Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach

Li, Jing; Fang, Hongyuan; Song, Wenyan

doi:10.1016/j.jclepro.2019.03.070

Cited by 177 publications

(108 citation statements)

References 69 publications

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“…The cloud model [31,32] belongs to the category of uncertain artificial intelligence methods. Uncertainties in nature can be divided into randomness and fuzziness from the perspective of the attributes.…”

Section: Cloud Model Optimization Based On the Entropy Methodsmentioning

confidence: 99%

Rice Irrigation Schedule Optimization Based on the AquaCrop Model: Study of the Longtouqiao Irrigation District

Zhai

et al. 2019

Water

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As a crop with high water consumption, rice is an important measure of efforts to improve agricultural irrigation efficiency and alleviate the contradiction between the supply and demand of agricultural water resources. This paper takes the Longtouqiao irrigation district, in the hinterland of the Sanjiang Plain, a major rice-producing area in northern China, as an example, and the AquaCrop crop growth model and entropy-cloud model are jointly used to develop a rice irrigation schedule optimization model based on three kinds of typical rainfall years. Different irrigation schemes are established and evaluated by using the model built based on images. The results showed that the yield's normalized root mean square error (NRMSE) value of the AquaCrop model was 9.949% (< 10%) after calibration, and the our model results showed a good agreement with observed data, which indicated that the calibrated model was suitable for rice growth simulation in the research area. For the same irrigation water amount, rice was irrigated to a great extent at the tillering stage, and a small amount of irrigation water at the regreening stage of rice could improve rice yield. During irrigation, rice production can also be promoted by regulating the irrigation amount according to the rainfall in each growth period, and the optimal irrigation water amount can be controlled between 20 and 60 mm each time. Under the three typical annual scenarios of dry, normal and wet years, the respective optimal quantification results for the field capacity, total irrigation water amount and irrigation times in the rice growth period to attain the optimal irrigation effect were 25%, 425 mm, and 17 times, respectively; 25%, 450 mm, and 14 times; respectively, and 25%, 425 mm, and 17 times, respectively. The research results can provide a decision-making basis for water-saving measures and efficient rice irrigation water management.

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Section: Cloud Model Optimization Based On the Entropy Methodsmentioning

confidence: 99%

Rice Irrigation Schedule Optimization Based on the AquaCrop Model: Study of the Longtouqiao Irrigation District

Zhai

et al. 2019

Water

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“…Phochanikorn and Tan [21] developed an integrated MCDM method through the combination of fuzzy decision-making trial and evaluation laboratory (DEMATEL), fuzzy analytic network process (ANP) and prospect theory to select green suppliers in the palm oil products industry. In addition, many single models have been proposed for sustainable supplier selection, which include the rough cloud TOPSIS [14], the interval 2-tuple TODIM [41], the picture fuzzy VIKOR [42] and the intuitionistic fuzzy TOPSIS [43] methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Through selecting appropriate green suppliers, firms can balance economic-based supplier capabilities, as well as social and environmental capabilities, contributing to their strategic competitive advantages. In this context, a growing number of studies have investigated supplier selection problems incorporating sustainability criteria in recent decades [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment

Shi

Zhang

et al. 2019

Sustainability

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With the increasing awareness of global environmental protection, green production has become a significant part for enterprises to remain in a competitive position. For a manufacturing company, selecting the most suitable green supplier plays an important role in enhancing its green production performance. In this paper, we develop a new green supplier evaluation and selection model through the combination of heterogeneous criteria information and an extended multi-attributive border approximation area comparison (MABAC) method. Considering the complexity of decision context, heterogeneous information, including real numbers, interval numbers, trapezoidal fuzzy numbers, and linguistic hesitant fuzzy sets, is utilized to evaluate alternative suppliers with respect to the selected criteria. A maximizing consensus approach is constructed to determine the weight of each decision-maker based on incomplete weighting information. Then, the classical MABAC method is modified for ranking candidate green suppliers under the heterogeneous information environment. Finally, the developed green supplier selection model is applied in a case study from the automobile industry to illustrate its practicability and efficiency.

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“…The use of linguistic terms to solve the MCDM problem would be more realistic than numerical values directly [44]. Decision-makers prefer to provide their preferences by using linguistic terms than numerical values due to its simplicity [24].…”

Section: Introductionmentioning

confidence: 99%

Research Gaps in Multi-Criteria Decsion Making Methods for Researchers as an Areas of Interest

Ayasrah

Turan

2020

JMMST

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The increase in Multi criteria decision-making studies is reflecting its importance as an interested area for research. Even though a high number of revealed studies mainly in last decade, still this field have challenges that require attentions from researchers in future. Most of MCDM related studies did not consider determining DM weights or using subjective methods, and there is an absence of implementing sensitivity analysis to DM weights as well. In addition to the need to develop new methods that utilizing the web or mobile technologies to deal with complexity and uncertainty adhered to MCDM problems. This paper represents a trial to list current challenges in this field as areas of interest for researchers in future studies that aims to enhance the quality and effectiveness of decision-making process.

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Sustainable supplier selection based on SSCM practices: A rough cloud TOPSIS approach

Cited by 177 publications

References 69 publications

Rice Irrigation Schedule Optimization Based on the AquaCrop Model: Study of the Longtouqiao Irrigation District

Rice Irrigation Schedule Optimization Based on the AquaCrop Model: Study of the Longtouqiao Irrigation District

Green Supplier Evaluation and Selection with an Extended MABAC Method Under the Heterogeneous Information Environment

Research Gaps in Multi-Criteria Decsion Making Methods for Researchers as an Areas of Interest

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