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 an ePortfolio is required.
The student’s major 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.
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.
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.
The purpose of this course is to provide students with a general background in the application of various statistical and econometric analysis techniques and to provide new ideas for analyzing data in their research. The course will focus in a number of model-estimation methods that are used in engineering data analysis. While examples will be drawn primarily from civil and transportation engineering, the methods presented have broad applications to a variety of data-analysis applications in engineering and beyond, and these will also be discussed in the course. The material covered goes well beyond the techniques typically covered in statistics courses. The underlying theory and limitations will be discussed to ensure that the methods are properly applied and understood. The NLOGIT/LIMDEP software will be used, but no prior knowledge necessary.
The focus of this course is on the theory and practice underlying the analysis and modeling of individual people's choice behavior with applications to transportation planning, travel demand forecasting, and travel behavior analysis. The course will provide students with an understanding of the theory, methods, application and interpretation of multinomial Logit (MNL), Nested Logit and other members of the Generalized Extreme Value (GEV) family of models. It will also include an introduction to Mixed Logit models. Meanwhile, the course will use a class project as an opportunity for students to explore their interest in analyzing discrete choice behavior.
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.
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.
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.
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.
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.
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.
In this course, we will focus on the environmental impact of business operations, by taking a cradle to cradle approach that includes raw material acquisition through manufacturing use, end of life disposal (reuse/recycling/re-manufacturing). The topics covered include tools and techniques needed to quantify environmental impact in supply chains such as life cycle assessment and carbon foot printing, environmental legislation, design for the environment, recycling and re-manufacturing, energy efficiency, energy efficiency, eco-certification, responsible sourcing and managing supply of renewable resources. The formulation of business and supply chain strategies that lead to actionable, proactive agenda for sustainability that not only ensures profitability for the firm but also social and environmental responsibility will be the central theme of this course. This course is dual listed with MGO 439.
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.
This course is devoted to delivering, constructing, and implementing business forecasting analytical models and systems (with data) that are capable of enhancing decision making and providing decision support for managerial and policy decisions at the firm, industry, and country levels. It is emphasized that applications of predictive methodology and empirical analysis are an integral part of the course. Both methods and results provide solutions to problems and insights to decision makings.
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.
*Should not be taken simultaneously with STL 502/IE550
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.
The Transportation issues are at the top of national news as part of what is described as a "transportation revolution." Autonomous vehicles, electric fleets, flying cars, and hyper-loops are but a few examples of large-scale impending impacts in mobility, city planning, financial structures, and consumer behavior. Students will be immersed in a wide range of current topics through discussion and analyses of the changing face of transportation. Guest speakers, news articles and team projects all will be elements of the course.
Examines goals of strategic urban transportation planning to develop sustainable transportation systems for an urban area to promote desirable patterns of human activities. Using case studies, introduces methods for the sustainable development of urban transportation system through a strategic perspective. Investigates how technology is used to produce estimates of future travel demand and utilization of urban transportation facilities. Considers how strategic urban transportation planning evolves in response to changes in environmental sustainability, energy, development patterns, and intergovernmental coordination at the state, local, and federal levels. Includes lectures, discussions, site visits, and fieldwork. May be offered on an intermittent basis.
Evolution of the U.S. transportation system; contemporary transportation problems: provision of transportation, transport networks, transport flows, urban transportation, logistics, information technologies; transport and urban forms.
The purpose of this course is to provide students with the principles of traffic safety with a focus on safety modeling, methods, and applications. More specific topics include: safety audits; identification and prediction of hazardous locations; safety hot spots and countermeasures; traffic conflicts; accident frequency analysis; accident rate analysis; accident injury-severity analysis; selecting safety projects; intersection safety; human behavior in accident analysis. NLOGIT (LIMDEP) software will be used in this course, but no prior knowledge is necessary.
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.
This course provides opportunities for independent study of subject matter not included or not treated in sufficient depth in a regularly offered course. Independent projects or reading courses may be arranged with individual faculty members. Students must make arrangements with a specific faculty member for work on a particular topic before registering. Requires approval from the Director of Graduate Studies and major advisor. Registration is done by the graduate coordinator.
This course will provide students with a final integrative experience. Students will use the skills acquired during the other classes in executing project goals. Students will provide short reports to supervising faculty to ensure that learning objectives are being met. Project must be approved by the advisor.
This coures is 3 credit hours. Students opting for the Master's Project only need to take 4, 3-credit electives in addition to this course to fulfill degree requirements.
This course will provide students with a final integrative experience. Through the thesis students will design, implement, complete and report on significant and original, independent research related to the content learned in the Sustainable Transportation and Logistics, Students will conduct their research under the supervision of their major advisor and a thesis committee.
This coures is 6 credit hours. Students opting for the Master's Thesis as their culminating experience only need to take 3, 3-credit electives in addition to this course to fulfill degree requirements.