A novel hybrid approach for product concept evaluation based on rough numbers, shannon entropy and TOPSIS-PSI

Chen, Zhe; Zhong, Peisi; Liu, Mei; Sun, Han Xu; Kai, Shoichi

doi:10.3233/jifs-210184

Cited by 15 publications

(20 citation statements)

References 38 publications

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“…The 2-tuple linguistic (2TL) provides the environment of the two-layer method, and this part introduces the rationale to select 2TL. Due to the uncertain environment, fuzzy set 48 , rough set 49 , grey decision 50 , and some other extended methods are applied in MADM problems 51 . 2TL is a model based on the linguistic fuzzy set proposed by Herrera and Martínez 52 .…”

Section: Preliminariesmentioning

confidence: 99%

An integrated expert weight determination method for design concept evaluation

Chen

Zhong

Liu

et al. 2022

Sci Rep

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Expert weight determination is a critical issue in the design concept evaluation process, especially for complex products. However, this phase is often ignored by most decision makers. For the evaluation of complex product design concepts, experts are selected by clusters with different backgrounds. This work proposes a novel integrated two-layer method to determine expert weight under these circumstances. In the first layer, a hybrid model integrated by the entropy weight model and the Multiplicative analytical hierarchy process method is presented. In the second layer, a minimized variance model is applied to reach a consensus. Then the final expert weight is determined by the results of both layers. A real-life example of cruise ship cabin design evaluation is implemented to demonstrate the proposed expert weight determination method. To analyze the feasibility of the proposed method, weight determination with and without using experts is compared. The result shows the expert weight determination method is an effective approach to improve the accuracy of design concept evaluation.

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Section: Preliminariesmentioning

confidence: 99%

An integrated expert weight determination method for design concept evaluation

Chen

Zhong

Liu

et al. 2022

Sci Rep

Self Cite

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show abstract

“…It is another extension of fuzzy sets. In contrast to the fuzzy number, rough number treats the uncertain information without an external pre-setting interval boundary or additional membership function and distribution forms 4 . Zhai 10 , 40 first applied the method in design concept evaluation, where the rough set is widely used in product concept selection.…”

Section: State Of the Artmentioning

confidence: 99%

“…To reduce the subjective bias caused by individual preference, group decision-making is implemented. Meanwhile, as the evaluation attributes are multiple and complex, multiple attribute decision-making (MADM) methods are receiving considerable interest in design concept evaluation 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Zhai 10 used rough numbers (RNs) to quantify the vagueness of raw information, and proposed an integrated method based on rough set and grey relation analysis. Zhu 11 , Chen 4 , Tiwari 12 and Song 13 also converted the raw data to intervals using rough set theory. Shidpour 14 constructed two decision matrices using a rough set and a fuzzy set.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the other extensions of the fuzzy set, the rough set does not need further individual judgment information in the decision matrix building 4 . In other words, the method is more objective compared to other fuzzy logic methods.…”

Section: Introductionmentioning

confidence: 99%

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A novel integrated MADM method for design concept evaluation

Chen

Zhong

Liu

et al. 2022

Sci Rep

Self Cite

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Design concept evaluation plays a significant role in new product development. Rough set based methods are regarded as effective evaluation techniques when facing a vague and uncertain environment and are widely used in product research and development. This paper proposed an improved rough-TOPSIS method, which aims to reduce the imprecision of design concept evaluation in two ways. First, the expert group for design concept evaluation is classified into three clusters: designers, manufacturers, and customers. The cluster weight is determined by roles in the assessment using a Multiplicative Analytic Hierarchy Process method. Second, the raw information collection method is improved with a 3-step process, and both design values and expert linguistic preferences are integrated into the rough decision matrix. The alternatives are then ranked with a rough-TOPSIS method with entropy criteria weight. A practical example is shown to demonstrate the method’s viability. The findings suggest that the proposed decision-making process is effective in product concept design evaluation.

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Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)

Saroughi,

Mirzania,

Achite

et al. 2024

Environ Monit Assess

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Predicting groundwater level (GWL) fluctuations, which act as a reserve water reservoir, Particularly in arid and semi-arid climates, is vital in water resources management and planning. Within the scope of current research, a novel hybrid algorithm is proposed for estimating GWL values in the Tabriz plain of Iran by combining the artificial neural network (ANN) algorithm with newly developed nature-inspired Coot and Honey Badger metaheuristic optimization algorithms. Various combinations of meteorological data such as temperature, evaporation and precipitation, previous GWL values, and the month and year values of the data were used to evaluate the algorithm's success. Furthermore, shannon entropy of performance of models was assessed according to 44 different statistical indicators which is classified into two class: accuracy and error class. Hence, based on high value of Shannon entropy, the best statistical indicator was selected and the results of best model and selecting the best scenario were analyzed. Results indicated that value of Shannon entropy is higher for accuracy class than error class. Also, for accuracy and error class respectively, Akaikes Information Criterion (AIC) and Residual Sum of Squares (RSS) indexes with the highest entropy value which is equal to 12.72 and 7.3 are the best indicators of both classes and Legate-McCabe Efficiency (LME) and Normalized Root Mean Square Error-mean (NRMSE-Mean) indexes with the lowest entropy value which is equal to 3.7 and -8.3 are the worst indicators of both classes. According to the results of evaluation best indicator in the testing phase, AIC indicator value for HBA-ANN, COOT-ANN, and the stand-alone ANN models is equal to -344, -332.8, -175.8, respectively. Furthermore, it was revealed that the proposed metaheuristic algorithms significantly ameliorate the performance of the stand-alone ANN model and offered satisfactory GWL prediction results.Finally, it was concluded that the Honey Badger optimization algorithm showed superior results than the Coot optimization algorithm in GWL prediction.

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A novel hybrid approach for product concept evaluation based on rough numbers, shannon entropy and TOPSIS-PSI

Cited by 15 publications

References 38 publications

An integrated expert weight determination method for design concept evaluation

An integrated expert weight determination method for design concept evaluation

A novel integrated MADM method for design concept evaluation

Shannon entropy of performance metrics to choose the best novel hybrid algorithm to predict groundwater level (case study: Tabriz plain, Iran)

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