ASOS is the research, teaching, collaboration and consulting outlet of Dr. Egilmez.

ASOS was founded as a research lab in spring 2017 in the Industrial and Systems Engineering program, at University of New Haven. It is currently housed in Plaster School of Business and Entrepreneurship, at Lindenwood University.

The word ASOS was inspired by the famous city, Assos, where Aristotle started the first Academy, “Academy of Assos”, which is currently located in the city of Çanakkale, Turkey.

Mission: ASOS’ mission is established on the following four pillars:

Each letter of ASOS represents the broad research and professional area that we work on and have expertise in, namely: A: Analytics, S: Sustainability, O: Optimization, and S: Simulation.

1) To provide analytical solutions to the complex problems of manufacturing and service industries, as well as local and global nonprofit and government organizations
2) To recruit, and train undergraduate & graduate students from diverse backgrounds and enable them to become excellent learners and industry professionals
3) To contribute to the advancement of the state-of-art in analytics, sustainability, optimization, and simulation research domains through academic publications, presentations, collaborations, grants and scholarships
4) To collaborate with academics and researchers from international and domestic institutions.

Vision: Supporting growth of academic youngsters and young spirits and working with local/global partner colleagues, professionals, and nonprofit organizations.

Following is a list of detailed explanations about the research ares in terms of the problem domains and research methods:

ANALYTICS

In the “ANALYTICS” field, we aim to provide decision support to the local and global issues, where data analytics, machine learning, parametric and non-parametric statistical modeling and analysis tools are developed and adopted to assist with solving complex problems related to social, environmental, and economic aspects of engineered systems and make environmentally benign, cost-effective, and socially acceptable policies. We typically use the following methods and software:

• R
• SAS Enterprise miner
• SAS Studio
• Automated Neural Networks with IBM Statistica
• Predictive and classification modeling
• Parametric/Nonparametric statistical modeling and analysis

SUSTAINABILITY

In the “SUSTAINABILITY” field, we aim to study problems of multidisciplinary domains including manufacturing, supply chain, energy, food & agriculture, transportation and built environment from sustainability point of view by using novel research methods such as life cycle assessment (LCA), data envelopment analysis (DEA), system dynamics (SD), carbon, energy, water and ecological footprint analysis, multi-criteria decision making, goal programming and fuzzy set theory. Sustainability is a very broad domain, which needs to be studied thoroughly with quantitative decision support methods as well as qualitative research. Following problem domains have been central area of interest in sustainability research:

• Manufacturing industries’ sustainability assessment
• Sustainability assessment of agricultural and food production systems
• Transportation sustainability assessment: LCA and eco-efficiency analysis
• Carbon, energy, water nexus
• Green buildings
• Sustainable infrastructure systems

OPTIMIZATION

In the OPTIMIZATION field, we aim to develop intelligent and efficient ways to solve large scale optimization problems that arise in supply chain management, transportation, logistics, manufacturing, health care, and homeland security by utilizing cutting edge solution techniques such as mixed integer programming, constraint programming, Markov decision processes and hybrid methods.

SIMULATION

In the “SIMULATION” field, we aim to provide solutions to the research and industry problems, where simulation modeling can be used to understand the existing systems’ behavior and performance as well as test and simulate the pre-defined investment and performance improvement strategies.

In terms of the simulation methods, we are able to develop Monte-Carlo, discrete event simulation (DES), dynamic simulation (SD), and agent-based simulation (ABS) models depending on the complexity of the system and the research objectives. Such modeling approaches are crucial to understand how complex systems operate and test improvement strategies on a risk free environment. Following are example problem domains we have had research and industry projects completed:

• Manufacturing system and layout design
• Production planning, inventory control, and scheduling
• Sustainable transportation
• Green buildings