AnalysingCausal dependencies of composite service resilience in cloud manufacturing using resource-based theory and DEMATEL method

Namjoo, Mohammad Reza; Keramati, Abbas

doi:10.1080/0951192x.2018.1493231

Cited by 15 publications

(7 citation statements)

References 135 publications

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“…The DEMATEL method [ 47 ] is used to explore the interrelationship between the decision criteria and identify the key considerations pertinent to reshoring decisions to help understand the system underpinnings of the decision [ 48 ]. DEMATEL has no cut-off value for the number of respondents [ 49 ]; hence, it can offer reliable outcomes using inputs from a limited number of experts [ 50 ]. The subjectivity of experts’ opinions makes the DEMATEL outcomes susceptible to uncertainty, imprecision, and vagueness, because the information may be filtered through the responders’ perception despite using realistic and unbiased data [ 51 ].…”

Section: Data Collection and Processingmentioning

confidence: 99%

Reshoring Decisions for Adjusting Supply Chains in a Changing World: A Case Study from the Apparel Industry

Pourhejazy

Ashby

2021

IJERPH

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Global conditions for manufacturing are evolving rapidly and the myopic financial factors that once made overseas locations attractive for offshoring are now in favor of revising it. Besides, the COVID-19 pandemic has highlighted the need for restoring the previously offshored competencies. As a strategic decision, reshoring requires a balance of short- and long-term financial and non-financial considerations. This study extends the reshoring literature by exploring the underpinnings of the decision. For this purpose, the extended fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to study the interrelationship among the decision criteria and explore the sequential effect of the prominent criteria on reshoring decisions. Data from the UK apparel industry is used as a baseline to provide insights for other industry situations. Findings are supportive of the supply process complexity as the prominent considerations with the highest potential impact on the financial criterion. Along with supply process complexity, environmental sustainability appears to have had the highest influence on cost-efficiency as the major driver of past offshoring decisions. Overall, the research findings provide insights for deeper analysis of the manufacturing location decisions for a globalized setting.

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Section: Data Collection and Processingmentioning

confidence: 99%

Reshoring Decisions for Adjusting Supply Chains in a Changing World: A Case Study from the Apparel Industry

Pourhejazy

Ashby

2021

IJERPH

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“…Lan et al [38] proposed a novel approach to obtain the local composite service dependencies [39] and their discontinuous dependencies through call chain analysis. This approach divided service mining into four steps: data aggregation, service dependency set aggregation counting, service local dependency mining, and discontinuous dependency mining.…”

Section: Call Chain Analysismentioning

confidence: 99%

Service Call Chain Analysis for Microservice Systems

Hao¹,

Hao²,

Zhang³

et al. 2022

Journal of Internet Technology

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Industrial practitioners widely adopt the microservice architecture to build applications. An application with microservice architecture can be composed of a set of individual services. Although microservice can improve the scalability of a system by isolating services, the complexity and difficulty of defect detection and analysis grow. High verification cost, a long feedback cycle, and high communication cost pose challenges to the maintenance of microservice systems. To address the problem, we propose a call chain tracing and analysis approach designed for the microservice architecture. To evaluate its effectiveness, we implement our approach as a plugin, namely Cam, to monitor and analyze exceptions by tracing call chains. Currently, Cam is packaged as a maven plugin for applications using Spring Cloud, which is an opensource microservice framework for Java programs. We experiment it with a microservice system to demonstrate its availability. The result shows that Cam can help software engineers understand the workflow of a service call and locate potential defects.

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“…For each task category, an artificial neural network was constructed to predict the time required for manufacturing cloud tasks in the category. Namjoo and Keramati [10] used resource-based theory and Dematel method to study the causality between the dimensions and attributes of composite service elasticity in cloud manufacturing. Souza et al [11] studied the distributed service layout strategy in mixed fog-cloud scenarios and proposed a concurrent service execution scheme.…”

Section: Literature Reviewmentioning

confidence: 99%

“…where w i represents the unit time cost and EC j represents the execution cost of the j-th manufacturing task. Equations (9)-(13) are objective functions, in which equation (9) maximizes the total service matching degree, equation (10) maximizes the total composition synergy degree, and equations (11)-(13) represent the minimum values of the total cloud entropy, execution time, and execution cost of service composition, respectively. Equations (14)- (16) are constraints, in which equation (14) stipulates that the maximum execution time of m manufacturing tasks cannot exceed the threshold time ET 0 .…”

Section: Multiobjective Optimization Model For Servicementioning

confidence: 99%

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Yao

Liu

2020

Mathematical Problems in Engineering

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Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

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AnalysingCausal dependencies of composite service resilience in cloud manufacturing using resource-based theory and DEMATEL method

Cited by 15 publications

References 135 publications

Reshoring Decisions for Adjusting Supply Chains in a Changing World: A Case Study from the Apparel Industry

Reshoring Decisions for Adjusting Supply Chains in a Changing World: A Case Study from the Apparel Industry

Service Call Chain Analysis for Microservice Systems

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

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