Forward–backward analysis of RFID-enabled supply chain using fuzzy cognitive map and genetic algorithm

Kim, Moon-Chan; Kim, Chang Ouk; Hong, Seong Rok; Kwon, Ick-Hyun

doi:10.1016/j.eswa.2007.08.015

Cited by 63 publications

(28 citation statements)

References 12 publications

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“…Since this problem is NP-hard, artificial intelligence-based random search techniques have gained favour in this area of research (Kim et al, 2008). …”

Section: ) Solution Methodologymentioning

confidence: 99%

Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

et al. 2013

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“…Since this problem is NP-hard, artificial intelligence-based random search techniques have gained favour in this area of research (Kim et al, 2008). …”

Section: ) Solution Methodologymentioning

confidence: 99%

Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

et al. 2013

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“…Although the usefulness of using neural networks and support machines has been reported in the literature, the obstacles are in model building and use of model in which the correlation between all the variables are difficulty to be inferred and understood. Recently, cognitive map [2] has been applied in many scientific areas, such as political science [3], organization and strategy planning [4] [5], analysis of business performance indicators [6], software project management [7], supply chain management [8]- [9], medical diagnosis [10]- [12], engineering design [13], etc. When a fuzzy cognitive map (FCM) has been constructed, it becomes a very usefully tool for supporting decision making [14].…”

Section: Introductionmentioning

confidence: 99%

Using FCM Based Hybrid Computational Approach for Diseases Diagnosis in Traditional Chinese Medicine

Chen¹,

You²,

Wu³

et al. 2014

IJMLC

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Abstract-Fuzzy cognitive map (FCM) belongs to one of the soft computing technique for modeling complex systems, which utilize the advantages from the synergistic theories of neural networks and fuzzy logic. The development of FCM highly relies on the human expert experience and knowledge. So, without those from expert(s), the FCM is hard to be constructed successfully. In this study, a self-adaptive FCM without any involvement of experts by using hybrid evolutionary computation approach is proposed. It includes the genetic algorithm (GA) and particle swarm optimization (PSO). The purpose of GA is to decide the significant variables. Based on those variables selected by GA, the most appropriate cognitive map can be constructed by PSO, i.e., the relationship matrix for the set of variables. The purpose of the research is to find the minimum subset of cognitive variables and the corresponding correlation matrix from historical numerical dataset so as to construct the optimal FCM decision model. In this study, the diagnosis of traditional Chinese medicines has been investigated base on twelve-meridian data obtained by meridian energy analysis device. The computational results show that the proposed approach is able to provide higher classification accuracy than those of the approaches in literature or by using commercial software.Index Terms-Fuzzy cognitive map, genetic algorithm, particle swarm optimization, traditional Chinese medicine, twelve-meridian. I. INTRODUCTIONTraditional Chinese medicine (TCM) has been one of the the primary forms of healthcare. Although Western medicine remains the mainstream of the healthcare system, TCM enjoys considerable popularity as a complementary form of healthcare to the Chinese population [1]. In TCM, the meridian energy analysis device (MEAD) is developed by medical experts to objectively measure and acquire the physiological data on the body meridian energy. The integration of the MEAD data with results from Western medical instruments (ex. hematological testing, endoscope, ultrasound, CI scanner and magnetic resonance imaging, etc.) is currently widely employed in TCM clinical practice in order to construct an objectively modern diagnosis. Manuscript received April 7, 2014; revised May 19, 2014. This work was supported in part by the National Science Council, Taiwan under Grant NSC 101-2221-E-150-058.T. In real world, the medical diagnosis is highly complicated in nature so that it's not easy to form a comprehensive model taking into account all the significant variables through the conventional statistical methods. Machine learning methods such as neural networks and support vector machines have been displayed to be with more dependable than other conventional approaches. Although the usefulness of using neural networks and support machines has been reported in the literature, the obstacles are in model building and use of model in which the correlation between all the variables are difficulty to be inferred and understood. Recently, cognitive map [2] When a fuzzy cognitiv...

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“…This is particularly true in heterogeneous environments, including multiple readers, applications instances, supply chain management, complex process and sophisticated business semantics [16,17,18,19].…”

Section: Related Workmentioning

confidence: 99%

A SCADA oriented middleware for RFID technology

Cardiel¹,

Heradio²,

Somolinos³

et al. 2012

Expert Systems with Applications

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Radio Frequency IDentification (RFID) has emerged as the new technology paradigm for acquisition and information management. RFID can be used to improve significantly the efficiency of business processes by providing the capability of automatic identification and data capture. This technology introduces new challenges on data and process information management in current systems. RFID data are timedependent and dynamically changing. In addition, data carry implicit semantics. The homogeneous data processing of such implicit semantics allows us to propose RFID middleware as a WHO-WHEN-WHERE data problem. This paper presents DEPCAS, a new middleware for RFID information based on the SCADA architecture for control systems. An application of DEPCAS is the resolution of heterogeneous situations, which solves the WHAT or context-aware to apply the auto identification data received from RFID systems in business applications.

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Forward–backward analysis of RFID-enabled supply chain using fuzzy cognitive map and genetic algorithm

Cited by 63 publications

References 12 publications

Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

Economical impact of RFID implementation in remanufacturing: a Chaos-based Interactive Artificial Bee Colony approach

Using FCM Based Hybrid Computational Approach for Diseases Diagnosis in Traditional Chinese Medicine

A SCADA oriented middleware for RFID technology

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