Scott J. Mason, PhD.

Fluor Endowed Chair of Supply Chain Optimization and Logistics
Professor of Industrial Engineering

Dr. Scott J. Mason

Dr. Scott J. Mason joined the Clemson IE faculty in July 2010 as the inaugural Fluor Endowed Chair in Supply Chain Optimization and Logistics. After working full time in the semiconductor industry, Dr. Mason began his academic career in the Department of Industrial Engineering at the University of Arkansas. At Arkansas, Dr. Mason served as Chair of Graduate Studies for nine years and as Associate Department Head for six years. He advised numerous undergraduate Honors and Master’s theses, as well as doctoral dissertations in the areas of applied operations research and large-scale systems modeling, optimization, and algorithms. Many student research projects evolved directly from Dr. Mason’s practical experience in industry or from local organizations who contacted Dr. Mason with a specific need.

Frequently drawing upon both his industry experience and current consulting projects, Dr. Mason has been able to educate students in both the theoretical and practical aspects of being an industrial engineer, as evidenced by his numerous teaching, research, and service award, including Faculty Member of the Year, in both 2004 and 2008, at Arkansas. A consummate student advocate, Dr. Mason has continued his tradition of working with students to find employment at leading companies and helping companies to identify top engineering talent in Clemson’s IE department for internships, class projects, and full-time employment. In fact, Dr. Mason was selected the Outstanding Advisor in the College of Engineering at Arkansas and was awarded a Faculty Gold Medal at the university level for his commitment to student-focused research.

Dr. Mason is active in industrial engineering professional societies and brings excellent visibility to Clemson through his experience as a Technical Vice President of Networking, Annual Conference General Co-Chair, and Senior Vice President of Continuing Education for the Institute of Industrial and Systems Engineers. Dr. Mason’s extensive network of colleagues and industry personnel have served him well as he leads SmartState program’s Fluor Center of Economic Excellence in Supply Chain and Logistics. These initiatives focus on both multi-disciplinary research projects and the administration and delivery of a distance-based, online Master of Science in Industrial Engineering degree program with special emphasis on capital project supply chains.

SmartState

After moving from video poker-based gaming to an education-focused lottery, the state of South Carolina’s General Assembly had the foresight to establish the South Carolina Centers of Economic Excellence (now SmartState) Program in 2002. SmartState, which was funded in part with proceeds from the South Carolina Education Lottery, created 85 endowed chairs at the state's three public research institutions—Clemson University, the Medical University of South Carolina and the University of South Carolina. Corporate or individual donors were required to match the state’s contributions, dollar-for-dollar, in establishing each endowed chair. The chairs were established in research areas that have/will advance South Carolina's economy, such as Advanced Materials and Nanotechnology; Automotive and Transportation; and Energy and Alternative Fuels.

Clemson’s Department of Industrial Engineering successfully engaged the Fluor Corporation, one of the world's largest publicly-traded engineering, procurement, fabrication, construction (EPFC) and maintenance companies, to match the state of South Carolina’s $2 million contribution. This partnership led to the realization of the $4 million Fluor Endowed Chair in Supply Chain Optimization and Logistics at Clemson. Without the vision and support of Fluor, the state of South Carolina, and some key Clemson industrial engineering faculty colleagues, I would not be at Clemson today—to them, I owe an eternal debt of gratitude.

Current and Former PhD Students

Former Post-Doctoral Research Fellows

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Eghbal Rashidi (2017)

Eghbal Rashidi is a faculty member of the Operations Management and Information Systems department at the Leavy School of Business, Santa Clara University (SCU). Previous to joining SCU, Eghbal was a postdoctoral research fellow at the Industrial Engineering department in Clemson University, working with Dr. Scott Mason on Supply Chain Risk Analysis. Eghbal holds a Ph.D. in Industrial and Systems Engineering from Mississippi State University. His research interests involve developing models and algorithms for large scale optimization problems with applications in supply chain and logistics, transportation, and homeland security.

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Harsha Gangammanavar (2016)

Harsha Gangammanavar is an Assistant Professor in the Engineering Management, Information, and Systems in Bobby B. Lyle School of Engineering at Southern Methodist University. Prior to joining the department, Dr. Gangammanavar was a Postdoctoral Fellow at Clemson University and a Visiting Assistant Professor at the University of Southern California. He received his M.S. in Electrical Engineering and Ph.D. in Operations Research from the Ohio State University, and B.E. degree in Electronics and Communications Engineering from Visvesvaraya Technological University in India. His research interests are in stochastic programming, large-scale optimization, and their applications.

My Research

BMW currently uses an SAP-based Material Requirements Planning (MRP) solution to monitor the ordering, consumption, and replenishment of its parts inventory for Plant Spartanburg. Updating and/or maintaining the data in the MRP system typically is a manual and tedious process that sometimes leads to inaccurate information being populated/captured in the system. Inventory inaccuracy, in the extreme case, could cause production to halt on one or more production lines due to insufficient inventory levels of critical parts that are needed for production.

BMW has asked researchers in the Department of Industrial Engineering at Clemson University (CU) to study this important problem. BMW will provide supply chain data regarding parts, suppliers, and inventory levels for key parts under study at Plant Spartanburg. In addition to gaining a basic understanding of BMW production operations, the CU team will investigate current MRP system functionality and work with BMW to understand previous issues faced, what caused them, and how they were rectified.

The CU team will use state-of-the-art analytics methods such as artificial intelligence, machine learning, clustering algorithms, and/or support vector machines to understand any evident trends or relationships present in BMW’s supply chain data. The CU team will work to identify method(s) for improved data visualization and explore automated methods for updating MRP system information based upon inventory usage.
The supply chain for the delivery of tritium for national priorities relies on many activities, materials, processes, and technologies. This includes aspects outside the Savannah River Site such as the supply of uranium, the production of lithium target pellets, the production of Tritium Producing Burnable Absorber Rods (TPBARs), the conversion of lithium to tritium, and the activities of many suppliers in the supply chain. On the Savannah River Site, tritium is extracted from TPBARs and then processed, packaged, and shipped. Each point in the supply chain has potential risks that include but are not limited to:

• Sole source supplier/single point of failure
• Use of foreign technology/material
• Export control issues
• Critical regulatory policies
• Viability in performance of lower tier suppliers and producers
• Economics and business drivers
• Uncertainty in production capacity, availability/reliability of equipment, transportation, etc.

Savannah River has asked researchers at Clemson University to provide analytical tools and information that will guide decision makers in assuring that this supply chain will continue to meet production requirements. The Clemson team will identify supply chain risks and limitations, provide guidance for mitigation strategies, and enable evaluation of “what if” scenarios and alternatives to the current supply chain.
Using past data from NCAA football games, we developed a tool that assists coaches with making in-game decisions regarding plays to call. Historical data describing the down, distance to gain a first down, field position, offensive formation, defensive front, and other key indicators. Our tool’s objective is to recommend the play that has the highest expected average gain and the highest probability of achieving a first down, based on the situation and defensive formation being faced.
Radiation therapy is one of the main methods of treating cancer. Patients are exposed to internal or external radiation sources with the goal of eradicating cancerous tissue (targets) while sparing healthy structures, or organs-at-risk (OAR), and minimizing the chance of future complications. Probably the most common method of radiation therapy is external beam photon treatment, which is delivered using several popular modalities. Volumetric modulated arc therapy (VMAT) is one of the gold standards of such techniques. VMAT treatment planning is still an unsolved problem, although researchers can get seemingly near-Pareto-optimal plans with respect to dosimetric objective function values. However, there are traditionally not-explicitly-account-for objectives that are not considered in the research spectrum (e.g., aperture shape/size, total MUs, etc.). Commercial treatment planning systems (TPSs) have begun to have added penalties for promoting aperture size, but these still need to be tuned with other competing dosimetric objectives. We study the problem of VMAT auto-planning, where, given patient geometry and some dosimetric goals, we generate a high-quality, deliverable, desirable-aperture-size VMAT treatment plan.
Supply chains evolve over time: they expand via construction and/or acquisitions, and contract via facility closures and/or cost-cutting decisions. Businesses operate in an uncertain world, where decisions regarding supply chain network design must be made despite the possibility of unforeseen future events that may disrupt or damage the supply chain. We study decision support models and methodologies for making network design decisions that promote successful current and future supply chain operations. Utilizing multi-criteria optimization, we seek to find a tradeoff between the overall operational costs of the supply chain, as well as its ability to operate under disruptions.
The optimal power flow (ACOPF) problem is one of the most fundamental optimization problems in the economic and reliable operation of electric power systems. Since the ACOPF formulation was introduced in 1962, developing efficient solution algorithm for the ACOPF has remained an active research field. The main challenges associated with solving the ACOPF include: a) nonconvex and nonlinear mathematical models of ac physics, b) large-scale power grids, and c) limited computation time available in real-time dispatch applications. A fast and robust algorithm for obtaining high quality solutions could improve the operational performance of power systems. As a result, small increases in dispatch efficiency could save billions of dollars per year. My research interests are focused on proposing a lower bounding algorithm to strong the convex relaxations for this problem. The approach is based on a two-stage algorithm that uses methods tailored to OPF problems. In the first stage, we tighten bounds for the voltage and phase-angle variables. The second stage adaptively partitions convex envelopes of the ACOPF into piecewise convex regions. We illustrate the strengths of the algorithms on benchmark AC-OPF test cases. The computational results show that our novel algorithms reduce the best-known optimality gap for some hard ACOPF cases and solve some large-scale instances which are intractable by the global optimization solver.
In recent years, there have been increasing concerns about the impacts of geomagnetic disturbances (GMDs) on electrical power systems. Geomagnetically-induced currents (GICs) can saturate transformers, induce hotspot heating and increase reactive power losses. These effects can potentially cause catastrophic damage to transformers and severely impact the ability of a power system to deliver power. To address this problem, we work on modeling GIC impacts to power systems and use this model to derive an optimization problem that protects power systems from GIC impacts through line switching, generator dispatch, and load shedding. Further, there is often uncertainty in predictions of direction and strength of the GMD event, thus our work also includes the development of methods that produce solutions that are robust to errors in predictions.
Consider a paint manufacturing firm whose customers typically place orders for two or more products simultaneously: liquid primer, top coat paint, and/or undercoat paint. Each product belongs to an associated product family that can be batched together during the manufacturing process. Meanwhile, each product can be split into several sublots so that overlapping production is possible in a two-stage hybrid flow shop. Various numbers of identical capacitated machines operate in parallel at each stage. We present a mixed-integer programming (MIP) to analyze this novel integrated batching and lot streaming problem with variable sublots, incompatible job families, and sequence-dependent setup times. The model determines the number of sublots for each product, the size of each sublot, and the production sequencing for each sublot such that the sum of weighted completion time is minimized. Several numerical example problems are presented to validate the proposed formulation and to compare results with similar problems in the literature. Furthermore, an experimental design based on real industrial data is used to evaluate the performance of proposed model.
Joint chance constraint (JCC) problems are challenging optimization problems. A natural practice to model them is to use sample approximation, which describes underlying uncertainty distribution with a finitely supported scenario set. The approximation imposes a non-convex feasible region by requiring a disjunctive scenario space to be feasible for the set of constraints. We focus on JCC with finite support in two-stage structure and allows recourse stage to be discrete. The problem is further decomposed based on scenarios to explore theoretical properties. These properties motivate an algorithm design using heuristic column generation to perform pricing on the dedicated decomposed structure without dependence of the strong duality theorem. We compare our algorithm with a state-of-the-art commercial solver to show performance improvements on two demonstrating problems. Furthermore, we study how different generation of columns can influence the algorithmic behavior differently.

Classes

Here you'll find a list of the courses I teach. Please click the course you're interested in below to view its respective syllabus or webpage.

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