A group multi-criteria decision-making based on best-worst method

Safarzadeh, Soroush; Khansefid, Saba; Rasti−Barzoki, Morteza

doi:10.1016/j.cie.2018.09.011

Cited by 102 publications

(65 citation statements)

References 61 publications

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“…By inspiring the characteristics of the BWM, a novel method named the group best-worst method is proposed to derive the criteria weights with GDM problems. Compared with the original BWM method [24], Soroush et al [26] paid attention to the significance of the DMs for calculating the optimal weights with respect to each criterion.…”

Section: Determining the Criteria Weights Using Gbwm Methodsmentioning

confidence: 99%

“…However, the application of the above-mentioned technique ignores the significance of multiple DMs towards the evaluation results and cannot play the role of group decision making (GDM). Therefore, motivated by Safarzadeh et al [26], we constructed the group best-worst method (GBWM) under the q-ROF context with the comprehensive model by taking the DMs' weights into account to obtain the reasonable criteria weights for supplier selection.…”

Section: Introductionmentioning

confidence: 99%

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Selecting the Optimal Green Agricultural Products Supplier: A Novel Approach Based on GBWM and PROMETHEE II

Liu

Zhao

et al. 2020

Sustainability

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Due to the uncertainty of natural factors and a larger global population, the work of supplying sustainable agricultural materials, especially green agricultural products, faces enormous challenges. How to effectively evaluate and select the most desirable green agricultural material supplier is an urgent issue for both agribusiness and government. In this paper, an integrated q-rung orthopair fuzzy (q-ROF) group best–worst method (GBWM) and the PROMETHEE II was introduced to availably solve such issue. Firstly, by taking similarity degree into account to solve incomplete weight information, a novel technique was constructed to determine the experts’ weight reasonably under the q-ROF context. Secondly, to improve consistency for group decision making and obtain a highly reliable selection result, the GBWM was used to derive criteria weights. Then, based on the proposed generalized p-norm knowledge-based score function, the PROMETHEE II was further improved to rank the feasible alternatives. After that, a representative case under the background of green agricultural material supplier selection was investigated in depth. Finally, the detailed comparative technique was conducted to verify the validity and superiority of the improved method.

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Section: Determining the Criteria Weights Using Gbwm Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Selecting the Optimal Green Agricultural Products Supplier: A Novel Approach Based on GBWM and PROMETHEE II

Liu

Zhao

et al. 2020

Sustainability

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“…Best-Worst method (BWM) is a recent Multi-Criteria Decision-Making (MCDM) method, developed to reduce the complexity of pairwise criteria in the AHP (Analytic Hierarchy Process) method [48][49][50][51][52][53]. Compared to AHP, BWM uses less comparison (BWM= 2n − 3, AHP= n(n − 1)/2), yet the consistency is better [48,54].…”

Section: Step 4: Best-worst Methodsmentioning

confidence: 99%

“…Compared to AHP, BWM uses less comparison (BWM= 2n − 3, AHP= n(n − 1)/2), yet the consistency is better [48,54]. BWM is used to solve various problems, namely, supplier selection [55], location selection [50,53,56], service quality improvement [57], product design selection [49,58,59], supply-chain management [60], and performance evaluation [52,61]. BWM allows the decision-maker to select the best and the worst criteria to be transformed into the weight of each criterion using linear programming.…”

Section: Step 4: Best-worst Methodsmentioning

confidence: 99%

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

2020

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Improving service logistics is crucial in order to reciprocate customer needs. The paper aims to validate the Logistics Service Provider (LSP) Lifecycle Model for re-designing logistics service in three LSP case studies in Thailand. The lifecycle-stage evaluation was adapted to identify the current status in its lifecycle. Afterward, logistics service strategies were implemented according to the voice of the customer by Quality Function Deployment (QFD). The study combined the Logistics Service Provider (LSP) Lifecycle Model with the application of Industry 4.0 (I4.0) to improve service logistics. Case studies showed the implementation of the service logistics strategies with the feasibility solution of Industry 4.0.Sustainability 2020, 12, 2394 2 of 17 real-time information, and Circular Economy (CE) [21] are now among the basic customer requirements. Furthermore, customer behavior has changed too. Quick and immediate responses are expected. Information Technology (IT) has been increasingly used in the global business to satisfy the customer in terms of supply [22,23]. Today, Industry 4.0 is being used to accomplish the customer target, operation, and improvement in manufacturing [24,25]. Some examples of the advancement of Industry 4.0 are applying to self-learning automation, big data analytics, real-time information, Internet of Things (IoT), and smart sensors [26,27]. This improvement expands to the logistics industry in the form of, for example, on-demand delivery which offers an abrupt response to the customer [28,29]. The trend has changed rapidly around the world and the logistics service is demanded to support it. Therefore, LSPs need to innovate and create new service to address these complicated customer trends [30]. This paper demonstrates the use of the LSP Lifecycle Model to re-design the logistics service using three case-study LSPs in Thailand. It aims to address the research questions of whether the developed LSP Lifecycle Model is valid and if the Industry 4.0 concept is applicable and can be beneficial for LSPs. Firstly, the study identified the stage of the service. Secondly, services were re-designed using QFD. Thirdly, customer requirements were prioritized using the Multiple-Criteria Decision-Making (MCDM) tool. Finally, the Industry 4.0 concept was mapped to address these demands. Literature Review

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“…These models and methods mainly solve problems by determining the weights of decision-makers and use fuzzy ideal and linguistics [24][25][26] to analyze the large-scale group decision-making, but it is difficult to the selection of risk response scheme in complex group. There are other methods that use hierarchical clustering algorithms and integration operators to construct hybrid multicriteria decision-making methods and choose the best solution [27][28][29][30]. Although these methods considered the complexity of decision-making by multiple alternatives and used multiple methods to sort the alternatives, they did not consider the satisfaction and preference of the decision-makers, and the decision-making results are one-side.…”

Section: Introductionmentioning

confidence: 99%

A Selection Model on Risk Response Scheme for Complex Equipment Research and Manufacturing

Liu

2019

Mathematical Problems in Engineering

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Risk is an important factor affecting the success of complex equipment research and manufacturing, so how to deal with the risk properly has become the key to risk management of complex equipment cooperative research and manufacturing. In view of this, considering that the choice of risk response schemes for complex equipment research and manufacturing is a consensus issue of group negotiation, this paper exploits group decision-making and utility theory to establish a risk disposal scheme selection model for complex equipment development based on group negotiation consensus, and then a case verifies the validity and rationality of the proposed model. The results show that the consensus scheme selection problem proposed in the paper effectively combines the preference value and utility, considers the supplier’s risk preference behavior, and achieves the multisubject consensus scheme.

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A group multi-criteria decision-making based on best-worst method

Cited by 102 publications

References 61 publications

Selecting the Optimal Green Agricultural Products Supplier: A Novel Approach Based on GBWM and PROMETHEE II

Selecting the Optimal Green Agricultural Products Supplier: A Novel Approach Based on GBWM and PROMETHEE II

On LSP Lifecycle Model to Re-design Logistics Service: Case Studies of Thai LSPs

A Selection Model on Risk Response Scheme for Complex Equipment Research and Manufacturing

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