Model construction and modelling issues. Linear programming (LP) formulation, Simplex method: two-phase algorithm, dual simplex method, network simplex method. Duality, sensitivity analysis, economic interpretation and applications. Integer programming (IP), modelling techniques using zero-one variables. Branch and bound algorithm. Nonlinear programming formulation. Nonlinear programming optimality conditions. Computer packages and case studies. Prerequisite: Graduate standing. (Not counted for credit for ISE graduates).

Axioms of probability, joint conditional probability, independence, continuous, discrete and mixed random variables, functions of random variables, expectations and conditional expectations, variances and co-variances, correlation, multi-dimensional random variables. Markov chains, Poisson processes, Applications in inventory control, quality, reliability and renewal theory. Prerequisite: Graduate standing.

Review of linear programming, revised simplex method, product form of the inverse, duality, dual simplex method, primal dual simplex method, sensitivity analysis, parametric programming, bounded variable linear programs, decomposition principle, classical networks, shortest path problem, maximal flow problem, Dantzig-Wolfe multi-commodity networks. Additional topics may be selected from complementarity, fractional programming and computational efficiency of linear programming algorithms. Case studies. Prerequisite: Graduate Standing and consent of instructor

The course emphasizes the basic concepts of data analysis related to unsupervised and supervised learning. Specifically, in unsupervised learning the focus is on clustering (partition, density based and hierarchical), correlation analysis, and dimension reduction. Optimization methods in regression (linear and regularized), classification (linear, kernel, trees and boosting), handling data uncertainty and robust optimization, model selection, and model validation (cross validation and bootstrapping) will also be considered. The topics will be covered w.r.t Operations Research viewpoint. Prerequisite: Graduate Standing.

This course introduces supply Chain Management (SCM) concepts and issues. The major content of the course is divided into three modules: supply chain integration, supply chain decisions, and supply chain management and control. A variety of instructional tools including lectures, case discussions, and group projects and presentations are employed. Prerequisite: Graduate standing

Role of reliability engineering in industry, reliability measures, and bath-tub reliability curve, and overview of reliability assessment. Component failure behavior and reliability prediction based on failure data analysis. Reliability of systems including serial, parallel, k-out-of-n, complex systems, standby systems, and their combination. Improvement of system reliability and reliability growth. Availability of repairable systems. Accelerated life testing. Practical applications and case studies from aerospace, automotive, and manufacturing industries. Prerequisite: Graduate Standing Note: This course cannot be taken for credit with ISE 505

Review of mathematical models for maintenance, capacity planning models, planning and scheduling models, inspection models, preventive maintenance models, component replacement models, Block replacement models, models for spare parts provisioning, models for condition based models including proportional hazard models. Integrated models that include maintenance, production and quality. Prerequisite: ISE 503 and ISE 502.

Analysis of production and inventory systems, forecasting, single and multi-period deterministic inventory models, stochastic inventory models, deterministic and stochastic production planning, Multistage and dynamic production planning models, MRP systems, Pull, Push and Just-in-Time Systems. Prerequisites: Graduate Standing.

Reliability engineering applications, reliability measures, static and dynamic reliability models. Bath-tub curve, reliability; series, parallel and r-out-of-n configuration. Reliability data analysis using the exponential, Weibull and lognormal distributions, catastrophic failure models: hazard rate models. System reliability: approximation methods and reliability bounds. Accelerated life testing. Case studies and applications. Prerequisite: ISE 502

Maintenance & Facility Maintenance Management Systems, their philosophies, trends and prospective. Maintenance organization, maintenance strategy and concepts, including total productive maintenance (TPM) and Reliability centered maintenance (RCM), forecasting maintenance load, maintenance jobs measurement and standards, quality of maintenance jobs, maintenance productivity and auditing, benchmarking, maintenance performance measures (KPIs), CMMS, life cycle costing and maintenance resources control. Case studies. Prerequisite: Graduate Standing

Condition monitoring technologies in predictive maintenance, in depth study of the use of vibration analysis, acoustic emission, infrared thermograph, leak detection, oil analysis, and emission monitoring. Devices and products for condition monitoring. Data acquisition and use of predictive maintenance software to analyze and interpret the results of condition monitoring, base line database development. Case studies and computer applications Prerequisite: Graduate Standing.

This course adopts a modeling approach to supply chains problems. Topics covered include supply chain design, multi-location inventory-distribution models, transportation and vehicle routing, supply chain distribution network design, integrated production, inventory and distribution problem, and reverse logistics. The key insights provided by such system-wide models will be illustrated through the use of spreadsheets and software packages such as CPLEX, presentations of research papers for emerging supply chain optimization problems. Pre-requisites: ISE 503 and ISE 502

Convexity and optimality conditions. Review of simplex, duality, and Lagrange duality. Interior point methods. The decomposition principle. Dantzig-Wolfe decomposition, Benders decomposition. Application of the decomposition principle to solve large scale linear programs. Case studies. Pre-requisite: ISE 503 or equivalent

Pre-Requisites: ISE503

Basic discrete-event simulation modeling, queuing models, simulation languages, review of basic probability and statistics, random-number generators, generating random variables, output data analysis, validation of simulation models. Simulation language, simulation models, real case studies.

Mathematical programming models that include linear, nonlinear, and chance constrained programming and their application in various areas of maintenance.: Maintenance capacity planning models, maintenance resources and shared resources and services for large organizations, component replacement models, Block replacement models, models for spare parts provisioning, models for condition-based models including proportional hazard models

Transportation and distribution systems in supply chains. Modes of transportations. Technology for logistics such as bar coding and RFID. Logistics and distribution key processes. Economics of logistics and distribution. Organization of logistics. In house versus outsourcing of logistics. Third and fourth party logistics. IT application in various logistics and distribution functions. Logistics performance evaluation and benchmarking.

Pre-Requisites: ISE505

Topics cover Overview of Maintenance 4.0, review programming with Python, types of data preprocessing and data feature extractions methods, including standardization and binary and One- Hot encoding. Methods of data analysis include regression, correlation, association rules, clustering, and classification algorithms. Case studies for data analytics in reliability and maintenance such as prediction of remaining useful life, maintenance planning and scheduling, reliability growth analysis workflow, and improving maintenance operations with data analytics. Prerequisite: Graduate Standing

Application of mathematical programming to the facility location, and layout. Location and layout problems in continuous and discrete spaces, location allocation problems.

Formulation of engineering problems as nonlinear programs; Optimality conditions for nonlinear programs; Algorithms for unconstrained optimization; algorithms for constrained non-linear program; methods of feasible directions (Sequential unconstrained minimization techniques), comparison of algorithms for nonlinear programs. Case Studies. Prerequisite: Graduate Standing and consent of instructor

Fundamental concepts of mathematical and simulation models; efficient generation of random variants, construction of discrete event simulation models, discussion of available computer languages, variance reduction techniques, Jacknifying and classical methods, output analysis. Prerequisite: Graduate standing

The course covers the nature, scope, and importance of forecasting, with techniques for forecasting and time series analysis. Topics include regression, moving averages, exponential smoothing, correlation and least square technique, analysis of forecast errors, Box-Jenkins models and Bayesian methods in forecasting. The design of forecasting systems will be emphasized with application oriented examples.

Modeling with graphs and networks, data structures for network and graphs, shortest path algorithms, properties of the matrix, label setting and label correcting algorithms, spanning tree algorithms, maximum flow algorithms, maximum flow minimum cut theorem, algorithms for the assignment, semi-assignment and the transportation problems, minimum-cost flow algorithms, the simplex method on a graph, out-of-kilter algorithm, embedded networks, constrained network and generalized network, multi-commodity network. Modeling with network includes cases from production, facility location, distribution and inventory and human resource planning. Prerequisite: ISE 503 or equivalent

Pre-Requisites: ISE503

Graph theory with a data mining focus adopting an optimization perspective. Descriptive Analysis of Networks Optimization models used in graph mining. Similarity Metrics, Clustering and Classification, Community Detection, Validation Techniques, Mathematical programming. Summarizing graphs, graph partition formulations, k-club problem, cliques and sub-groups. Software applications and use. Case studies. Course project. Prerequisite: Graduate Standing

Structuring decision problems: single criterion versus multiple criteria, certainty versus risk and uncertainty versus conflict, criteria and attributes, payoffs and losses. Utility function for decision making. Decision making with single and multiple criteria under certainty: selected discrete MCDM models. Decision making under risk: decision trees, single and multiple stages. Value of information. Decision making under uncertainty. Decision making under conflict: game theory. Decision support systems. Case studies. Prerequisites: Graduate standing.

Intelligent decision support systems (IDSSs). Introduction: decision theory, modeling of decision process. AI techniques to build IDSSs: knowledge representation, decision trees, case-based reasoning, case similarity, case retrieval, case retrieval with indexes, case adaptation, planning and plan adaptation. Integrating Digital Twins into IDSSs. Case studies and projects related to process control. Prerequisite: Graduate Standing

Maintenance Strategy, Organizing the maintenance structure, Maintenance management techniques, Designing maintenance organization, maintenance processes, planning and scheduling, quality assurance in maintenance systems, maintenance management information systems, measuring and benchmarking maintenance performance, auditing and improving maintenance systems. Case studies Prerequisite: Graduate standing.

The course adopts the unifying theme of the supply chain to emphasize on the interactions and integration of systems with customers, suppliers, technology, and people. Topics would include quality concepts and theory, dimension and perspectives on quality, supply chain quality standards, strategic quality planning, statistical quality tools such as control charts, process capability, quality design in products and services, quality function deployment in supply chain, six sigma; quality continuous improvement, case studies. The focus is on the practical application of the underlying principles of quality – how to define it, how to measure it and how to continuously improve. Course project.

Tools for reliability analysis such fault trees, failure mode and effect analysis and root cause analysis. Maintainability concepts and measures, system effectiveness and operational readiness, Repairable systems: methods based on renewal theory, system availability. Reliability centered maintenance, Design for maintainability. Practical applications and case studies. Prerequisite: ISE 509

Predictive and analytic prognosis tools for machine monitoring, emerging technologies, that include information and communication technologies (ICT), analysis of big data in maintenance, clustering and forecasting, e-maintenance, integration of maintenance quality, production and scheduling. Prerequisite: ISE503 and ISE502

Pre-Requisites: ISE503 And ISE502

Design of industrial operations with emphasis on the effective uses of the human body. An examination of the problems of establishing time standards and proposed solutions. Learning curves, fatigue allowances, variations of the MTM system, computerized work measurement systems, staffing problems. Term project on industrial methods design.

Statistical methods in the design and analysis of quality control systems: sampling inspection plans, attributes and variables; inspection errors; comparison of sampling plans; control charts design; adaptive quality control; total quality control. Machine and process capability studies; organizing for quality; machine case studies/projects with local industries. Prerequisites: Graduate Standing.

A scientific and engineering approach to experimentation and analysis of data. Single-factor experiments; Latin squares etc., factorial experiments. Missing data analysis; nested factorial design; multifactor design; fractional replications. Case studies. Prerequisite: Graduate standing, (ISE 535 and STAT 530 only one of them can be taken for credit).

Design of man-machine systems utilizing results from various disciplines including anthropometric data and engineering research. Emphasis is placed on making optimal use of human capabilities. Includes consideration of research techniques in human factors engineering. Prerequisite: Graduate Standing.

Supply chain processes, value and costs associated with quality, the dimensions of quality in supply chain, the application of practical statistical process control (SPC) methods to supply chain processes, quality policy, establishment of quality improvement programs, and quality improvement reporting.

A basic methodology course in Occupational Safety and Health. Topics cover a spectrum of contemporary safety and risk management problems drawn from process as well as manufacturing industries. Problems will be handled using methods of Operations Research and Simulation. A project is a part of the course. Prerequisites: Graduate Standing.

Queuing Systems; some important random processes, birth-death queuing systems in equilibrium; Markovian queues in equilibrium. Network of queues. Prerequisite: ISE 502 or Equivalent.

Introduction to stochastic process, stationary, ergodicity, Poisson process, linear models, Markov chains, renewal theory, Markov renewal processes, semi-Markov processes and Applications in queuing and other areas Prerequisite: ISE 502. (Not to be taken for credit with EE 570)

Pre-Requisites: ISE502

Introduction to TAM. TAM concept. Systems approach to TAM. The phases of TAM, scoping, preparation, execution, and start-up. Critical elements of TAM. , scope planning, work load estimation, scheduling of TAM activities. TAM contracts, Critical path analysis, execution, and monitoring. Analysis of TAM life cycle and performance measurements (KPIs). Ethics in maintenance practice, Reporting, learning from TAM experiences and continuous improvement. Life cycle costing of equipment.

Pre-Requisites: ISE510 Or ISE529 Or ARE525

Variety of sequencing and scheduling problems in O.R., job shop and flow shop scheduling, discussion of performance measures, dynamic programming, integer programming, computational complexity and NP-completeness results, discussion of well solved problems, branch and bound methods, variety of heuristic approaches for intractable practical problems, guaranteed accuracy heuristics. Prerequisites: Graduate Standing and Consent of the Instructor.

An overview of the structure and management of logistics and physical distribution system is important for successful supply chain management. Topics include supply chain network design, manufacturing strategies, distribution strategies, warehousing, order processing, packaging, inventory management across echelons and enterprises, material handling, transportation modes and management, and international logistics.

An evaluation of various factors affecting human physical performance in industrial environment. Topics include anthropometry, bio-mechanics, energy expenditure, heat stress fatigue.

User characteristics, Design of keyboards, Controls, and VDT's; Human factors in personal computers, Computer aided design, Computer-aided manufacturing and Control rooms; Human error in computer systems.

Examples of optimization problems in engineering design: flexural systems, stressed systems, mechanical systems, digital filters. Optimality conditions. Single and multivariable unconstrained optimization. Constrained optimization. Survey of global optimization: exact and non-exact methods. Each student is expected to solve an optimal design problem related to his background. Prerequisite: Graduate standing. (Not open to credit for SE majors).

Greedy methods for continuous and discrete variables. Concept of neighbor solution and neighborhood size. Penalty and Lagrange Methods for handling constraint models. Examples of combinatorial optimization problems in engineering. Simulated annealing, genetic algorithms, tabu search, evolutionary methods and neural networks. Hybrid methods. Application to large engineering optimization problems. Term project. Prerequisite: graduate standing ( Both ISE 571 and EE 556 can not be taken for credit)

This course covers new and recent topics in Industrial and Systems Engineering. A faculty member shall propose the independent study topics and shall be approved by the department council and the graduate council. Prerequisite: Consent of the Instructor

This course covers new and recent topics in Industrial and Systems Engineering. A faculty member shall propose the independent study topics and shall be approved by the department council and the graduate council.

Graduate students working towards either M.S. or Ph.D. degrees, are required to attend the seminars given by faculty, visiting scholars, and fellow graduate students. Additionally each student must present at least one seminar on a timely research topic. Among other things, this course is designed to give the student an overview of research in the department, and a familiarity with the research methodology, journals and professional societies in his discipline. Graded on a Pass or Fail basis. Prerequisite: Graduate standing

In this course the student conducts a project where he applies the knowledge gained in the course work to a problem in his area under the supervision of a faculty member in ISE and prepares a report. The report is expected to include an introduction, literature review, research methodology, model building and or data analysis, recommendations, references and appendices. This course requires a final project presentation and a report. It is required for all M. Eng students. Prerequisite: ISE 502 and ISE 503.

Pre-Requisites: ISE502 And ISE503

Algorithms for solving large scale linear programs. Interior and exterior point methods and their convergence properties. Computational complexity of linear programing algorithms . Efficient implementation for large scale LP, computer project. Prerequisite: ISE 503.

Pre-Requisites: ISE503

This course is intended to allow students to conduct research in advanced problems in his MS research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the courses. Graded on a Pass or Fail basis. Prerequisite: ISE 502, ISE503 and prior arrangement with an instructor.

Pre-Requisites: ISE502 And ISE503

Advanced forecasting models including Box and Jenkins approach. Advanced aggregate production planning models includes linear, quadratic and nonlinear programming models. Desegregation schemes. Lot sizing techniques for material requirement planning. Nervousness and freezing just-in-time manufacturing philosophy. Group technology. Algorithms for part family formation. Flexible manufacturing systems. World-class manufacturing. Effects of maintenance and quality on production. Research papers from various journals in the field are covered. Term projects. Prerequisite: ISE 508.

Pre-Requisites: ISE508 Or ISE508

A student has to identify a specific problem, analyze it in depth, identify research objectives, and conduct the research to achieve the objectives under a supervision of a faculty member. In this course students have to demonstrate that they can conduct a research or a research-based design project individually and independently. Co-requisite: ISE 599.

Pre-Requisites: ISE599*

Co-Requisites: ISE 599

A graduate student will arrange with a faculty member to conduct an industrial research project in the areas of Maintenance and Reliability preferably in a local plant. The project shall include diagnosis of an existing problem or identifying an area of improvement. The problem or area of improvement will be demonstrated by data analysis, problem formulation, a suggested approach for solving the problem or realizing the improvement. Then implementation, validation and repot writing. Prerequisite: Completion of at least 12 credit hours in the program.

Elements of Convex analysis, optimality conditions for smooth optimization problems, duality theory for Nonlinear programs, formulation of quadratic programs as linear complementarity problems (LCP), successive linear programming or quadratic programming methods for NLP, convergence of nonlinear programming algorithms, complementary pivot method for LCP, complementary pivot methods for fixed point computing and their application to NLP, survey of other methods for constrained NLP (Frank-Wolfe method, methods of feasible directions, reduced gradient methods, penalty and barrier methods, gradient projection methods, active set methods and others), case studies. Prerequisite: ISE 521 or equivalent

Pre-Requisites: ISE521 Or ISE521

Interval arithmetic. Functions of intervals. Systems of interval linear and nonlinear equations and inequalities. Unconstrained global optimization. Inequality and equality constraints global optimization problems.

Extension to the classical network problem formulation including constrained, multi-commodity and nonlinear networks. Uni-modularity property, assignment and matching, Lagrangian relaxation and network optimization. The decomposition approach for solving constrained and multi-commodity network. Traveling salesman problem, routing models, branch and bound and heuristics for routing problems. Polynomial time scaling algorithms, strongly polynomial algorithm for network problems. Algorithms for nonlinear networks. Complexity of network algorithms. Prerequisite: ISE 525.

Different formulations of the stochastic programming problem. Chance constrained problems, the recourse problem, linear programming under uncertainty. Decision rules in chance constrained programming, deterministic equivalence in stochastic programming, multi-stage stochastic programming, Duality and Computational issues in stochastic programming, Problems of existence of solution and optimality conditions in stochastic programming, stability of solutions in stochastic programming. Prerequisites: ISE 503 and ISE 502.

Pre-Requisites: ISE502 And ISE503

Structuring decision problems with multiple criteria. Fundamentals and recent advances in multiple criteria decision making (MCDM) models. Selected approaches for discrete MCDM. Multiple criteria optimization: schemes for generating efficient solutions selected approaches: Goal programming, interactive approaches, surrogate worth tradeoff. Group decision making and negotiation. MCDM support systems. Case studies. Prerequisites: ISE 503 or Equivalent and Consent of the Instructor.

Pre-Requisites: ISE503

Advanced concepts in the identification, design, analysis, development and implementation of human operated systems; existing and emerging systems identified from industry. Case examples of theories of communication, decision and control.

Pre-Requisites: ISE536 Or ISE536

The queue G/M/m, the method of collective marks, the queue G/G/1. Bounds, inequalities and approximation, priority queues. Application in computers.

Characterization and Specification of stochastic processes, stationary and ergodicity, correlation function and power spectra, wiener, Poisson, Markov and Gaussian processes; Martingales; orthogonally principle and mean square estimation; stochastic integrals. Introduction to stochastic differential equations and stochastic calculus. Prerequisite: ISE 543.

Pre-Requisites: ISE543 Or ISE543

Formulation examples, computational complexity of algorithms and problems, P, NP-complete and NP-hard classes of problems, cutting plane theory, branch and bound, knapsack problem, Bender decomposition, partial enumeration and implicit enumeration methods, Lagrangian relaxation, local search and other heuristic approaches, simulated annealing, computer project. Prerequisite: ISE 503.

Pre-Requisites: ISE503 Or ISE503

Multi-Stage problems and recursive algorithms, application in a variety of areas, Markov renewal programming and discrete dynamic programming, applications to optimal control. Prerequisite: ISE 503.

Pre-Requisites: ISE503

Analysis of production and inventory systems, deterministic inventory models, stochastic inventory models, deterministic and stochastic production planning, process selection, multistage and dynamic production planning models, modern materials management techniques like Just-in-Time, Kanban etc., single and multiple source models.

Analysis of costs of manufacture and discussion of the economics of low, medium, and high volume manufacture with emphasis on the factors of production. Economics of replacement.

Systematic presentation of conceptual and pragmatic metrologies, tools, and techniques for productivity measurement, evaluation, planning, and improvement. Focus is on productivity engineering and management as ongoing, consistent process through a formalized, rational, and unified treatment of the productivity four-phases cycle.

Quantitative study of the non-traditional material removal and forming processes. Economic aspects, theoretical and industrial applications. Electro-chemical machining, electrical discharge machining, high energy forming, and laser and electron beam machining.

The objective of this course is to select a specific area in Operations & Research and study cases and research papers to enable the student to conduct research at the frontier of this area. The specific contents of the special topics will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council. Prerequisite ISE 502 and ISE 503

Pre-Requisites: ISE502 And ISE503

The objective of this course is to select a specific area in Production Systems and Quality Control, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council. Prerequisite: ISE 508

The objective of this course is to select a specific area in Man-Machine Systems, and study cases and research papers in it to enable the student to conduct research at the frontier of the area. The specific contents of the special topic will be given in detail at least one semester in advance of that in which it will be offered. It is also subject to the approval of the graduate council. Prerequisite: Consent of instructor.

This course has a variable content and usually will be on a recent topic or an area not covered in the ISE courses. A faculty member shall propose the independent study topics and shall be approved by the department council. A student in MS shall take a maximum of three credit hours of independent studies.

Graduate students working on their Ph.D. degree are required to attend seminars and contribute to the general area of their dissertation research. Grades will be Pass or Fail. Prerequisite: Admission to Ph.D. Program

This course is intended to allow the student to conduct research in advanced problems in his Ph.D research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the courses. Graded on a Pass or fail basis. Prerequisite: Prior arrangement with an instructor.

This course is intended to allow the student to conduct research in advanced problems in his Ph.D research area. The faculty offering the course should submit a research plan to be approved by the graduate program committee. The student is expected to deliver a public seminar and a report on his research outcomes at the end of the courses. Graded on a Pass or fail basis. Prerequisite: Prior arrangement with an instructor.

None

This course enables the student to submit his Ph.D. Dissertation Proposal and defend it in public. The student passes the course if the Ph.D. Dissertation Committee accepts the submitted dissertation proposal report and upon successfully passing the Dissertation Proposal Public Defense. The course grade can be NP, NF or IC. Prerequisite: Ph.D. Candidacy, ISE 699

Pre-Requisites: ISE699

This course enables the student work on his Ph.D. Dissertation as per the submitted dissertation proposal, submit its final report and defend it in public. The student passes this course if the Ph.D. Dissertation Committee accepts the submitted final dissertation report and upon successfully passing the Dissertation Public Defense. The course grade can be NP, NF or IP. Prerequisite: ISE 711

Pre-Requisites: ISE711