Students pursuing the STL MS degree select from one of two program concentrations, transportation or logistics. To complete the degree, students in both concentrations are required to complete the same five core courses (15 credits) in addition to five (5) concentration-specific and /or general electives (15 credits). These electives may include a project (3 credit hours) or thesis (6 credit hours), both of which would qualify as the culminating degree experience. If neither a project or thesis is selected, then a comprehensive exam is required.
The student’s academic advisor must approve all proposed programs of study, including the elective courses to be counted towards the degree. For the project and thesis options, a STL faculty member must agree to serve as the project/thesis advisor.
This course covers global logistics and distribution issues, dealing with the management of physical material flows, documentation, and information flows in cross-border supply chains. Logistics issues such as intermodal transportation, e-fulfillment, cross-border trade regulations, reverse logistics, and design of sustainable supply chains are dealt with. The optimum design of distribution systems, inventory positioning in distribution networks, selection of optimal transportation modes, inter-modal transport, etc. are also covered. Emerging technologies such as warehouse management systems (WMS), distribution requirements planning (DRP), radio frequency identification (RFID), geographical information systems (GIS), global positioning systems (GPS) applications are also covered. Both qualitative issues (such as regulatory issues, INCO terms and documentation) as well quantitative tools and techniques such as the use of Route Assist software will be covered. Prerequisite: MGO 630.
Concepts of operations research methodology including objective functions, constraints and optimization. An introduction to linear and integer programming emphasizing transportation and logistics applications and an optimization software tool such as OPL/CPLEX. If time permits, elementary mathematical models of Markov decision processes and waiting-line (queuing) models with Poisson arrival and exponential service.
This course focuses on design, modeling and optimization of global supply chain networks. The course deals with modeling approaches and quantitative tools and techniques for design and optimization of global supply chain networks. The course also covers information systems and technologies for supply chain planning and coordination. The topics covered include: supply chain strategy formulation, performance metrics, new forecasting models applicable for supply chain contexts, newsvendor models for capacity and aggregate planning, models for location and design of supply and distribution entities, inter-organizational planning, advanced planning systems, multi-echelon inventory management techniques, distribution requirements planning (DRP) systems, joint transportation-inventory models, and pricing and revenue management techniques. The course will be taught in a manner that will enable you to obtain APICS professional certification (CPIM / CFPIM) with minimum preparation after the course. Prerequisite: MGO 630.
This course aims to provide students with a general background of various statistical analysis techniques and data mining methods that are used in transportation systems. It covers various practical analytical topics in transportation and logistics, including model estimation, data analysis, traffic forecasting, and incident prediction. A broad range of transportation related techniques are covered in statistics, data mining and optimization skills, such as Logistic Regression, Poisson Regression, Time Series Modeling, Survival Analysis, Classification, and Clustering. Popular statistical modeling software will be used to solve various practical problems.
As an example of a Complex Adaptive System (CAS), the behavior of transportation systems emerges as a result of the interactions among the many different components that make up the system. This course will introduce students to the different transportation system components, and will then review some of the fundamental concepts regarding transportation systems and how they may be modeled. The discussion will include both passenger transportation as well as freight transportation.
This course addresses the design, operation, control and management of transportation facilities. Topics covered in the course include geometric design of roadways, capacity analysis for freeway segments, signal timing and design, and intersection design and layout. Students are introduced to a number of traffic analysis and traffic simulation software, including SYNCHRO and SimTraffic. Students are required to undertake a comprehensive term project that involves detailed analysis and/or simulation of a transportation facility and write a survey-type paper on a topic of recent interest that is related to traffic operations and design. LEC. Prerequisite: Senior or graduate standing.
Research in traffic flow theory focuses on developing mathematical models that describe the interaction among the vehicle/driver unit and the infrastructure. The mathematical models derived provide the basis for evaluating the quality of service provided by the transportation system, and for assessing the impact of the system on the surrounding environment. This course addresses traffic stream and driver characteristics, car following and macroscopic models, traffic impact models, signalized intersections models, and traffic simulation. LEC. Prerequisite: Graduate standing.
The focus of this course is the theory and the state-of-practice of individual discrete choice modeling with applications in transportation and other fields. The course provides students with an understanding of the theory, methods, application and interpretation of Binary Logit, Multinomial Logit (MNL), Nested Logit and other members of the Generalized Extreme Value (GEV) family of models. The general theory and modeling methodology applicable to the discrete choice problems are discussed. Classroom examples and assignments focus on applications in the context of travel related choices. LEC. Prerequisite: Senior or graduate standing.
This course is designed to provide students with the fundamentals of highway design including layout, horizontal and vertical alignment, earthwork requirements, intersection design, and safety considerations. Students will be taught how to use existing computer software to generate and evaluate designs. The course includes a significant project component.
The focus of this course is current and state-of-the-art methods for forecasting travel demand. A major component of the course focuses on the four-step urban transportation planning process consisting of the trip generation, trip distribution, mode split, and traffic assignment steps. Recent refinements to the process are discussed together with a brief introduction to activity-based models. LEC. Prerequisites: Senior or graduate standing.
The focus of this class is modeling flow patterns through urban transportation networks. An analytical approach to modeling the resulting flow pattern is adopted, based on the formulation and solution of the traffic assignment problem as a non-linear optimization problem. Among the topics covered in the course are transportation networks and optimality, cost functions, deterministic and stochastic user equilibrium assignment, origin-destination matrix estimation, and network reliability and design. LEC. Pre-requisite: CIE 539.
This class focuses on the operations, control and management of integrated surface transportation networks, including the use and application of intelligent transportation systems. Topics covered in the course include traffic monitoring systems, advanced traffic management systems, dynamic traffic assignment and route guidance, adaptive traffic control systems, the development and application of traffic simulation models to the control and management of integrated transportation systems, and automated highway vehicle systems. LEC. Pre-requisite: CIE 536.
This course is concerned with analyzing the problem of optimally locating one or more facilities. The approach is a purely analytical one, and the focus is on studying the vast academic literature in this field of Operations Research. Specific topics that are covered include the p-median, p-center, and stochastic queue median problems. Both network and planar location topologies are considered. Analytical tools are developed for these various problems and solution algorithms are detailed.
Basic theory of Discrete Optimization as well as the computational strategies for exact and heuristic solution of problems having discrete decision variables. Discrete Models can be divided into two main categories: Integer Programming and Combinatorial Optimization. Integer programming encompasses models with a mixture of discrete and continuous decision variables, and ones for which efficient algorithms are not likely to be found. On the other hand combinatorial models may deal with problems having pure discrete elements for which clean and efficient procedures exist. This latest class includes Network Optimization. This course will place emphasis on Integer Programming and related areas. The course is a good one for students who are planning to apply OR tools in Production or Manufacturing problems or supply chain/service/logistics related problems as well as continue using an optimization software tool called CPLEX.
This course teaches the fundamentals of applied probability theory. Topics include algebra of events; sample space representation of the model of an experiment (any non-deterministic process); random variables; derived probability distributions; discrete and continuous transforms and random incidence. The course also introduces elementary stochastic processes including Bernoulli and Poisson processes and general discrete-state Markov processes. This is followed by a discussion of some basic limit theorems and some common issues and techniques of both classical and Bayesian statistics.
Development and application of mathematical models for queuing systems. Topics include Poisson and Erlang systems, bulk and priority queues, queuing networks, and the optimal design and control of queuing systems. A prerequisite knowledge of stochastic processes is recommended.
Solutions to graph theory and optimization problems on directed and undirected graphs. Shortest path, maximum flow, minimum weight flows, and matching problems. Also, the traveling salesman and Chinese postman problems.
The service sector is the largest sector of all developed countries and evidence suggests that productivity in the service sector has lagged behind that of its manufacturing counterpart. It is therefore critical in the global market to improve the efficiency and competitiveness of these service processes. This course is designed to apply theory with practice in service business process management. The objective of this course is to provide the student with an understanding of the issues, models and numerical methods particular to service management, with attention to both the strengths and weaknesses of these devices. Prerequisite: MGO 630.
This course deals with design, control, and operation of supply chains for competing effectively in the context of global operations management. Both manufacturing and service (such as health care) industry supply chains are covered. The topics covered include: state-of-the-art qualitative and quantitative techniques for optimum configuration of in-bound and outbound logistics, principles of postponement in design, processes and logistics, mass customization, global location factors for offices, plants and distribution centers, collaborative planning, forecasting and replenishment (CPFR) systems, countering bullwhip effects in supply chains, vendor managed inventory (VMI), strategic alliances and partnering, global purchasing and buyer-supplier relationships, and the complexities of the material, information, and cash flows across international borders. This course supplements MGS 616, which covers e-commerce integration aspects of supply chains. Prerequisite: MGO 630.
This course focuses on purchasing and supply management principles and practices in the context of global supply chains. The importance of purchasing is primarily due to the fact that the value of materials procured in manufacturing, and service supply chains such as retail can be more than 65% of the cost of the goods sold. Given the globalization of supply sources, it has become increasingly necessary to understand the complexities of global supply markets, cross-border legal aspects of purchasing, global vendor development, systematic reduction of supply risk, strategic alliances and supply network building, vendor managed inventory (VMI) contracts, and new forms of negotiation strategies with suppliers. Purchasing practices to support lean organizations to ensure just-in-time delivery on a global basis are also covered. In addition, given the growth of internet technology, e-commerce technologies to support purchasing, and supplier relationship management (SRM) systems are also be covered. Prerequisite: MGO 630.
This course focuses on production and inventory management problems in the entire supply chain, and the application of quantitative models and information systems and technologies for these problems. An enterprise resource planning (ERP) system platform is assumed and the course also covers the implementation aspects of ERP systems. The topics covered include supply chain strategy and coordination mechanisms, forecasting systems, aggregate planning, advanced planning systems (APS), master production scheduling, materials requirements planning (MRP) systems, inventory management for suppliers, manufacturers and distributors, cellular manufacturing, just-in-time (JIT) systems, lean manufacturing, optimized production technology (OPT), and flexible manufacturing systems (FMS) technologies. The completion of this course will enable students to take the certification examinations (CPIM/CFPIM) for American Production and Inventory Control Society (APICS). Prerequisite: MGO 630.
The basic objectives of the course are to enable students:
The basic objectives of the course are to enable students:
This course provides an introduction to the fundamental concepts and issues of financial accounting with emphasis on the interpretation of financial statements. The course addresses the economic consequences of transactions and their presentation on corporate financial statements. A primary objective is to introduce corporate financial statements as a tool for company valuation and decision making. Emphasis is on the analysis of effects of decisions on financial performance and use of financial statements to evaluate organizations.
This course will be an intensive study of Linear Programming (LP). LP deals with the problem of minimizing or maximizing a linear function in the presence of linear equality and/or inequality constraints. Both the general theory and characteristics of LP optimization problems as well as effective solution algorithms and applications will be addressed. The course is a good one for students who are planning to apply Operations Research (OR) tools in all areas of application in the public and private sectors including production or manufacturing problems and service/logistics related problems as well as to learn an optimization software tool called OPL/CPLEX. This course is part of the core for the MS and PhD degrees concentrating in OR; therefore comprehension of the underlying mathematical theory/why things work is emphasized.
A development of the mathematical theory of conflict, cooperation competition, and coercion among economic decision-makers. Classical n-person game theory and its relationship to linear programming. Dynamic cooperative games, their applications to decentralized control systems and the analysis of the behavior of decision-makers in organizations.
This is an applied Operations Research course, where the focus is on the utilization of the analytical tools that students have learned in other Operations Research courses to study problems of urban significance. The course starts off with a review of basic probabilistic concepts. The first topic covered is that of geometrical probability, a powerful tool to approach urban problems. Then a discussion on queuing theory is presented. This is followed by a discussion of spatial queues that are used in modeling urban emergency service systems. The next topic is on network problems that are useful in an urban context. The final topic is on simulation modeling as applied to urban problems. All topics are reinforced with real-world examples and in-depth homework assignments.
This course introduces students to algorithm design and implementation in a modern, high-level, programming language (currently, Java). It emphasizes problem-solving by abstraction. There will also be a brief coverage of the social and ethical aspects of computing. Topics include data types, variables, expressions, basic imperative programming techniques including assignment, input/output, subprograms, parameters, selection, iteration, Boolean type, and expressions, and the use of aggregate data structures including arrays and records. Students will also have an introduction to the basics of abstract data types and object-oriented design, as well as the mathematics of computer science such as Boolean algebra, basic number theory, etc.
This course is a continuation of CSE 503, in which heavy emphasis is placed on abstract data types (ADTs) and object-oriented methodology, where the student will be expected not only to understand ADTs, but also to design and implement robust ADTs using a modern, object-oriented, programming language. Topics such as encapsulation, polymorphism, templates, and inheritance will be emphasized. Essential topics to be integrated in this framework include the use of recursion; pointers; dynamic memory management; linked structures including linked lists, binary trees, stacks, queues, and other advanced data structures; and algorithms, including advanced searching and sorting algorithms. The analysis of algorithm complexity ("big O" notation) will be introduced.
This course focuses on the fundamental techniques in data mining, including data warehousing, frequent pattern mining, clustering, classification, anomaly detection and feature selection methods. Specifically, we will cover the following topics: Data warehousing--model design; Frequent pattern mining--association rules mining; Clustering--partition-based, hierarchical-based, density-based approaches, spectral clustering; Feature selection--dimensionality reduction; Classification--decision-tree, Bayesian, rule-based, SVM, ensemble methods; Anomaly detection--statistics-based, density-based, clustering-based; Evaluation and validation of data mining results; Correlation analysis--metrics and analysis; Graph and network mining; Visualization of patterns--mapping between high-dimensional data and low-dimensional data; and Multi-source information integration. To demonstrate how data mining techniques are applied to various domains, we focus on the software systems design of bioinformatics, discussing the applications of data warehousing and data mining in biological and biomedical related fields. The class discusses various software systems and provides insight that will help students gain a comprehensive understanding of the bioinformatics field. Projects will be designed based on these applications.
STL 500 presents advanced topics in sustainable Transportation and Logistics to meet the needs and interests of students. LEC. Content varies each semester. May not be offered on a regular basis.