Designing and managing a supply chain involves decisions such as the selection of suppliers, the location and capacity of warehouse and production facilities, the products, the modes of transportation, supporting information systems, as well as planning and management of all activities along the chain of supplies. Logistics is a part of the supply chain process that plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point-of-origin to the point-of-consumption in order to meet customers' requirements.
Research activities in Supply Chain, Logistics and Transportation include:
- Supplier selection, capacity planning and integrated inventory management.
- Distribution network design, hub location, cross docking, warehousing and transportation.
- Procurement planning of components and raw materials, assembly and distribution scheduling of customizable products in a build-to-order supply chain.
- Establishing inventory replenishment policies to achieve material flow stability throughout the lean supply chain in prominent aerospace companies.
Lean is a management philosophy originated from Toyota Production system which strives to eliminate waste and achieve improved process flow to produce products and render services in the most efficient way.
Research activities in Lean include:
- Designing and validating lean engine assembly lines in an aircraft engine assembly plant.
- Pacing the work within a long production cycle and establishing production flow stability in lean production lines.
- Designing lean patient flows in various types of clinics (Emergency, Outpatient, Inpatient) in major hospitals to reduce patient waiting times while optimizing resource utilization.
Quality and Reliability Management (QRM) are the important metrics that quantify the ability to satisfy a stated need of a product or system. Probabilistic and stochastic modeling tools are predominantly employed to characterize, model and analyze system level failures, decisions on optimal maintenance schedule, Maintenance, Repair & Overhaul (MRO) etc.
Research activities in QRM area includes:
- Development of mathematical models to characterize optimal pricing of warranty and service contracts; design of extended warranty contracts based on varying objectives
- Probabilistic risk assessment; Defect pattern characterization; product design, development, performance and life-cycle management
- Optimal inspection and replacement policy, shock models
- Intelligent process control and other related topics
Healthcare has benefited from numerous advances in diagnosis and treatment, innovative medical devices, and use of advanced technology. Despite these advances, healthcare remains fraught with significant inefficiencies, errors and waste. HSE is an interdisciplinary field that integrates knowledge, methods and solutions from a variety of fields including mathematical modeling, operations research, statistics, information systems, management, and other related fields to solve applied problems.
- Predictive modeling to study disease spread and develop suitable intervention strategies
- Tools to calibrate healthcare operational data for analysis and quality management
- Process engineering in healthcare provider systems
- Development of data driven decision support tools and methodologies that allow development of safe, efficient and cost effective solutions for healthcare delivery
- Work design incorporating the socio-organizational impact of technology adoption
Politics, national security, weather forecasting, medical diagnosis, image and speech recognition, and bioinformatics, are but a few of the fields in which data mining and machine learning have been applied. Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. The main objectives for developing these algorithms include identifying patterns within the available data or making predictions, or both.
Research at AGU focuses on:
- Development of fast and accurate algorithms that help solve predictive tasks whose outcomes are quantitative as well as tasks whose outcomes are qualitative.
- Development of accurate algorithms applied to rare events (imbalanced) data sets
- Applications of data mining methods in various engineering fields.
Mathematical modeling of various resource allocation problems. These problems arise in the areas of operations management, product distribution, job scheduling, etc. and typically require modeling through Linear Programming, Mixed Integer Programming and Multi objective optimization.
Research activities in this area include:
- Development of mathematical models to identify optimal job schedules in a composite manufacturing environment
- Optimal allocation of production capacity to different grades (products) in petrochemical plants.
- Modeling and optimization of convergent and divergent supply chains
- Multi-criteria Decision Making for Sustainability
