scholarly journals Self-Adaptive Framework Based on MAPE Loop for Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2996 ◽  
Author(s):  
Euijong Lee ◽  
Young-Duk Seo ◽  
Young-Gab Kim
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Dynamic Environment ◽  
Adaptive Software ◽  
Machine Model ◽  
Wide Range ◽  
Finite State ◽  
Game Theoretic ◽  
Adaptive Framework ◽  
Self Adaptive

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

RINGA: Design and verification of finite state machine for self-adaptive software at runtime

2018 ◽  
Vol 93 ◽  
pp. 200-222 ◽  
Author(s):  
Euijong Lee ◽  
Young-Gab Kim ◽  
Young-Duk Seo ◽  
Kwangsoo Seol ◽  
Doo-Kwon Baik
Keyword(s):  
Finite State Machine ◽  
State Machine ◽  
Adaptive Software ◽  
Finite State ◽  
Self Adaptive

scholarly journals DECISION QUALITY IN COMPLEX PRODUCT DEVELOPMENT: REFLECTIONS ON A CASE STUDY

2021 ◽  
Vol 1 ◽  
pp. 861-870
Author(s):  
Ghadir Siyam ◽  
Mariely Salgueiro ◽  
John Kennedy
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Dynamic Environment ◽  
Decision Quality ◽  
Future Research ◽  
Decision Making Process ◽  
Dependency Structure ◽  
Complex Projects ◽  
Wide Range ◽  
Dependency Structure Matrix

AbstractConceptual design projects are increasingly known as an intense decision-making process. Much of the decision-making is comparing the degree of preference between choices. In the complex projects of the upstream business of oil and gas, good decisions are crucial for success. Decisions are typically made within a dynamic environment, wide range of uncertainty, and have to account for the asset life cycle. This paper reflects on the application of the decision quality framework with a focus on decision modelling. Using an industrial example, a systematic approach to visualise and improve decision making process is proposed. The approach applies a Dependency Structure Matrix (DSM) and Decision Quality frameworks and identified opportunities for future research.

Dynamic performance assessment of a supply chain process

2015 ◽  
Vol 21 (4) ◽  
pp. 743-770 ◽  
Author(s):  
Anup Kumar ◽  
Kampan Mukherjee ◽  
Amit Adlakha
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Dynamic Environment ◽  
Chain Process ◽  
Content Type ◽  
Supply Chain Efficiency ◽  
Stochastic Dea ◽  
Wide Range ◽  
Dea Models ◽  
Lagged Effects

Purpose – A variety of tools are available to measure supply chain efficiency, but there are a few methods available for assessing efficiency in dynamic environments. The purpose of this paper is to illustrate the use of data envelopment analysis (DEA) with the help vector auto regression in measuring internal supply chain performance in dynamic environment. Design/methodology/approach – Two DEA models were developed – the static DEA that is traditional DEA methodology and the dynamic DEA. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making benchmarking and resource planning decisions. Findings – The results demonstrate that lagged effects can lead to changes in efficiency scores, rankings, and efficiency classification. So, using static DEA models in dynamic environment can be potentially misleading. Using impulse response analysis it has been seen that shocks given to marketing strategy in MR affects more at each of the decision-making unit’s (DMU’s) compared to other variables, further the authors could also investigate the dependent variables (output) shocks to input variables. Social implications – Methodology can be applied to a wide range of evaluation problems in place of conventional DEA models. Results show that lagged effects can lead to substantial discrepancies in evaluation results. Biased evaluation results would easily lead to erroneous decision and policy making for the firm. Therefore the authors should always take a broader perspective in evaluating longitudinal performance by incorporating the effects into evaluation and decision-making processes. Future work of this study could look into the possibility of modeling in a stochastic supply chain environment. In addition, it will also be interesting to look into evaluating the stochastic DEA model in multiple time periods in order to examine whether there is any technological influence on the supply chain efficiency. Originality/value – The contribution of this study provides useful insights into the use of DDEA as a modeling tool to aid managerial decision making in assessing supply chain efficiency in dynamic environment.

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