IE 411

KING ABDULAZIZ UNIVERSITY

Department of Industrial Engineering

IE 411: Operations Research II

Dr. Ammar Y. Alqahtani

Covers analytical development and solution to probabilistic models in operations research. Topics include integer and nonlinear optimization, dynamic programming, Markov chains, queueing theory, inventory models, and metaheuristics.

Course Prerequisites

IE 311 and IE 332; engineering students only.

Textbooks

• Barry Render, Ralph M. Stair Jr, Michael E. Henna, and Trevor S. Hale, “Quantitative Analysis for Management”, 13th Edition, Pearson Education, Inc., 2017. ISBN: 978-0134543161 [B].
• Hillier and Lieberman, “Introduction to Operations Research”, 10th Edition, McGraw Hill, Inc., 2015. ISBN: 978-1259162985. [H].
• Singiresu S. Rao, “Engineering Optimization: Theory and Practice”, 4th Edition, , John Wiley and Sons, 2009. ISBN: 978-0470183526 [S].
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Course Topics:

• Nonlinear Optimization: Unconstrained Optimization (Single Variable Functions), Unconstrained Optimization (Multiple Variables Functions and Search Techniques), Constrained Optimization (Lagrange Multipliers), Convex Programming (Kuhn-Tucker Conditions), Quadratic Programming, Beale's Algorithm and Geometric Programming.
• Integer Programming: Cutting Plane Algorithms.
• Dynamic Programming: Characteristics of Dynamic Programming Problems (deterministic & probabilistic)
• Markov Chains: Applications, Chapman-Kolmogorov equations, classification of states, first passage times.
• Markov Chains: Steady state probabilities, long-run expected values, absorbing chains.
• Queueing Theory: Basic structure, concepts and applications, role of the exponential distribution, birth-and-death process, queueing models based on the birth-and-death process.
• Queueing Theory: Queueing models involving non-exponential distributions.
• Inventory Theory: Applications and components of inventory models, deterministic models – continuous review, periodic review.
• Inventory Theory: Stochastic models – continuous review, single period, larger inventory systems in practice.
• Metaheuristics: Complexity theory, deterministic and probabilistic heuristics including local search, ant colony optimization, genetic algorithms, tabu search, and simulated annealing.
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  Course Objectives:

  At the completion of the course, students should be able to:

• Acquire, identify, formulate, solve, and analyze integer and nonlinear programming problems, and interpret their results.
• Develop Markov chain transition matrices and analyze the state of a system in the transient and steady state.
• Identify, formulate and solve an appropriate queueing model that applies in a given queueing system application.
• Acquire, identify, formulate, solve, and analyze dynamic programming problems and interpret the findings
• Design or improve a queueing system in terms of server and/or system capacity in order to obtain a given level of customer service.
• Design an inventory policy under deterministic or probabilistic demand.
• Formulate the initiation, iterative steps, and/or stopping criteria of various metaheuristic methodologies.
• Recognize probabilistic processes and probabilistic modeling applications, present a real-world application of any given course topic.
• Apply computer software to solve Operations Research -II problems of different techniques and interpret the results and findings.