Programs

Concentration (CX) Program - Decision Analytics


Introduction

The interdisciplinary field of Decision Analytics (DA) seeks to understand and improve the judgment and decision making of individuals, groups, and organizations. Decision Analytics is grounded in theories and methods drawn from mathematics, probability and statistics, operations research, optimization, and artificial intelligence-based tools such as machine learning. The knowledge of this multidisciplinary area can be applied almost everywhere including government, manufacturing, design, health care, transportation, city planning, and business. The Systems Engineering department proposes a concentration in DA with the aim to equip students with the knowledge and skills for scientific decision making. The concentration consists of four courses taught by the systems Engineering Department, Mathematics and Information and Computer Sciences. The courses are Decision Making, Intelligent Decision Support Systems, Applied Game Theory and Cases in Decision Analytics.

Offered to:

ISE, CIE, CS, SWE, EE, ME, CHE

Eligibility:

  • ISE Students who has finished all their major junior level courses are eligible to enroll in this concentration:
  • A student of other majors can enroll in this concentration if he is able to fulfil the prerequisite requirements of the concentration which are: (ISE 205 0r EE315 or STAT319) and (ISE 303 or STAT 361)
  • For the concentration to be registered in the students’ records, the students should finish all the concentration courses successfully.

Objectives:

  • Objective 1: Present to the students the process of decision analysis and decision making.
  • Objective 2: Cover decision making under certainty, risk, and uncertainty.
  • Objective 3: Equip students with approaches and methods for formulating and solving complex decision problems.
  • Objective 4: Expose students to various applications of decision analytics.

Learning Outcomes:

  • SLO1: state the difference between uncertainty and risk.
  • SLO2: describe methods for solving decision situations/problems.
  • SLO3: formulate decision situations/problems.
  • SLO4: design intelligent decision support system.
  • SLO5: develop optimization models to address decision situations/problems.
  • SLO6: assess the capabilities of decision support systems.
  • SLO7: lead teams and communicate effectively in writing and orally.

ISE 447 – Decision Making

Basic decision-making model under certainty with multiple criteria as well as under pure Uncertainty, Risk, Risk with information and conflict with single criteria. Structuring decision problems as well as applications in systems engineering are emphasized through problem sets, case studies and term project.

Pre-requisites: ISE 205

ICS 487 – Intelligent Decision Support Systems

Introduction and need for Decision Support Systems (DSS). Nature of Decision problems and the elements of the decision process with examples. Essential elements of decision-making. Evolution of DSS: management information systems, decision support systems (DSS), intelligent decision support systems (IDSS). IDSS architecture, data collection, data analysis & exploration, design and implementation. IDSS techniques: case-based reasoning, decision trees, knowledge representation. Case studies and projects: e-commerce, knowledge management, recommender systems and action.

Prerequisites: ISE 205 or STAT 319 or EE 315

ISE 455 – Applied Models for Optimal Decisions

Review of decision making under uncertainty and risk. Linear and linear programming for deterministic decision making. Chance constrained and two stage stochastic programming. Risk analysis, robust and queuing theory. New concepts will be presented through cases studies using inductive teaching. Students must work on a project that demonstrates their understanding of decision making under uncertainty applied to a real case.

Pre-requisites: ISE 447

MATH 407 – Applied Game Theory

Formulation of strategic and cooperative games in energy industry, such as oil & gas and electric power companies, and portfolio analysis. Dominant, optimal strategies and Nash equilibrium. Coalition formation in cooperative games is used to represent OPEC to investigate their formation. Games in characteristic function format. Concepts of solutions for games. Pareto optimal solutions, core, and Shapely value. Other cases for allocation of resources, design, supply chain will be modelled in the context of game theory.

Pre-requisites: ISE 303 OR STAT 361