Operational Research: An International Journal
Special Issue on “Optimization of Ambient Intelligence Systems”

Aim
Ambient intelligence (AmI) is a future vision in which an environment supports the people inhabiting it in an unobtrusive, interconnected, adaptable, dynamic, embedded, and intelligent way. In this vision, an environment is sensitive to the needs of its inhabitants and capable of anticipating their needs and behavior. There are various forms of AmI systems including smart home, smart factories, smart shops, mobile guides, virtual tours, ubiquitous health care systems, online social networks, among others.
Optimizing an AmI system is a controversial problem. Numerous AmI systems involve human decision-making processes, such as deciding whether to follow the results of an online restaurant recommendation system. However, human decision-making is not strictly optimizing in an economical and mathematical sense. In addition, representing people’s subjective feelings by using a simple scale, as performed in several other fields, is inappropriate. Therefore, an AmI system optimization problem cannot be resolved simply by applying heuristics.
Optimizing an AmI system is also a difficult task. First, bulk information may need to be processed, which renders the optimization model extremely large. In addition, such data are dynamic and often incomplete, and this phenomenon poses a challenge to the adaptability and robustness of the optimization model. Furthermore, users’ preferences for the recommended service are unclear, vague, inconsistent, and difficult to quantify. Setting a single objective function that is applicable to everyone is thus a difficult task. In addition, cultural differences also considerably influence optimizing an AmI system. This implies that the relationship among the variables in the optimization model may differ according to culture. Furthermore, data incompleteness is another problem. In most cases, users are unwilling or find it inconvenient to answer all the questions, for example, when in need of a distant emergency care. However, the system must still assist the user by making decisions based on incomplete information.
In most AmI systems, only a few (or countable) alternatives are available, thus forming a discrete feasible region with limited solutions. Most problems of optimizing AmI systems have been formulated as mixed integer-linear or integer-nonlinear programming problems. This special issue is intended to provide technical details of the optimization of AmI systems and the corresponding applications. These details will hold great interest for researchers in ambient intelligence, optimization, system science, operations research, information management, artificial intelligence, and computational intelligence, as well as for practicing managers and engineers. This special issue features a balance between state-of-the-art research and practical applications. This special issue also provides a forum for researchers and practitioners to review and disseminate quality research work on optimizing AmI systems and the critical issues for further development.

Topics
Topics of interest include, but are not limited to:
- Optimization problems in AmI systems
- Optimization for smart home, smart factories, smart shops, and virtual tours
- Optimized location-aware services, ubiquitous health care systems, and online social networks
- New techniques for solving mixed integer-linear or integer-nonlinear programming optimization problems
- Efficient algorithms and heuristics for solving AmI optimization problems
- Group decision making and optimization in AmI
- Parallel, distributed, and cloud computing for AmI
- Simulation-based optimization of an AmI system
- Case studies
- Other related topics

Target Dates
Submission Deadline: October 31, 2016
Notification of the Initial Decision: January 31, 2017
Notification of Acceptance: April 30, 2017
Final Paper Due: June 30, 2017

Submission Guidelines
Quality and originality of the contribution are the main acceptance criteria. Manuscripts must be submitted via the online submission system:
https://www.editorialmanager.com/orij/default.aspx
For journal information and author guidelines, please visit
http://www.springer.com/business+%26+management/operations+research/journal/12351?detailsPage=editorialBoard

Guest Editors
Toly Chen, Ph.D.
Founding President
Ambient Intelligence Association of Taiwan
Distinguished Professor
Department of Industrial Engineering and Systems Management
Feng Chia University
100, Wenhwa Rd., Seatwen, Taichung City, Taiwan
tolychen@ms37.hinet.net; tcchen@fcu.edu.tw
http://tolychen.myweb.hinet.net

Wanpracha Art Chaovalitwongse, Ph.D.
Professor of Industrial and Systems Engineering, College of Engineering
Professor of Radiology (joint), School of Medicine, UW Medical Center
Associate Director of Integrated Brain Imaging Center (IBIC)
University of Washington, Seattle
artchao@uw.edu
http://faculty.washington.edu/artchao/WAC.html

I-Hsuan Hong, Ph.D.
Associate Professor
Institute of Industrial Engineering
National Taiwan University
1, Sec. 4, Roosevelt Rd., Taipei 106, Taiwan
Phone: +886-2-3366-9507
Fax: +886-2-2362-5856
ihong@ntu.edu.tw
http://www.ie.ntu.edu.tw/ihong/