Machine intelligence is increasingly entering roles that were until recently dominated by human intelligence. As humans now depend upon machines to perform various tasks and operations, there appears to be a risk that humans are losing the necessary skills associated with producing competitively advantageous decisions. Therefore, this research explores the emerging area of human versus machine decision-making. An illustrative engineering case involving a joint machine and human decision-making system is presented to demonstrate how the outcome was not satisfactorily managed for all the parties involved. This is accompanied by a novel framework and research agenda to highlight areas of concern for engineering managers. We offer that the speed at which new human-machine interactions are being encountered by engineering managers suggests that an urgent need exists to develop a robust body of knowledge to provide sound guidance to situations where human and machine decisions conflict. Human-machine systems are becoming pervasive yet this research has revealed that current technological approaches are not adequate. The engineering insights and multi-criteria decision-making tool from this research significantly advance our understanding of this important area.
The conventional Design-Bid-Build (DBB) construction contracting method has had various drawbacks exposed in highway construction practice, including lack of communication, inefficient design, antagonizing relationships, and increased disputes. To mitigate the negative aspects of DBB, several alternative contracting methods and alternative project delivery systems have been devised and introduced to the industry over the past 30 years. Five such innovations were tested by a research team from the University of Florida under the sponsorship of the Florida Department of Transportation (FDOT). To perform a realistic assessment, this study categorized FDOT projects built between 2006 and 2015 into groups according to current contract amounts. Both absolute and relative metrics were defined and employed. For comparison purposes, a collective analysis on all gathered data was performed. Additionally, the influence of outliers on the results was examined. The results showed that analyses based on individual cost categories are more convincing because large projects tend to impose stronger influence on the analyses. In addition, outliers must be identified and screened to reach realistic and reliable conclusions. With regard to the actual performance of the contracting methods, each performs differently within different cost categories.
At present, the further development of new energy vehicles industry is hindered by limited consumer’s participation or capital investment. Therefore, a new multilateral model of cross-industry alliance needs to arise. The advanced charging technology of Internet-distributed mobile energy can link up with many market participants closely and form an effective and multilateral win-win cross-industry alliance. This new industry alliance can realize unexpected multiple goals, for example, (1) consumers who have purchased new energy vehicles can avail free charging; (2) potential vehicle buyers can be encouraged to use new energy vehicles; (3) the new energy vehicle manufacturers can expand their production scale; (4) the new energy vehicles sellers (4S shop) can expand their sales volume; (5) large shopping malls can attain more income; (6) financial institutions can absorb more deposits; (7) governments can further promote low-carbon traffic. This article analyzes the cross-industry alliance and its forming mechanism.This work was funded by the China Scholarship Council.
This work was funded by the China Scholarship Council.
This study explores the use of augmented reality smart glasses (ARSGs) by physicians and their adoption of these products in the Turkish medical industry. Google Glass was used as a demonstrative example for the introduction of ARSGs. We proposed an exploratory model based on the technology acceptance model by Davis. Exogenous factors in the model were defined by performing semi-structured in-depth interviews, along with the use of an expert panel in addition to the technology adoption literature. The framework was tested by means of a field study, data was collected via an Internet survey, and path analysis was used. The results indicate that there were a number of factors to be considered in order to understand ARSG adoption by physicians. Usefulness was influenced by ease of use, compatibility, ease of reminding, and speech recognition, while ease of use was affected by ease of learning, ease of medical education, external influence, and privacy. Privacy was the only negative factor that reduced the perceived ease of use, and was found to indirectly create a negative attitude. Compatibility emerged as the most significant external factor for usefulness. Developers of ARSGs should pay attention to healthcare-specific requirements for improved utilization and more extensive adoption of ARSGs in healthcare settings. In particular, they should focus on how to increase the compatibility of ARSGs. Further research needs to be conducted to explain the adoption intention of physicians.
Buildings are known to significantly affect the global carbon emissions throughout their life cycle. To mitigate carbon emissions, investigation of the current performance of buildings with regard to energy consumption and carbon emissions is necessary. This paper presents a process-based life cycle assessment methodology for assessing carbon emissions of buildings, using a multi-storey reinforced concrete building in a Sri Lankan university as a case study. The entire cradle-to-grave building life cycle was assessed and the life span of the building was assumed as 50 years. The results provide evidence of the significance of operation and material production stages, which contributed to the total carbon emissions by 63.22% and 31.59% respectively. Between them, the main structural materials, concrete and reinforcement steel made up 61.91% of the total carbon emitted at the material production stage. The life cycle carbon emissions of the building were found to be 31.81 kg·m-2 CO2 per year, which is comparable with the values obtained in similar studies found in the literature. In minimizing the life cycle carbon emissions, the importance of identifying control measures for both building operation and material production at the early design stage were emphasized. Although the other life cycle stages only contributed to about 5.19% of the life cycle carbon emissions, they should also receive attention when formulating control strategies. Some of the recommended strategies are introducing energy efficiency measures in building design and operation, using renewable energy for building operation and manufacturing of materials, identifying designs that can save mass material quantities, using alternative materials that are locally available in Sri Lanka and implementing material reuse and recycling. This study is one of the first to undertake a life cycle carbon emissions assessment for a building in the Sri Lankan context, with the hope of facilitating environmentally-friendly buildings and promoting sustainable construction practices in the country.
Understanding the holistic relationship between refinery production scheduling (RPS) and the cyber-physical production environment with smart scheduling is a new question posed in the study of process systems engineering. Here, we discuss state-of-the-art RSPs in the crude-oil refining field and present examples that illustrate how smart scheduling can impact operations in the high-performing chemical process industry. We conclude that, more than any traditional off-the-shelf RPS solution available today, flexible and integrative specialized modeling platforms will be increasingly necessary to perform decentralized and collaborative optimizations, since they are the technological alternatives closer to the advanced manufacturing philosophy.
The empirical Complex Model developed by the US Environmental Protection Agency (EPA) is used by refiners to predict the toxic emissions of reformulated gasoline with respect to gasoline properties. The difficulty in implementing this model in the blending process stems from the implicit definition of Complex Model through a series of disjunctions assembled by the EPA in the form of spreadsheets. A major breakthrough in the refinery-based Complex Model implementation occurred in 2008 and 2010 through the use of generalized disjunctive and mixed-integer nonlinear programming (MINLP). Nevertheless, the execution time of these MINLP models remains prohibitively long to control emissions with our online gasoline blender. The first objective of this study is to present a new model that decreases the execution time of our online controller. The second objective is to consider toxic thresholds as hard constraints to be verified and search for blends that verify them. Our approach introduces a new way to write the Complex Model without any binary or integer variables. Sigmoid functions are used herein to approximate step functions until the measurement precision for each blend property is reached. By knowing this level of precision, we are able to propose an extremely good and differentiable approximation of the Complex Model. Next, a differentiable objective function is introduced to penalize emission values higher than the threshold emissions. Our optimization module has been implemented and tested with real data. The execution time never exceeded 1 s, which allows the online regulation of emissions the same way as other traditional properties of blended gasoline.
In this work, we examine the impact of crude distillation unit (CDU) model errors on the results of refinery-wide optimization for production planning or feedstock selection. We compare the swing cut+ bias CDU model with a recently developed hybrid CDU model (Fu et al., 2016). The hybrid CDU model computes material and energy balances, as well as product true boiling point (TBP) curves and bulk properties (e.g., sulfur % and cetane index, and other properties). Product TBP curves are predicted with an average error of 0.5% against rigorous simulation curves. Case studies of optimal operation computed using a planning model that is based on the swing cut+ bias CDU model and using a planning model that incorporates the hybrid CDU model are presented. Our results show that significant economic benefits can be obtained using accurate CDU models in refinery production planning.