Bachelor of Science (B.S.) Major in Industrial Engineering / Master of Science (M.S.) Major in Engineering (Industrial Engineering Non-thesis Option)

IE 1310. Introduction to Industrial Engineering.

This course provides an introduction to the field of industrial engineering through examination of its historical development, foundational concepts, and principal areas of practice. Students are introduced to analytical methods and problem solving approaches used to analyze and design systems involving people, materials, information, and technology. Topics include productivity analysis, operations research, facilities planning, statistical process control, and human factors. The course also surveys career paths in manufacturing, healthcare, logistics, and service industries, emphasizing the professional roles and functions of industrial engineers within contemporary organizations. Prerequisite: [MATH 1315 or MATH 1317 or MATH 1319 or MATH 1329 or MATH 2321 or MATH 2417 or MATH 2471 with a grade of "C" or better] or [ACT Mathematics score of 24 or better] or [SAT Mathematics score of 520 or better] or [SAT Math Section score of 550 or better] or [Accuplacer College Mathematics score of 86 or better] or [Compass College Algebra score of 46 or better] or [Next-Generation Advanced Algebra and Functions Test of 263 or better].

IE 3305. Introduction to Data Analysis.

This course introduces principles and applications of data analysis using spreadsheet and programming tools, including Microsoft Excel, Python, and SQL. Students study methods for data manipulation, visualization, optimization, and decision support modeling within industrial engineering contexts. The course examines spreadsheet based productivity techniques, database interaction, and the use of programming languages for advanced analytical tasks. Students also explore contemporary machine learning methods as analytical tools used in modern industrial systems. Emphasis is placed on interpreting empirical data, constructing analytical models, and supporting technical decision making through systematic computational approaches. Corequisite: IE 3320 with a grade of "D" or better.

IE 3320. Engineering Statistics.

This course introduces principles of probability and statistics used in engineering analysis, including probability distributions, data visualization, interval estimation, hypothesis testing, and regression modeling. Students study statistical methods applied to engineering problems and contemporary data analysis contexts. The course examines mathematical frameworks for interpreting datasets, assessing variability, and evaluating statistical significance. Emphasis is placed on the use of descriptive and predictive statistical models to support technical analysis and informed engineering decision‑making. Prerequisites: MATH 2472 or MATH 2473 with a grade of "D" or better.

IE 3330. Quality Engineering.

This course provides an introduction to quality engineering methods used to analyze, monitor, and improve manufacturing and service processes. Students examine statistical tools for measuring process variability and apply continuous improvement methodologies to evaluate system performance. Topics include DMAIC, statistical process control, process capability analysis, and acceptance sampling. Emphasis is placed on data driven decision making and the use of statistical software to analyze quality characteristics. Through applied examples, students develop the ability to interpret results, assess process performance, and support engineering judgments in quality related contexts across diverse operational environments. Prerequisite: IE 3320 with a grade of "D" or better.

IE 3340. Operations Research.

This course examines fundamental optimization models and analytical methods in operations research, including linear programming, simplex-based algorithms, duality theory, sensitivity analysis, integer programming, and network flow models. Emphasis is placed on mathematical formulation, algorithmic solution techniques, and interpretation of model outcomes in engineering decision-making contexts. Students engage in quantitative modeling and computational implementation of optimization methods to address structured industrial problems. By the end of the course, students will be able to formulate optimization models, apply appropriate solution methods, analyze solution properties, and evaluate their practical implications. Prerequisite: [CS 1428 or CS 1342] and ENGR 3315 and MATH 3377 and IE 1310 with grades of "D" or better.

IE 3360. Lean Systems and Ergonomics.

This course examines lean process improvement tools, ergonomics, and work design principles used in industrial and service systems. Topics include time study and motion analysis, process mapping, 5S, line balancing, setup time reduction, standardized work, and work sampling. The course also introduces anthropometry and ergonomic design guidelines as analytical frameworks for evaluating human–system interaction. Emphasis is placed on applying quantitative and observational methods to analyze system performance, workplace design, and operational efficiency across manufacturing and service contexts. Prerequisite: IE 3320 with a grade of "D" or better.

IE 4310. Statistical Design of Experiments.

This course examines principles and analytical methods in the statistical design and analysis of experiments. Topics include hypothesis testing, analysis of variance, blocked and factorial designs, fractional factorial experiments, regression modeling, and response surface methodology. Emphasis is placed on experimental planning, model formulation, statistical inference, and interpretation of experimental results in engineering contexts. Students apply statistical modeling techniques and computational tools to analyze experimental data and evaluate system performance. By the end of the course, students will be able to design controlled experiments, analyze experimental data, and assess the validity and practical implications of statistical conclusions. Prerequisite: IE 3320 with a grade of "D" or better.

IE 4320. Integrated Production Systems.

This course introduces concepts and mathematical models used in the design and control of integrated production systems within manufacturing and service environments, emphasizing forecasting, aggregate production planning, inventory management, material requirements planning, and shop floor control. Students apply quantitative models and decision making tools to create demand forecasts, production plans, determine optimal inventory levels and reorder points, and schedule shop floor activities. Emphasis is placed on formulating engineering solutions, analyzing data to support operational decisions, and evaluating system performance. Prerequisite: IE 3320 with a grade of "D" or better. Corequisite: IE 3340 with a grade of "D" or better.

IE 4330. Reliability Engineering.

This course teaches analytical methods to model, design, and maintain system reliability, aiming to prevent and ensure equipment performance over its lifecycle. Key topics include statistical models such as exponential and Weibull lifetime distribution for components and systems, reliability block diagram for series, parallel, k-out-of-n redundancy and networks, accelerated life testing and proportional hazard rate model, failure in time and design for reliability, preventive and predictive maintenance, spare parts logistics and repairable inventory, and Markov decision process with applications in condition-based maintenance. Prerequisite: IE 3320 with a grade of "D" or better.

IE 4335. Lean Six Sigma Methodologies.

This course examines the principles and methodologies associated with Lean Six Sigma in manufacturing and service systems. Inquiry centers on structured improvement models such as DMAIC and PDCA and supporting tools for process analysis and control. Quantitative topics include statistical process control, introductory experimental design, and process risk assessment techniques used to evaluate process changes. The course examines approaches for diagnosing process issues, proposing improvements, and developing sustainment plans. Examples are drawn from both manufacturing and service enterprises. Prerequisite: IE 3330 and IE 4310 with grades of "D" or better.

IE 4340. Non-Linear Optimization Techniques.

This course introduces mathematical modeling and computational methods for nonlinear programming problems in engineering applications. The course presents techniques for optimizing unconstrained and constrained nonlinear models. Instructional methodology includes lectures and hands-on computational experiences using Python and multiple contemporary nonlinear optimization software. Students examine how problem structure affects tractability and solution quality. By the end of the course, students should be able to formulate and solve engineering problems using nonlinear programming approaches and evaluate solution performance in applied contexts. Prerequisite: IE 3340 with a grade of "D" or better.

IE 4342. Advanced Linear and Integer Programming.

This course examines advanced mathematical modeling and computational techniques used to formulate and solve linear and integer programming problems in engineering contexts. Students explore stochastic programming, dynamic programming, and decomposition methods that support large‑scale optimization. Emphasis is placed on understanding model structures, evaluating algorithmic approaches, and interpreting solution properties. Applications include manufacturing, service operations, supply chains, healthcare, and electrical systems. The course provides students with the analytical tools needed to select appropriate models, apply solution algorithms, and assess computational performance in complex decision‑making environments. Prerequisite: IE 3340 with a grade of “D” or better.

IE 4350. Supply-Chain Engineering.

This course examines engineering and managerial issues in supply chain systems with an emphasis on decision making in complex operational environments. Topics include supply chain strategy, network design, demand and supply planning, inventory management, sourcing, transportation, pricing, and coordination enabled by information systems. The course emphasizes how decisions across multiple stages of a supply chain interact and influence overall system performance. Through analytical modeling and problem solving, students evaluate tradeoffs among cost, service level, responsiveness, and operational efficiency in manufacturing and service settings. Prerequisite: IE 3340 with a grade of "D" or better.

IE 4355. Facilities Planning.

This course examines principles and analytical methods for facility planning, layout design, and material handling systems. Topics include facility location, plant layout models, space requirements analysis, warehouse operations, material handling equipment selection, and system integration considerations. Emphasis is placed on quantitative modeling, systematic layout design procedures, and evaluation of alternative facility configurations under operational constraints. Students apply analytical tools and design methodologies to develop and assess facility planning solutions for manufacturing and service environments. By the end of the course, students will be able to analyze facility systems, design layout alternatives, and evaluate operational performance implications. Prerequisite: ENGR 3315 and MFGE 2332 with grades of "D" or better.

IE 4360. Human Factors Design.

This course introduces principles and practices of human factors engineering applied to the design and development of ergonomic systems and products. Students apply anthropometric and human performance data using experimental and observational methods to evaluate engineering designs. Emphasis is placed on integrating human capabilities and limitations into engineering decision‑making within technical and safety constraints. Through projects and case studies, students examine the application of human factors methods across multiple engineering contexts and assess their impact on system performance, safety, and usability. Prerequisites: IE 3360 with a grade of "D" or better.

IE 4370. Probabilistic Operations Research.

This course describes probabilistic models in operations research to include queuing theory, simulation, and Markov chains. Main topics include discrete Markov transition models and probability matrix, Ergodic and non-ergodic Markov process, computing steady-state probability using one-step transition probability, death-birth queueing model, Little’s law, Erlang B and Erlang C queueing systems, and machine repairperson problem. Emphasis will be placed on modeling applications to solve problems in manufacturing and service industry sectors, such as aerospace, energy production, retail stores and maintenance logistics supply chains. Prerequisite: [CS 1428 or CS 1342] and IE 3320 with grades of "D" or better.

IE 4381. Introduction to Systems Engineering.

This course explores the strategic design and management of complex, large-scale systems and their interdependencies within a "systems of systems" framework. The scope encompasses the entire lifecycle, from initial needs analysis and concept definition to final integration and evaluation. Students will apply systems thinking methodologies to model and solve multi-disciplinary engineering challenges. Upon completion, participants will be equipped to lead technical projects using standardized systems engineering processes to ensure operational efficiency and resilience. Prerequisite: IE 3320 with a grade of "D" or better.

IE 4392. Industrial Engineering Design I.

This course introduces students to the application of Industrial Engineering principles in a project‑based, team‑oriented environment. Students analyze industrial and systems engineering problems, identify technical and economic constraints, and apply relevant engineering standards under realistic conditions. Through structured planning, documentation, and iterative development, student teams learn to design, evaluate, and communicate engineering solutions for real or simulated industrial contexts. Emphasis is placed on effective project management, professional communication, and the ability to integrate knowledge acquired in prior engineering coursework. This course serves as the first part of a two‑course sequence and prepares students for continued project development in Industrial Engineering Design II (IE 4393). Prerequisite: IE 3330 and IE 3340 and IE 3360 all with grades of "D" or better. Corequisite: 6 hours from [IE 4310 or IE 4355 or IE 4370] both with grades of "D" or better.

IE 4393. Industrial Engineering Design II.

This course continues the two‑semester design sequence by guiding student teams through the implementation and evaluation of solutions to industrial and systems engineering problems. Students work within realistic technical, economic, and organizational constraints while applying analytical methods learned in previous coursework. Emphasis is placed on project definition, modeling, documentation, and adherence to relevant engineering standards. Teams prepare written reports and deliver oral presentations that communicate design decisions to diverse stakeholders. The course provides structured opportunities for applying professional skills in problem solving, documentation, and technical communication. Prerequisite: IE 4392 and 6 hours from [IE 4310 or IE 4355 or IE 4370] with grades of "D" or better. Corequisite: 6 hours from [IE 4320 or IE 4350 or MFGE 4396] with grades of "D" or better.

IE 4399D. Heuristic Optimization Techniques.

This course introduces heuristic and metaheuristic optimization methods for solving large scale and computationally challenging engineering problems. Topics include problem complexity, NP hardness, constructive and improvement heuristics, and major metaheuristic frameworks such as simulated annealing, tabu search, genetic algorithms, ant colony optimization, and particle swarm optimization. Applications are drawn from scheduling, routing, logistics, and resource allocation. Through algorithm development, computational implementation, and evaluation of solution performance, students learn to design, implement, and assess heuristic methods for optimization problems where exact approaches are computationally impractical. Prerequisite: [CS 1428 or CS 1342] and IE 3340 with grades of "D" or better.

IE 4399G. Special Topics in Project Management.

This course provides undergraduate students with solid foundations of project management. Classical, prescriptive and adaptive methodologies are presented. Students will get to know different standards in project management, whereas the main focus will be on those from PMI (Project Management Institute). This course covers all phases of project management and introduces the most relevant tools and techniques to initiate, plan and execute projects in different contexts successfully. In addition to techniques, the “soft” perspective of managing people and their cooperation within projects will be addressed in detail.

IE 5310. Advanced Statistical Design of Experiments for Engineers.

This course examines advanced methods in the statistical design and analysis of experiments for engineering applications. Topics include full and fractional factorial designs, response surface methodology, optimal and robust design strategies, random and mixed factor experiments, and industrial experimentation techniques. Emphasis is placed on experimental optimization, model adequacy evaluation, and application of DOE methodologies to complex manufacturing and service systems. Students develop advanced experimental strategies to improve system performance and reliability. By the end of the course, students will be able to design advanced experimental frameworks and critically evaluate engineering experimentation outcomes. Prerequisite: ENGR 5310 with a grade of "C" or better or instructor approval.

IE 5320. Modeling and Analysis of Manufacturing Systems.

This course examines methods for modeling and analyzing manufacturing systems. Topics include sustainable production systems, material handling operations, scheduling methods, and supply chain coordination. Analytical and quantitative models are used to evaluate production flow, resource utilization, and system performance in manufacturing environments. The course analyzes how modeling techniques support decision-making in production planning and operational management. Emphasis is placed on identifying system inefficiencies, evaluating alternative production strategies, and improving productivity, cost efficiency, and sustainability across manufacturing and supply chain processes.

IE 5330. Advanced Quality Control and Reliability Engineering.

This course provides in-depth knowledge in reliability modeling and maintenance optimization for components and systems. Main subjects include parametric lifetime models such as exponential, Weibull, lognormal and gamma distribution for components and systems, reliability block diagram for series, parallel, series-parallel, k-out-of-n active redundancy and networks, accelerated life testing and proportional hazard rate model, stress and strength interference model, failure-in-time and design for reliability, preventive and condition-based maintenance, repairable parts inventory, and Markov decision process with applications in predictive maintenance. Prerequisite: ENGR 5310 with a grade of "C" or better.

IE 5340. Applied Deterministic Operations Research for Engineers.

This course covers mathematical modeling and computational methods for linear and integer programming in engineering applications. The course presents solution techniques and concepts, including graphical solution, simplex method, duality theory, sensitivity analysis, and branch-and-bound method. Instructional methodology comprises lectures and hands-on computational experiences using Python and multiple contemporary optimization software. Applications are drawn from areas such as manufacturing, service systems, supply chain, healthcare operations and transportation. By the end of the course, students should be able to formulate and solve engineering problems using linear and integer programming methods and report on the results in applied contexts.

IE 5343. Non-Linear Optimization Techniques for Engineers.

This course covers mathematical modeling and computational methods for nonlinear programming problems in engineering applications. The course presents techniques for optimizing unconstrained and constrained nonlinear models. Instructional methodology includes lectures and hands-on computational experiences using Python and multiple contemporary nonlinear optimization software. Students examine how problem structure affects tractability and solution quality and present results in a technical report. By the end of the course, students should be able to formulate and solve engineering problems using nonlinear programming approaches and evaluate solution performance in applied contexts.

IE 5345. Advanced Optimization.

This course examines advanced optimization techniques, including decomposition methods for linear and integer programming as well as multiobjective, stochastic and dynamic programming models. Emphasis is placed on analyzing how model structure and decomposition strategies influence computational tractability and solution quality. Applications are drawn from areas such as manufacturing, supply chain, healthcare operations and energy systems. Instruction includes lectures, case studies, and hands-on computational experiences using Python and contemporary optimization software. By the end of the course, students should be able to design advanced models and decomposition methods for solving large-scale linear and integer programs in applied contexts. Prerequisite: IE 5340 with a grade of "C" or better.

IE 5347. Advanced Heuristic Optimization.

This course examines heuristic and metaheuristic optimization techniques with an emphasis on real world engineering applications. Methods that search beyond local optima, including simulated annealing, tabu search, genetic algorithms, ant colony systems, and particle swarm optimization, are studied and applied to practical problems such as scheduling, routing, logistics, and resource allocation. The course addresses problem specific heuristic design, performance evaluation, and serial and parallel implementations. Students analyze selected research papers and conduct computational experiments to assess solution quality and computational efficiency. Emphasis is placed on applying heuristic methods to large scale optimization problems where exact algorithms are impractical. The course is intended for advanced undergraduate students in engineering and related fields.

IE 5360. Advanced Inventory Control.

This course introduces advanced analytical approaches used in inventory management within industrial and supply chain settings. Students examine quantitative forecasting methods, inventory policy design, and optimization techniques that support data‑informed decision‑making. Emphasis is placed on evaluating model assumptions, comparing alternative strategies, and interpreting system behavior in environments characterized by uncertainty and variable demand. Through case‑based and computational exercises, students apply analytical tools to assess the performance of different inventory systems. The course provides a foundation for understanding how inventory models support operational planning and production efficiency without endorsing specific managerial or policy choices. Prerequisite: IE 5340 with a grade of "C" or better.

IE 5370. Scheduling.

This course examines advanced scheduling methodologies and the analytical frameworks used to model and optimize production and service systems. Students study deterministic and stochastic scheduling models across single‑machine, multi‑machine, job shop, flow shop, and multi‑echelon environments. Emphasis is placed on evaluating theoretical foundations, comparing alternative solution approaches, and applying mathematical and computational tools to realistic industrial engineering problems. Through structured problem‑solving and model‑based analysis, students gain experience formulating scheduling problems, assessing algorithmic performance, and interpreting results to support decision‑making in complex operational settings. Prerequisite: IE 5340 with a grade of "C" or better.

IE 5397. System Thinking and Analysis.

This course explores the strategic design and management of complex, large-scale systems and their interdependencies within a "systems of systems" framework. The scope encompasses the entire lifecycle, from initial needs analysis and concept definition to final integration and evaluation. Students will apply systems thinking methodologies to model and solve multi-disciplinary engineering challenges. Upon completion, participants will be equipped to lead technical projects using standardized systems engineering processes to ensure operational efficiency and resilience. Prerequisite: ENGR 5310 with a grade of "C" or better.