperative games, cooperative games, games with incomplete information, repeated games and evolutionary stable strategies, case studies will be used to illustrate the application of game theory to real world problems along with implementing game-theoretic settings.

Basic concepts of decision making under certainty, risk and uncertainty, decision tables - decision trees - sequential decision-making - opportunity loss, one-time decisions - expected value of information - conditional probability - decision analysis, multiple comparison and multiple ranking methods are explained -approaches to the management of risk and concepts as such as uncertainty and variability, Quantifying uncertainty, Probability assessment methods and risk attitudes real life case studies and state of art software will be used throughout the course.

Concepts of multi-objective programming, vector optimization problems techniques, utility theory, goal programming methods, interactive multi-objective methods and evolutionary algorithms for multi-objective programming - real life case studies and state of art solver software will be used throughout the course.

Introduction to network problems in operations research, computer science, electrical engineering and systems engineering concepts of graph theory, network representations, network transformations, shortest paths algorithms, maximum flows algorithms, minimum cost flows algorithms, generalized network and combinatorial-based network models real life case studies and algorithms implementation will be stressed throughout the course.

Design, build, verify, and test predictive data models to make data-driven decisions concepts, methods, and applications of pattern discovery in data mining, concepts of cluster analysis, clustering methodologies, algorithms, and applications, partitioning methods, hierarchical methods, and density-based methods - steps of data preparation, analysis, learning and modeling, and identifying the predictive/descriptive model that produces the best results.

Bayesian data analysis, inference and networks, a disciplined approach to hypothesis formulation and test and to cultivate and appreciation of reasoning and decision making under uncertainty.

The effective application of popular data analytics tools - practical approaches to data cleaning, parsing, and extraction for various sources solving predictive analytics problems using models such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks time-series analysis forecasting process: goal definition, data visualization, modeling, performance, evaluation, and model deployment - interpretation of results.

Key concepts, principles, and tools of strategic management - overview of the strategic decision making process, formulating business vision and mission, external assessment - internal assessment - strategies analysis and choice - real life case studies, applications and use of state of art software packages are stressed throughout the course.

Introduction to systems thinking and the system dynamics approach to policy analysis -applications to business management and public policy - causal-loop and 'stock and flow' models of business growth, technology adoption, marketing, and various other domains. use of role-based simulation games to explain key principles of systems. use of simulation software to model and solve problems - case studies in dynamic policy analysis.

Concepts of agent-based modeling (ABM) and complex systems -agents, their internal structure, their interactions, simulation environment -model validation. complex adaptive systems; feedback loops, externalities, nonlinearity, chaos theory, self-organization theory, emergent properties resilient and robustness, schemata and cellular automata real life case studies, applications and ABM implementations are stressed throughout the course.

concepts and fundamentals of crisis management identifying, preventing, and controlling crisis situations, identifying potential risks or situations that may precipitate a crisis developing, assessing and determining consequences of contingency plans real life case studies, applications and use of state of art software packages are stressed throughout the course.

A complete and comprehensive picture of the management of service industries -understanding of the nature and importance of the service sector in the international economy effective decision making in the management of a service organization -design and management of systems for services, contextualization of the service sector and its role in the economy. service marketing - service quality, service portfolio management, service systems, capacity management.

Formulating, estimating, and evaluating the expected economic outcomes of alternatives designed to accomplish a defined purpose mathematical techniques simplify the economic evaluation of alternatives determining the economic feasibility of projects; alternatives, especially the time value of money, interest rates, depreciation, replacement, economic life, present value, rate of return, and payback period financing supply and demand - private and social cost estimations, secondary and intangible benefits and costs, benefit-cost models - economic risk analysis - economic optimization.

Introduction to the context and addresses the principles and the basic techniques of project management various project management techniques related to project definition, project scheduling, managing projects under resource limitation, and managing project risks -integrated planning, scheduling, and control systems for planning the scope of a project real life case studies and state of art software will be used throughout the course.

This course aims at introducing students to novel topics in decision support that need to be identified in a responsive manner as technology evolve and develop.

This course aims at introducing students to novel topics in decision support that need to be identified in a responsive manner as technology evolve and develop.

Particular emphasis is placed on the importance of practical experience and all teaching involves industry standard hardware, software, methods and techniques. Students asked to complete training on chosen area of specialization to be familiar with the industry.

This course will continue for two semesters. In the first semester; a group of students will select one of the projects proposed by the department and analyze the underlying problem. In the second semester; the design and implementation of the project will be conducted.