Assessing the sustainable supply chains of tomato paste by fuzzy double frontier network DEA model

Tavassoli, Mohammad; Fathi, Amirali; Saen, Reza Farzipoor

doi:10.1007/s10479-021-04139-4

Cited by 34 publications

(7 citation statements)

References 49 publications

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“…Many businesses can prevent from irreparable loses such as bankruptcy and complete shutdown by investing in resilient SSCs (Sharifi et al, 2020). Nonetheless, the presence of resilient SSCs cannot provide organizations with competitive advantages if such SCs are not assessed by using powerful methodologies and techniques (Tavassoli et al, 2021). Thus, it is crucial to develop and apply sustainably resilient SCs assessment methods for improving the performance of business operations.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

et al. 2022

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In era of reglobalization, sustainably resilient supply chains (SCs) are imperative in corporations to improve performance and meet stockholders’ expectations. However, sustainably resilient SCs could not be effective if are not assessed by using advanced frameworks, systems, and models. As such, developing a novel network data envelopment model (DEA) to appraise sustainably resilient SCs is our purpose in this article. To do so, we present a new double-frontier methodology to provide optimistic and pessimistic efficiency measures in network structures. Moreover, ideas of outputs weak disposability, chance-constrained programming, and discrete dominance are incorporated in a unified framework of modelling efficient and inefficient production technologies. The new network DEA model also can address dissimilar types of data, including undesirable and integer-valued and ratio outputs, stochastic intermediate products, and integer-valued inputs in a unified framework. Furthermore, an aggregated Farrell type efficiency measure is developed which allows to provide the complete ranking of units so that each decision-making unit (DMU) has its own rank in both overall and divisional point of view. We show the unique features of our developed model using a real case study in paint industry to evaluate the efficiency and reducing carbon dioxide (CO2) emissions. The results show that how well the proposed models can evaluate the sustainability and resilience of supply chains in the presence of uncertainty and with dissimilar types of data.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

et al. 2022

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“…(PR9) Joshi et al [ 71 ]; Mahroof et al [ 72 ]; Martin-Rios et al [ 73 ]; Gupta et al [ 74 ] Distribution initiatives (H4) Low carbon distribution logistics practices. (PR10) Yang and Xu [ 75 ]; Tavassoli et al [ 76 ]; Kashyap and Agarwal [ 77 ] Traceability and monitoring of the food items. (PR11) Zhou et al [ 78 ]; Lawo et al [ 79 ]; Sharma and Jayant [ 80 ]; Kamilaris et al [ 81 ] Customer-based (H5) Preferences for the product binding sustainability.…”

Section: Literature Reviewmentioning

confidence: 99%

Avenues of decarbonisation in the dynamics of processed food supply chains: Towards responsible production consumption

Sharma,

Singh,

Tyagi

et al. 2024

Heliyon

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“…Moreover, one would want to be able to identify tomato plants in addition to plot-based comparisons. This would also solve the assignment issue, as each plant could be readily coded by a label at the pot, and tomatoes could be distinguished by how far away from the pot they were on the stem [23]- [25]. The best accuracy is 95.49% for CNN, whereas the corresponding figures for convolution layer + RF, convolution layer + KNN, and convolution layer + SVM are 94.0%, 93.09%, and 93.34%.…”

Section: Performance Evaluation Matrixmentioning

confidence: 99%

Tomato pest recognition using convolutional neural network in Bangladesh

Polin,

Hasan,

Habib

et al. 2024

Bulletin EEI

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The tomato is one of the most popular and well-liked veggies among Asians. It is interesting to note that in Bangladesh, it is the second most significant vegetable consumed. Moreover, tomato is served not only as a vegetable, but it is also served as sauce, jam, etc., and used in making different types of cuisines. But the fact is due to the pests, thousands of tons of tomatoes are harmed every year in Bangladesh. The production of tomatoes in Bangladesh is harmed by a number of dangerous pests. We develop a solution to recognize pests at an early stage. Five different pest types, including aphids, red spider mites, whiteflies, looper caterpillars, and thrips, have been studied in this research. To identify tomato pests, we curated image datasets from online and offline repositories and processed them using a convolutional neural network (CNN) model. We used features from CNN layers for three machine learning algorithms: Random Forest (RF), support vector machine (SVM), and K-Nearest Neighbors (K-NN). This comprehensive approach allowed a thorough comparison of these algorithms in tomato pest recognition. For recognizing tomato pests, our methods generate excellent results. The accuracy of our experiment is 95.49% which indicates the successful completion of the experiment.

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Assessing the sustainable supply chains of tomato paste by fuzzy double frontier network DEA model

Cited by 34 publications

References 49 publications

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

RETRACTED ARTICLE: Evaluating sustainably resilient supply chains: a stochastic double frontier analytic model considering Netzero

Avenues of decarbonisation in the dynamics of processed food supply chains: Towards responsible production consumption

Tomato pest recognition using convolutional neural network in Bangladesh

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