Mohammad Mehdi Pouria, Abbas Akbarpour, Hassan Ahmadi, Mohammad Reza Tavassoli and Amir Saedi Daryan
Offshore structures are among the structures exposed to fire more often. Most of these structures are likely to be associated with flammable materials. In this research, some of…
Abstract
Purpose
Offshore structures are among the structures exposed to fire more often. Most of these structures are likely to be associated with flammable materials. In this research, some of the structures constructed on top of marine decks have been studied.
Design/methodology/approach
For this purpose, the upper-bound theory of plastic analysis has been used to investigate its collapse behavior. In this way, genetic algorithm has been used for application of the combination of elementary mechanisms in the classic plastic analysis problem.
Findings
The studied structures are optimized by plastic analysis theory before and after the fire and their failure modes are compared with each other. The comparison of the results indicates significant changes in the load factor value, as well as the critical collapse mode of the structure before and after the fire.
Originality/value
Results indicate that the combination of plastic analysis and a genetic algorithm can predict the collapse mode of the structure before and after the fire accurately.
Details
Keywords
Arash Heidari and Nima Jafari Navimipour
The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized…
Abstract
Purpose
The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.
Design/methodology/approach
Systematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.
Findings
In this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.
Research limitations/implications
This research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.
Practical implications
One of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.
Originality/value
This survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.
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Eric Weisz, David M. Herold, Nadine Kathrin Ostern, Ryan Payne and Sebastian Kummer
Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing…
Abstract
Purpose
Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.
Design/methodology/approach
Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.
Findings
We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.
Originality/value
Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.