Forecasting of Radio Frequency Identification Entropy Viscosity Parking and Forwarding Algorithm Flow Risks and Costs: Integrated Supply Chain Health Manufacturing System (ISCHMS) Approach

Rodger, James A.

doi:10.1109/jrfid.2018.2795801

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…In (8), there are three possibilities: S h ij >S h kj is beneficial, S h ij = S h kj is neutral, or S h ij <S h kj represents a loss.…”

Section: Todimmentioning

confidence: 99%

“…The perceived value function values for each supplier under all attributes can be integrated using Equation (8). The results are listed in Table 9.…”

Section: Case Studymentioning

confidence: 99%

“…Ghodsypour and O'Brien [7] used the analytic hierarchy process (AHP) to find the largest integrated value of a candidate supplier, then ranked suppliers based on that value. James [8] improved supply chain management by using a new integrated manufacturing enterprise system based on parking and forwarding algorithms, RFID, and a Markov decision process.…”

Section: Introductionmentioning

confidence: 99%

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A Preference Model for Supplier Selection Based on Hesitant Fuzzy Sets

Zhou

Dou

Liao³

et al. 2018

Sustainability

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Abstract:The supplier selection problem is a widespread concern in the modern commercial economy. Ranking suppliers involves many factors and poses significant difficulties for decision makers. Supplier selection is a multi-criteria and multi-objective problem, which leads to decision makers forming their own preferences. In addition, there are both quantifiable and non-quantifiable attributes related to their preferences. To solve this problem, this paper presents a preference model based on hesitant fuzzy sets (HFS) to select suppliers. The cost and service quality of suppliers are the main considerations in the proposed model. HFS with interactive and multi-criteria decision making are used to evaluate the non-quantifiable attributes of service quality, which include competitive display, qualification ability, suitability and competitiveness of solutions, and relational fitness and dynamics. Finally, a numerical example of supplier selection for a high-end equipment manufacturer is provided to illustrate the applicability of the proposed model. The preferences of a decision maker are then analyzed by altering preference parameters.

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“…In (8), there are three possibilities: S h ij >S h kj is beneficial, S h ij = S h kj is neutral, or S h ij <S h kj represents a loss.…”

Section: Todimmentioning

confidence: 99%

“…The perceived value function values for each supplier under all attributes can be integrated using Equation (8). The results are listed in Table 9.…”

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Preference Model for Supplier Selection Based on Hesitant Fuzzy Sets

Zhou

Dou

Liao³

et al. 2018

Sustainability

View full text Add to dashboard Cite

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“…This is particularly evident in supply Chain Management as an IoT of collaboration, advancement, adaptation to internal failure, efficiencies and synchronization is required between a PC organisation's RFID and Supply Chain Management segments. These collaborations likewise incorporate security issues and sifting [12], [13]. A fundamental prerequisite in the huge RFID frameworks is the requirement for a high throughput of reading tags.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Large-Scale RFID Networks With Energy-Efficient Dynamic Cluster Head Selection: A Performance Improvement Approach

Pandian,

Somasundaram,

Sahu

et al. 2024

IEEE Access

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This paper focuses on the use of radio frequency signals for non-contact tracking and localization utilising Radio Frequency Identification (RFID) technology. The clustering approach is quite useful when dealing with large RFID readers. This method stimulates the expansion of RFID nodes without compromising the overall network performance. The Cluster Head (CH) is the most critical node in a clustered RFID system. This research presents a method for dynamically selecting the cluster head that takes the connectivity and power of each RFID reader into account. In dynamic mode, selecting a new cluster head is based on Fuzzy Logic. Based on these data, the energy level of 0.443 and centrality of 0.809 are the thresholds at which a node has a 91.8 % chance of becoming a CH. The management of massive RFID networks is also handled, with cluster numbers increasing at different time intervals. The RFID network's effectiveness is determined by measuring throughput, accuracy, delay, success, and error rates. The network may accommodate up to a thousand nodes with 13 node leaders for improved capacity. The results show a 97.8 % success rate, 0.22 % accuracy, a 2.64 % error rate, and a 36.91 second latency.

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