Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods

Ioannis Minis (University of the Aegean, Greece), Vasileios Zeimpekis (University of the Aegean, Greece), Georgios Dounias (University of the Aegean, Greece) and Nicholas Ampazis (University of the Aegean, Greece)

DOI: 10.4018/978-1-61520-633-9

ISBN13: 9781615206339|ISBN10: 1615206337|EISBN13: 9781615206346

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Description & Coverage

Description:

Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent.

Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Defining optimum service levels and inventory costs
Demand forecasting
Distribution channel management
Forecasting, sales and customer service
Just in time productivity
Procurement and inventory management
Production planning and scheduling
Production Scheduling
Sales management
Warehousing, transportation and distribution management

Reviews and Testimonials

Standard operational research is now a mature and widely recognized field of applied mathematics and high-level computation. The ever-growing realm of applications and the computing power explosion of the last two decades are driving related research in new exciting directions, continuing to play an important role in modern management engineering. A pioneering position of those real world OR applications holds for supply chain management, a fast growing topic combining computing algorithms either based on discrete mathematics and heuristics or, related to computational intelligence based on evolution, learning and adaptation. To this end, we hope that the present Volume contributes towards the advancement of high quality research in CI solutions for real world OR problems."

– Ioannis Minis, University of the Aegean, Greece; Vasileios Zeimpekis, University of the Aegean, Greece; Georgios Dounias, University of the Aegean, Greece; and Nicholas Ampazis, University of the Aegean, Greece

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Editor/Author Biographies

Ioannis Minis is a Professor in the Department of Financial and Management Engineering of the University of Aegean. He conducts research in design, production and operations systems, including supply chain management. Dr. Minis holds a Ph.D. degree from the University of Maryland in Mechanical Engineering, and has held the positions of Assistant and Associate Professor of Mechanical Engineering at the same University (1988-1997). He is conducting research in Operations for over 20 years and has authored two edited volumes, several book chapters, over 45 journal articles and over 60 articles in conference proceedings. Dr. Minis has been the recipient of the 1993 Earl E. Walker Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers (SME).

Vasileios Zeimpekis is Adjunct Lecturer in the Department of Financial & Management Engineering at the University of the Aegean. He is also Senior Research Officer at the Design, Operations, & Production Systems (DeOPSys) Lab situated at the same University. His interests focus on road transportation & logistics with emphasis on decision support systems and telematics. He has published 3 edited volumes and more than 40 papers in the area of supply chain and transportation. Vasileios holds a BEng(Hons) in Telecommunications Engineering from the University of Essex (UK). He has also been awarded with an MSc in Mobile & Satellite Communications from the University of Surrey (UK) and an MSc with Distinction in Engineering Business Management from the University of Warwick (UK). He earned his PhD from the Department of Management Science & Technology at the Athens University of Economics & Business.

George Dounias holds a Production and Management Engineering Diploma and a Doctoral Diploma both from the Technical University of Crete, Dept. of Production Engineering & Management. Since 2003 he is with the Department of Financial & Management Engineering, University of the Aegean, Chios, Greece (Full Professor since May 2010). He has served as Head of the Department (2008-2010), Head of the Postgraduate Studies in Financial and Management Engineering (2005-2008) and he is also Director of the Management and Decision Engineering Laboratory (2005-today). His research interests are related to Computational & Nature Inspired Intelligence, Management Engineering, Financial Decision Making, Medical Decision Making and Fault Diagnosis of Complex Dynamic Systems. George Dounias has participated in more than 25 national and international funded research programs in the last 20 years. He has published over 150 research reports and papers in journals, books and conferences, within 1993-2010 and he has served as editorial board member, guest editor or reviewer of 28 refereed international journals and as organizing / program committee in more than 60 international conferences, summer schools and special research events.

Nicholas Ampazis holds a BEng (Hons) degree from the Department of Electrical Engineering, Imperial College London (1992), an MSc degree with Distinction in Information Processing and Neural Networks from the Department of Mathematics, King's College London (1995), and a PhD with Distinction from the Department of Electical and Computer Engineering, National Technical University of Athens (2001). From 1996 to 2001 he was a scholar with the Institute of Informatics at the National Center from Scientific Research "Demokritos", Athens, Greece. From 2002 to 2005 he was an adjunct professor at the Department of Financial and Management Engineering (FME), University of the Aegean, and since 2005 he is an assistant professor (computer science) at the same department. He is also the director of the Intelligent Data Exploration and Analysis Laboratory (IDEAL) at the FME department. His research interests include computational intelligence algorithms (with emphasis in neural networks), data mining, information retrieval, recommender systems and supply chain management, and he has published over 40 papers journal and conference papers in these research areas.

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