Preference ratio-based maximum operator approximation and its application in fuzzy flow shop scheduling

Supplier Selection Problem (SSP) has become a critical objective of purchasing departments because of its signi cant e ect on successful logistic and Supply Chain Management (SCM). In real-life situations, SSP parameters are often imprecise, vague, uncertain, or incomplete. In this respect, fuzzy sets theory is the best developed approach to formulate these uncertainties. In this paper, we have extended fuzzy VIKOR using an e cient fuzzy distance measure to solve applicable SSP under group decision-making process. In our study, an e cient fuzzy VIKOR for solving SSP under group decisionmaking process is presented in which decision makers have di erent weights in decisionmaking process and their opinions are collected in the form of linguistic variables. In our methodology, preference ratio method is applied to rank the alternatives. Ultimately, several numerical illustrations and sensitivity analyses are performed to demonstrate the applicability of the proposed method.

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“…Sadi-Nezhad and Ghaleh-Assadihave [75] proposed an algorithm based on preference ratio to rank n TFNs as follows:…”

Section: Ranking Fuzzy Numbersmentioning

confidence: 99%

An extended fuzzy VIKOR for group decision making based on fuzzy distance to supplier selection

2016

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“…A novel fuzzy distance measurement method was proposed for generalized fuzzy numbers by C. Chakraborty and D. Chakraborty [20]. The ranking of alternatives with respect to all criteria and their weights is performed by applying the initial estimation method [21]. Gumus [22] presented the criteria according to which hazardous waste transportation firms are determined by a modified Delphi method.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Ranking of Organizational Resilience Factors by Using a Two-Step Fuzzy AHP and Fuzzy TOPSIS

Tadić

Aleksić

Stefanović

et al. 2014

Mathematical Problems in Engineering

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We presented a novel fuzzy multicriteria decision making approach to evaluate and rank organizational resilience factors with respect to user preference orders. Due to vagueness of the decision data, the precise numerical data are inadequate for real-life business situations. Human judgements can be expressed by linguistic expressions which are modeled by fuzzy sets. The complexity of the considered problem calls for analytic methods rather than intuitive decisions. Two fuzzy multi-criteria methods are proposed for solving the treated problem: Fuzzy Analytic Hierarchical Process (FAHP) is applied to determine the relative importance of business processes and the relative importance of organizational resilience factors under each business process, and an extension of the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is applied to rank the organizational resilience factors. With respect to complexity and the type of considered management problem, we introduce a modified fuzzy decision matrix. The proposed algorithm has efficiently been applied in the assessment of organizational resilience factors to small and medium enterprises of the process industry.

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“…Huang et al [11] proposed an improved genetic algorithm with CUDA and developed a longest common substring coding method to optimize fuzzy flow job shop scheduling problem. A lot related work can be found ( Ghrayeb,Omar [12], Lai et al [13], Nezhad et al [14] ).…”

Section: Introductionmentioning

confidence: 98%

Application of Interval Theory and Genetic Algorithm for Uncertain Integrated Process Planning and Scheduling

Wang

Gao

et al. 2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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Process planning and scheduling are two important parts in intelligent manufacturing system and have great impacts on production efficiency. Integrate them can highly increase the production feasibility and optimality. Researchers have done a lot work on integration of process planning and scheduling (IPPS). But former researchers rarely focused on uncertain environment. In reality many factors can cause the uncertainty of production process time. This paper pioneers in choosing a better solution in uncertain manufacturing environment based on interval theory. The uncertain process time is modeled as interval number. And then, the completion time is also an interval number. Genetic Algorithm (GA) is used to solve this model. The feasibility and effectiveness of the solution have been taken into consideration. The experimental results obtained by increasing the scale of the problem illustrate the proposed method is stable and effective.

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Preference ratio-based maximum operator approximation and its application in fuzzy flow shop scheduling

Cited by 23 publications

References 10 publications

An extended fuzzy VIKOR for group decision making based on fuzzy distance to supplier selection

An extended fuzzy VIKOR for group decision making based on fuzzy distance to supplier selection

Evaluation and Ranking of Organizational Resilience Factors by Using a Two-Step Fuzzy AHP and Fuzzy TOPSIS

Application of Interval Theory and Genetic Algorithm for Uncertain Integrated Process Planning and Scheduling

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