Modified Weighted Sum Method for Decisions with Altered Sources of Information

Sorooshian, Shahryar; Parsia, Yasaman

doi:10.13189/ms.2019.070301

Cited by 26 publications

(8 citation statements)

References 8 publications

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“…In cases where measurement units vary, the qualification values are standardized. Subsequently, after amalgamating the scores based on the weight of each criterion, the overall score for each potential option is established [20,21,22].…”

Section: Methodsmentioning

confidence: 99%

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

2023

Data Analytics and Artificial Intelligence

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SDN-Enabled IoT Networks bring about a transformative shift in conventional network models by integrating the core principles of Software-Defined Networking (SDN) into the realm of the Internet of Things (IoT). This integration empowers the agile and effective management of IoT devices, facilitating smooth connectivity, optimized distribution of resources, and flexible network setups. Through the consolidation of control and the utilization of virtualization methods, SDN-Enabled IoT Networks amplify scalability, security, and real-time responsiveness. This addresses the obstacles presented by the extensive proliferation of IoT devices. This paradigm transition heralds a fresh era of interconnectedness, where SDN assumes a central role in harmonizing the intricate interplay of IoT devices and services. The rapid expansion of Internet of Things (IoT) devices has introduced unparalleled complexities in overseeing networks and establishing connections. This compels the need for inventive strategies to effectively manage the substantial surge of IoT devices and their ever-changing connectivity prerequisites. Software-Defined Networking (SDN) emerges as a promising approach to tackle these issues by enabling the flexible management of networks and allocation of resources. This study investigates the amalgamation of SDN within the realm of IoT, aiming to streamline device connections, optimize data transmission efficiency, and accommodate adaptable network setups. Introducing an innovative weighted sum technique for resource allocation optimization, this work lays the foundation for a comprehensive framework that bolsters IoT network performance and expandability. Four different SDN implementations are examined, including the Conventional IoT Network, SDN-enabled IoT utilizing Centralized Control, SDN-enabled IoT employing Distributed Control, SDN-enabled IoT with Hierarchical Control, and SDN-enabled IoT utilizing Hybrid Control. The assessment considers various aspects such as Enhanced Scalability, Enhanced Traffic Engineering, Heightened Security, Implementation Complexity, Difficulty of Migration, and Reliance on Vendors. The Conventional IoT Network secures a moderate 3rd position with a Preference Score of 0.56030, while the SDN-enabled IoT with Centralized Control holds the 5th rank at 0.49732, despite excelling in specific domains. The SDN-enabled IoT with Distributed Control achieves the top rank with a notable Preference Score of 0.79414 due to comprehensive performance, followed by the SDN-enabled IoT with Hierarchical Control securing the 2nd spot (Preference Score: 0.57022), and the SDN-enabled IoT with Hybrid Control taking the 4th position (Preference Score: 0.51300), particularly excelling in Traffic Engineering.

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Section: Methodsmentioning

confidence: 99%

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

2023

Data Analytics and Artificial Intelligence

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“…Hence, for the analysis of the collected data, classic TOPSIS was not capable to consider inputs from two groups of experts. Sorooshian and Parsia [43] also explained this as one of the constraints of existing MADMs; they suggested a supplementary procedure for solving this issue, called decisions with altered sources of information, which will be included in this study. This procedure adds a few sub-steps to the MADM data entry of that can be summarized as: Step 1, construction of a decision matrix with inputs from the main source of information; Step 2, completion of the decision matrix with inputs from the altered source of information; and Step 3, normalizing the decision matrix.…”

Section: Methodsmentioning

confidence: 99%

Implementation of an Expanded Decision-Making Technique to Comment on Sweden Readiness for Digital Tourism

Sorooshian

2021

Systems

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Tourism provides many advantages for Sweden and the whole world, as well as its travelers. Since almost all types of tourism are currently in crisis as a result of the current COVID-19 pandemic, information and communication technology is expected to play a role, not only during the crisis but also in the post-COVID-19 era. Thus, with no expectations from types of tourism, Sweden needs to broaden its digital tours. As a result, this letter aims to classify the transition readiness of industry clusters for this digitalization move. An extended version of the TOPSIS technique was formulated and validated, plus a new framework for measuring digitalization readiness for this purpose. Lastly, analysis of the collected data proves that business tourism could lead the change, though adventure and rural tourism are at the farthest point from being considered ready to change.

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“…The weighted sum method, known as the traditional multifactor rating scheme, is a very popular, practical, and easily applicable subjective DM process [57][58][59][60][61][62][63][64][65][66][67]. Kumar and Suresh [58] stated that this procedure includes many DM techniques where every alternative could be scored using relevant factors, wherein every factor is weighted based on its significance.…”

Section: Modified Weighted Sum Methods (Wsm)mentioning

confidence: 99%

“…Kumar and Suresh [58] stated that this procedure includes many DM techniques where every alternative could be scored using relevant factors, wherein every factor is weighted based on its significance. In this study, because of the use of different sources of information, management criteria, and infection risk, a modified WSM was used, as presented by Sorooshian and Parsia [59], which was called the WSM, in general, in the study. The authors mentioned x Alternative (A) and y decision Criterion (C) sets, wherein the technique could be algorithmically delineated into five phases as follows:…”

Section: Modified Weighted Sum Methods (Wsm)mentioning

confidence: 99%

A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks

Parsia¹,

Sorooshian

2020

IJERPH

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Most of the healthcare facilities (HFs) have to face the nosocomial infections (NIs), which increase the rates of morbidity, mortality, and financial burden on the HFs and the patients. The control of the NIs is a global issue and requires additional effort. Because the pathogenic microbes can be transmitted among all the HF departments, the layout and design of the HFs (or the department configuration) is considered to play a significant role in control of the NIs. A few of the departments transmit the microbes more than other departments, called ‘cause’, while some other departments are more infected than others, called ‘effect’. Here, the researchers have stated that both the cause and effect departments are risky. This research tried to propose a comprehensive mathematical algorithm for choosing the high-risk department(s) by applying the NI and the managerial criteria to minimize NIs through rearchitecting of the HFs. To develop the algorithm, the researchers applied the multiple criteria decision-making (MCDM) methods. They used Decision-Making Trial and Evaluation Laboratory (DEMATEL) and modified weighted sum method (WSM) methods, and their hybrid, along with a modified nominal group technique (NGT) for data collection. The proposed algorithm was later validated by implementation in a HF as a case study. Based on all results of the algorithm, the high-risk departments in the HF were identified and proposed to be eliminated from the HF in such a way that the facility would retain its functionality. The algorithm was seen to be valid, and the feasibility of the algorithm was approved by the top managers of the HF after the algorithm was implemented in the case study. In conclusion, the proposed algorithm was seen to be an effective solution for minimizing the NIs risk in every HF by eliminating the high-risk departments, which could simplify the HF manager’s decisions.

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Modified Weighted Sum Method for Decisions with Altered Sources of Information

Cited by 26 publications

References 8 publications

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

Enabling Efficient IoT Device Connectivity and Dynamic Network Management through SDN: A Weighted Sum Method Approach

Implementation of an Expanded Decision-Making Technique to Comment on Sweden Readiness for Digital Tourism

A Decision-Making Algorithm for Rearchitecting of Healthcare Facilities to Minimize Nosocomial Infections Risks

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