Chen Tct Art Chaovalitwongse W Hong Ih Oper Res Int J
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 surround is sensitive to the needs of its inhabitants and capable of anticipating their needs and behavior. In that location are diverse 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 organisation is a controversial problem. Numerous AmI systems involve human being controlling processes, such every bit deciding whether to follow the results of an online eating house recommendation arrangement. However, homo controlling is not strictly optimizing in an economical and mathematical sense. In addition, representing people's subjective feelings by using a uncomplicated scale, every bit performed in several other fields, is inappropriate. Therefore, an AmI system optimization trouble 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 miracle poses a claiming 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 part that is applicable to anybody is thus a difficult task. In addition, cultural differences also considerably influence optimizing an AmI system. This implies that the human relationship amongst the variables in the optimization model may differ according to culture. Furthermore, data incompleteness is another problem. In most cases, users are unwilling or discover information technology inconvenient to respond all the questions, for example, when in need of a afar emergency care. However, the system must still assist the user past 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 equally mixed integer-linear or integer-nonlinear programming bug. This special issue is intended to provide technical details of the optimization of AmI systems and the respective applications. These details will agree great interest for researchers in ambient intelligence, optimization, system science, operations research, data management, artificial intelligence, and computational intelligence, as well every bit for practicing managers and engineers. This special issue features a balance between state-of-the-art research and practical applications. This special issue besides provides a forum for researchers and practitioners to review and disseminate quality research piece of work on optimizing AmI systems and the critical bug for farther development. Afterward a strict review process, seven articles from researchers around the earth were accepted.
A smart hospital can react to emergencies and unexpected events in real time. However, emergencies disrupt the established schedules of operating rooms in a smart hospital. This trouble needs to exist addressed, so as to salvage as many lives as possible. To this end, in the starting time paper, Al-Refaie et al. formulated three optimization models to optimize the schedules of operating rooms by considering unexpected events, thereby incorporating the surgeries of emergent patients into the established schedules.
Job sequencing and scheduling has been considered as i of the basic intelligences of a smart factory. In the second paper, Lin solved 2 sequencing and scheduling problems in a factory with correlated parallel machines: ane minimizes the makespan; the other considers the release times in minimizing the total weighted tardiness. Both problems are theoretically NP-hard. In improver, a variety of levels and combinations of machine correlations and job correlations in the processing times are taken into account. Lin formulated and optimized the mathematical programming models of the two problems. A co-operative-and-bound algorithm was likewise proposed to facilitate the solution finding.
In the 3rd paper, Lin et al. tried to resolve a trouble in mobile hotel recommendation—some users chose dominated hotels instead of the recommended hotels. This problem is difficult to resolve because at that place is no reason to recommend a hotel that is inferior to some other in all aspects. To accost this problem, they added an artificial dimension to each hotel to model unknown personal preferences. The weights assigned to all of the dimensions were derived past solving an integer-nonlinear programming (INLP) problem aimed at maximizing the successful recommendation charge per unit for the historical information. The results of a regional experiment supported the effectiveness of the proposed methodology.
Operating rooms (ORs), one of the most crucial hospital resources that generate the highest costs, are decumbent to bottlenecking. In the fourth paper, Al-Refaie et al. tried to optimize the multiple-period scheduling of patients in the ORs and intensive care units (ICUs) of a hospital. To this end, ii mathematical programming models were proposed and solved to determine the start times and sequences of patient surgeries, start times of the recovery procedures, and ICU bed assignments. For the ORs, the total cost of hospitalization, overtime, undertime, and cancelation was minimized while considering patient prioritization and satisfaction. For the ICUs, the total overtime cost was minimized.
To increase the ecological sustainability of manufacturing, enhancing the yield of each product is a critical job that eliminates waste matter and increases profitability. An as crucial job is to estimate the future yield of each product so that the majority of manufactory capacity tin can be allocated to products that are expected to accept higher yields. To this end, Chen and Wang proposed a fuzzy collaborative intelligence (FCI) approach. In the FCI approach, each skilful constructs an artificial neural work (ANN) to fit an uncertain yield learning procedure for estimating the futurity yield with a fuzzy value. Then, fuzzy intersection is applied to aggregate the fuzzy yield estimates from different experts. Co-ordinate to the experimental results, the proposed methodology outperformed 5 existing methods in improving the estimation accurateness.
Most smart systems are automated operation systems. The monitoring and checking of such systems is a critical task to make judgments and provide solutions. To fulfill this chore, Liu et al. developed an intelligent fuzzy control organisation to evaluate and improve the performance of a supervisor. In improver, an alert bespeak was generated by the intelligent fuzzy command organisation to remind the supervisor. The experimental results showed that the upshot of the intelligent fuzzy control organization on improving the supervisory functioning was significant.
Factory simulation is another intelligence of a smart manufacturing plant. Still, the big amount of money, time, efforts, and know-how required for conducting a factory simulation study force a factory to pursue the persistent application of the manufacturing plant simulation model, i.due east. the sustainability of the factory simulation model. In the terminal paper, Chen et al. proposed a multi-granularity approach to guess the sustainability of a manufactory simulation model based on short-term evidences. Co-ordinate to the results of a simulation experiment, the multi-granularity approach reduced the input space by 89% and maintained a very high estimation accuracy.
I would like to thank Operational Research: An International Journal Editor-in-Chief, Nikolaos Matsatsinis, for fully supporting the release of this special issue. I am likewise grateful to the contributors who shared their research as well as to the reviewers who spared their valuable time to review papers. I would as well like to thank the journal's staff. Without their back up and professional assistance, prepublication would not have been possible.
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Chen, TC.T., Fine art Chaovalitwongse, West. & Hong, IH. Optimization of ambient intelligence systems. Oper Res Int J 18, 575–577 (2018). https://doi.org/10.1007/s12351-018-0422-1
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DOI : https://doi.org/10.1007/s12351-018-0422-1
Source: https://link.springer.com/article/10.1007/s12351-018-0422-1