Selecting Green Suppliers by Considering the Internet of Things and CMCDM Approach

Emroozi, Vahideh Bafandegan; Roozkhosh, Pardis; Modares, Azam; Roozkhosh, Farnoosh

doi:10.1007/s41660-023-00336-9

Cited by 13 publications

(2 citation statements)

References 70 publications

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“…Consequently, the study presents a supply chain symbiosis technique by using DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs. Bafandegan Emroozi et al [19] illustrate the capability of four dissimilar combinatorial multi-criteria decision-making (CMCDM) methods to determine the good supplier in the rubber GSC. In [20], an intelligent dynamic subarray RIS architecture is based on deep reinforcement learning (DRL).…”

Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network's efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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“…16,17 By integrating IoT sensors, organizations can proactively identify and address potential issues, thereby improving overall equipment performance and reducing downtime. 18 Partiality often gives rise to numerous problems in human societies and economics. A system, by definition, comprises interconnected elements that interact with each other.…”

Section: Introductionmentioning

confidence: 99%

Presenting a new model for evaluating the factors affecting equipment reliability using system dynamics

Modares,

Pooya,

Emroozi

et al. 2024

Quality & Reliability Eng

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This study examines reliability of equipment (RoE) through three approaches: equipment design reliability, human reliability (HR), and maintenance‐based reliability. HR plays a key role in minimizing human error (HE) and subsequently enhancing RoE. Given that equipment reliability is influenced by numerous factors, these technologies come with various constraints, multiple outputs, and inputs. Compared to mathematical programming and analytical models, simulation methods in the field of reliability are relatively limited. However, system dynamics (SD) modeling is well‐suited to capture dynamics and complexity of systems, making it a valuable tool for long‐term strategic decision‐making. In this study, a combination of SD and regression approaches has been employed to explore the relationship between reliability and key variables such as HE and profit. Initially, the variables influencing reliability are identified, and then SD is utilized to understand the processes and interactions among these variables. Furthermore, linear regression is employed to establish the relationship between affective variables, reliability, and HE. To validate the results obtained from the proposed method, a sensitivity analysis is conducted. The results demonstrate the effectiveness of the proposed model. Simulation results indicate that implementing policies such as employee training and preventive maintenance significantly enhances RoE, leading to increased sales and profits for the organization. Therefore, managers should prioritize these variables and allocate adequate attention and resources to them.

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Green and Digital Supply Chain Innovation (GDSCI): a Systematic Mapping Study

Moujoud,

Bouloiz,

Gallab

2024

Sustainable Civil Infrastructures

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Selecting Green Suppliers by Considering the Internet of Things and CMCDM Approach

Cited by 13 publications

References 70 publications

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Presenting a new model for evaluating the factors affecting equipment reliability using system dynamics

Green and Digital Supply Chain Innovation (GDSCI): a Systematic Mapping Study

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