Courses

ENGL 500

The following objectives will be met through extensive reading, writing and discussion both in and out of class.Build a solid background in academic discourse, both written and spoken. Improve intensive and extensive critical reading skills. Foster critical and creative thinking. Build fundamental academic writing skills including summary, paraphrase, analysis, synthesis. Master cohesiveness as well as proper academic citation when incorporating the work of others.

GSSE - INDR
Undergraduate Programs
Credit:0

INDR 508

Topics on distribution fitting and generating random numbers and random variates will be covered as well as the statistical analysis of simulation output including some well-known analysis methods and variance reduction techniques. Recent developments in the area will also be discussed.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 503 or consent of the instructor

INDR 513

Brief review of basic processes like Poisson, Markov and renewal processes; Markov renewal processes and theory, regenerative and semi-regenerative processes; random walk, Wiener process and Brownian motion; martingales; stochastic differential equations and integrals; applications in queueing, inventory, reliability and financial systems.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 503 or consent of the instructor

INDR 540

Analysis of selected models, algorithms, and applications from location theory. Study of deterministic and stochastic problems in continuous and discrete space. Capacitated and uncapacitated facility location. Covering problems. Center and median problems. The quadratic assignment problem and facility layout. Location and routing. Transportation of hazardous materials. Flow-interception. Voting and competitive location problems.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: (INDR. 252 and INDR. 262) or consent of the instructor

INDR 562

Formulation of integer and combinatorial optimization problems. Optimality conditions and relaxation. Polyhedral theory and integer polyhedra. Computational complexity. The theory of valid inequality, strong formulations. Duality and relaxation of integer programming problems. General and special purpose algorithms including branch and bound, decomposition and cutting-plane algorithms.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 501 or consent of the instructor

INDR 503

The basic theory of the Poisson process, renewal processes, Markov chains in discrete and continuous time, as well as Brownian motion and random walks are developed. Applications of these stochastic processes are emphasized by examples, which are drawn from inventory and queueing theory, reliability and replacement theory, finance, population dynamics and other biological models.

GSSE - INDR
Undergraduate Programs
Credit:3

INDR 511

Convexity basics; optimality conditions for unconstrained problems; Gradient methods; quasi-Newton methods, conjugate gradient methods; constrained problems and KKT conditions; feasible direction methods; Lagrangian duality; Lagrangian relaxation in integer programming; selected topics in global optimization.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 501 or consent of the instructor

INDR 530

Tools, techniques, and skills needed to analyze decision-making problems characterized by uncertainty, risk, and conflicting objectives. Methods for structuring and modeling decision problems and applications to problems in a variety of managerial decision-making contexts. Structuring decision problems: Decision trees, model building, solution methods and sensitivity analysis; Bayes' rule, the value of information and using decision analysis software. Uncertainty and its measurement: Probability assessment. Utility Theory: Risk attitudes, single- and multiattribute utility theory, and risk management. Decision making with multiple objectives.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: ENGR. 200 or consent of the instructor

INDR 560

Methods for the solution of complex real world problems modeled as large-scale linear, nonlinear and stochastic programming, network optimization and discrete optimization problems. Solution methods include Decomposition Methods: Benders's, Dantzig-Wolfe, Lagrangian Methods; Meta-heuristics: Local search, simulated annealing, tabu search, genetic algorithms; Constraint Programming. Applications in transportation and logistics planning, pattern classification and image processing, data mining, design of structures, scheduling in large systems, supply-chain management, financial engineering, and telecommunications systems planning.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 501 or consent of the instructor

INDR 566

Introduction to scheduling: examples of scheduling problems, role of scheduling, terminology, concepts, classifications; solution methods: enumerative methods, heuristic and approximation algorithms; single machine completion time, lateness and tardiness models; single machine sequence dependent setup models; parallel machine models; flow-shop models; flexible flow-shop models; job-shop models; shifting bottleneck heuristic; open-shop models; models in computer systems; survey of other scheduling problems; advanced concepts.

GSSE - INDR
Undergraduate Programs
Credit:3

INDR 501

Convex analysis, optimality conditions, linear programming model formulation, simplex method, duality, dual simplex method, sensitivity analysis; assignment, transportation, and transshipment problems.

GSSE - INDR
Undergraduate Programs
Credit:3

INDR 510

Review of descriptive statistics, importants populations statistics and their distributions. Point estimation, estimations methods and minimum-variance unbiased estimators. Testing hypothesis, Neyman-Pearson lemma and likelihood ratio tests. Estimation and testing in linear regression modes. Analysis of variance models. Nonparametric statistics methods. Bayesian testing and analysis.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 252 or consent of the instructor

INDR 520

Network flow models and optimization problems. Algorithms and applications. Minimum spanning tree problem. Shortest path problems. Maximum flow problems, minimum cuts in undirected graphs and cut-trees. The minimum cost network flow problem. Matching problems. Generalized flows. Multicommodity flows and solution by Lagrangean relaxation, column generation and Dantzig-Wolfe decomposition. Network design problems including the Steiner tree problem and the multicommodity capacitated network design problem; their formulations, branch-and-cut approaches and approximation algorithms.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: INDR. 262 or consent of the instructor

INDR 550

Topics will be announced when offered.

GSSE - INDR
Undergraduate Programs
Credit:3

INDR 564

Theory and practice of dynamic programming, sequential decision making over time; the optimal value function and Bellman's functional equation for finite and infinite horizon problems; Introduction of solution techniques: policy iteration, value iteration, and linear programming; General stochastic formulations, Markov decision processes; application of dynamic programming to network flow, resource allocation, inventory control, equipment replacement, scheduling and queueing control.

GSSE - INDR
Undergraduate Programs
Credit:3
Pre-requisite: (INDR. 501 and INDR. 503) or consent of the instructor