Multiple resource leveling in construction systems through variation of activity intensities

1983 ◽  
Vol 30 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Robert C. Leachman
2011 ◽  
Author(s):  
Hsiang-Hsi Huang ◽  
◽  
Jia-Chen Shiu ◽  
Tai-Lin Chen ◽  
◽  
...  

Author(s):  
Adam F. Werner ◽  
Jamie C. Gorman

Objective This study examines visual, auditory, and the combination of both (bimodal) coupling modes in the performance of a two-person perceptual-motor task, in which one person provides the perceptual inputs and the other the motor inputs. Background Parking a plane or landing a helicopter on a mountain top requires one person to provide motor inputs while another person provides perceptual inputs. Perceptual inputs are communicated either visually, auditorily, or through both cues. Methods One participant drove a remote-controlled car around an obstacle and through a target, while another participant provided auditory, visual, or bimodal cues for steering and acceleration. Difficulty was manipulated using target size. Performance (trial time, path variability), cue rate, and spatial ability were measured. Results Visual coupling outperformed auditory coupling. Bimodal performance was best in the most difficult task condition but also high in the easiest condition. Cue rate predicted performance in all coupling modes. Drivers with lower spatial ability required a faster auditory cue rate, whereas drivers with higher ability performed best with a lower rate. Conclusion Visual cues result in better performance when only one coupling mode is available. As predicted by multiple resource theory, when both cues are available, performance depends more on auditory cueing. In particular, drivers must be able to transform auditory cues into spatial actions. Application Spotters should be trained to provide an appropriate cue rate to match the spatial ability of the driver or pilot. Auditory cues can enhance visual communication when the interpersonal task is visual with spatial outputs.


Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 570
Author(s):  
Chi Zhang ◽  
Changyong Liang ◽  
Chao Zhang ◽  
Yiming Ma

Population aging has become an important factor restricting China′s social and economic development. The smart health and elderly care industry has developed rapidly in the past five years. However, the service resources among various elderly service providers are relatively isolated and scattered. In other words, the core management problem in the components of the smart elderly care service ecosystem is how to deal with the relationships of interest among multiple resource agents. Thus, the main contribution of this study is to employ symbiosis theory and the logistic growth model to construct a model of the evolution of the symbiosis of multiple resource agents in the smart elderly care service ecosystem. Then, we carry out a stability analysis, and analyze the evolutionary model of two resource agents′ symbiosis under different values of interdependence coefficients. Finally, we use computer simulations to dynamically simulate the model and comparatively analyze the population density of the hospital–nursing home symbiotic relationship using real cases in China. According to the study, we find that the enterprise goal in the smart elderly care service ecosystem should be to maximize the overall value of the multiple resource agents, and the result of the symbiotic evolution between different resource agents depends on the symbiotic interdependence coefficient, while the resource agent uses different strategies under different symbiosis models. Therefore, regulation is needed to ensure the relative fairness of the distribution of value co-creation in the smart elderly care service ecosystem when the resource agent takes actions that benefit itself. Of course, when the ecosystem is in a reciprocal symbiosis model, each resource agent benefits from the activities of the other resource agents, which is ideal in reality; in other words, the best symbiosis model between the two resource agents should be the similar reciprocal symbiosis model.


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