scholarly journals Enhancing disaster mutual assistance decisions with machine learning: case of electricity utilities

2020 ◽  
Vol 16 (4) ◽  
pp. 281
Author(s):  
Mohammad Ali Tofighi ◽  
Ali Asgary ◽  
Ghassem Tofighi
2019 ◽  
Vol 36 ◽  
pp. 101110 ◽  
Author(s):  
Chun Man Clement Wan ◽  
Alvaro Nosedal-Sanchez ◽  
Jenaro Nosedal-Sanchez ◽  
Ali Asgary ◽  
Ben Pantin

2017 ◽  
Vol 26 (2) ◽  
pp. 230-240 ◽  
Author(s):  
Ali Asgary ◽  
Ben Pantin ◽  
Bahareh Emamgholizadeh Saiiar ◽  
Jianhong Wu

Purpose Disaster mutual assistance (DMA) or mutual aid is a reciprocal arrangement between organizations that permits and prearranges one company to access resources from another company to recover from disaster impacts faster. As a practical tool to access response resources quickly, DMA can be an important element of an effective emergency management process, but the decision to provide (or not to provide) DMA is challenging and involves a number of factors. The purpose of this paper is to present the results of a study conducted to identify DMA decision criteria and their weights based on electricity companies operating in North America. Design/methodology/approach The authors employed a combination of Delphi and analytical hierarchy process (AHP) methods. Delphi method identified the decision criteria that should be considered before electricity utilities enact their DMA agreements. A standard AHP calculated the weights of identified DMA criteria. Findings In total, 11 criteria were identified and classified into three main groups: responding criteria, requesting criteria and disaster criteria. Of the 11, “Emergency Conditions” within the responding criteria group, “Extent of Damage” of the requesting criteria group, and “Size of Disaster”, associated with the disaster criteria group, had the highest weight. Three other factors (“Work Safety Practice”, “Natural Hazards” and “Availability of Resources”) carried a noticeable weight difference, while the remaining factors were weighted relatively lower. Practical implications At present, a decision to provide mutual assistance is highly subjective, based on “gut feel”, and dependent on interpersonal relationships between the requestor and the provider. However, mobilizing and dispatching electricity industry crews is a risky and costly operation for both requesting and responding companies and requires careful assessment for which a cost-benefit threshold has not been developed. This cost-benefit perspective is often frowned upon owing to the intended altruistic nature of DMA agreements and its influence on decision makers. The developed criteria in this study are intended to assist electricity companies in making a more informed and quantifiable decision when deliberating a request for mutual assistance. These criteria may also be used by assistance-requesting companies to better identify electricity companies that are more likely to provide assistance to them. Originality/value This study contributes to the literature by examining the current state of DMA in electricity utilities, identifying decision criteria and weighing such criteria to enable electricity companies in making more objective decisions, thereby, increasing the overall effectiveness of their disaster management process.


2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.


2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  

Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols

Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  

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