3,630 research outputs found
A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems
The concept of sustainability is defined as composed of three pillars: social, environmental, and economic. Social sustainability implies a commitment to equity in terms of several âinterrelated and mutually supportiveâ principles of a âsustainable societyâ; this concept includes attitude change, the Earthâs vitality and diversity conservation, and a global alliance to achieve sustainability. The social and environmental aspects of sustainability are related in the way sustainability indicators are related to âquality of lifeâ and âecological sustainabilityâ. The increasing interest in green and sustainable products and production has influenced research interests regarding sustainable scheduling problems in manufacturing systems. This study is aimed both at reducing pollutant emissions and increasing production efficiency: this topic is known as Green Scheduling. Existing literature research reviews on Green Scheduling Problems have pointed out both theoretical and practical aspects of this topic. The proposed work is a critical review of the scientific literature with a three-pronged approach based on keywords, taxonomy analysis, and research mapping. Specific research questions have been proposed to highlight the benefits and related objectives of this review: to discover the most widely used methodologies for solving SPGs in manufacturing and identify interesting development models, as well as the least studied domains and algorithms. The literature was analysed in order to define a map of the main research fields on SPG, highlight mainstream SPG research, propose an efficient view of emerging research areas, propose a taxonomy of SPG by collecting multiple keywords into semantic clusters, and analyse the literature according to a semantic knowledge approach. At the same time, GSP researchers are provided with an efficient view of emerging research areas, allowing them to avoid missing key research areas and focus on emerging ones
The State-of-the-Art Survey on Optimization Methods for Cyber-physical Networks
Cyber-Physical Systems (CPS) are increasingly complex and frequently
integrated into modern societies via critical infrastructure systems, products,
and services. Consequently, there is a need for reliable functionality of these
complex systems under various scenarios, from physical failures due to aging,
through to cyber attacks. Indeed, the development of effective strategies to
restore disrupted infrastructure systems continues to be a major challenge.
Hitherto, there have been an increasing number of papers evaluating
cyber-physical infrastructures, yet a comprehensive review focusing on
mathematical modeling and different optimization methods is still lacking.
Thus, this review paper appraises the literature on optimization techniques for
CPS facing disruption, to synthesize key findings on the current methods in
this domain. A total of 108 relevant research papers are reviewed following an
extensive assessment of all major scientific databases. The main mathematical
modeling practices and optimization methods are identified for both
deterministic and stochastic formulations, categorizing them based on the
solution approach (exact, heuristic, meta-heuristic), objective function, and
network size. We also perform keyword clustering and bibliographic coupling
analyses to summarize the current research trends. Future research needs in
terms of the scalability of optimization algorithms are discussed. Overall,
there is a need to shift towards more scalable optimization solution
algorithms, empowered by data-driven methods and machine learning, to provide
reliable decision-support systems for decision-makers and practitioners
Growing Story Forest Online from Massive Breaking News
We describe our experience of implementing a news content organization system
at Tencent that discovers events from vast streams of breaking news and evolves
news story structures in an online fashion. Our real-world system has distinct
requirements in contrast to previous studies on topic detection and tracking
(TDT) and event timeline or graph generation, in that we 1) need to accurately
and quickly extract distinguishable events from massive streams of long text
documents that cover diverse topics and contain highly redundant information,
and 2) must develop the structures of event stories in an online manner,
without repeatedly restructuring previously formed stories, in order to
guarantee a consistent user viewing experience. In solving these challenges, we
propose Story Forest, a set of online schemes that automatically clusters
streaming documents into events, while connecting related events in growing
trees to tell evolving stories. We conducted extensive evaluation based on 60
GB of real-world Chinese news data, although our ideas are not
language-dependent and can easily be extended to other languages, through
detailed pilot user experience studies. The results demonstrate the superior
capability of Story Forest to accurately identify events and organize news text
into a logical structure that is appealing to human readers, compared to
multiple existing algorithm frameworks.Comment: Accepted by CIKM 2017, 9 page
Dynamic changes and multi-dimensional evolution of portfolio optimization
Although there has been an increasing number of studies investigate portfolio optimization from different perspectives, few
attempts could be found that focus on the development trend
and hotspots of this research area. Therefore, it motivates us to
comprehensively investigate the development of portfolio optimization research and give some deep insights into this knowledge domain. In this paper, some bibliometric methods are
utilized to analyse the status quo and emerging trends of
portfolio optimization research on various aspects such as
authors, countries and journals. Besides, âtheoriesâ, âmodelsâ and
âalgorithmsâ, especially heuristic algorithms are identified as the
hotspots in the given periods. Furthermore, the evolutionary analysis tends to presents the dynamic changes of the cutting-edge
concepts of this research area in the time dimension. It is found
that more portfolio optimization studies were at an exploration
stage from mean-variance analysis to consideration of multiple
constraints. However, heuristic algorithms have become the driving force of portfolio optimization research in recent years. Multidisciplinary analyses and applications are also the main trends of
portfolio optimization research. By analysing the dynamic changes
and multi-dimensional evolution in recent decades, we contribute
to presenting some deep insights of the portfolio optimization
research directly, which assists researchers especially beginners to
comprehensively learn this research field
Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks
Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy
Review on Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Localization in Wireless Sensor Networks
Wireless sensor network (WSN) is an evolving research topic with potential applications. In WSN, the nodes are spatially distributed and determining the path of transmission high challenging. Localization eases the path determining process between source and destination. The article, describes the localization techniques based on wireless sensor networks. Sensor network has been made viable by the convergence of Micro Electro- Mechanical Systems technology. The mobile anchor is used for optimizing the path planning location-aware mobile node. Two optimization algorithms have been used for reviewing the performacne. They are Grey Wolf Optimizer(GWO) and Whale Optimization Algorithm(WOA). The results show that WOA outperforms in maximizing the localization accuracy
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