WITHDRAWN: Application of a fuzzy multi-objective defuzzification method to solve a transportation problem

Ahmed, Jehan Saleh; Mohammed, Husam Jasim; Chaloob, Ibrahim Zeghaiton

doi:10.1016/j.matpr.2020.12.1062

Cited by 27 publications

(2 citation statements)

References 48 publications

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“…There are many methods of defuzzification available in the literature [36][37][38]. The criteria that should be used to select the appropriate defuzzification method are disambiguation (result in a single value), plausibility (located approximately in the middle of the area), and computational simplicity [35].…”

Section: Fuzzy Inference Processmentioning

confidence: 99%

A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry

2021

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The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.

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Section: Fuzzy Inference Processmentioning

confidence: 99%

A DMAIC Integrated Fuzzy FMEA Model: A Case Study in the Automotive Industry

2021

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“…Thus, by developing more robust model in the agri-supply chain, they combine collaborative fuzzy applications with large data and a higher degree of heuristic and meta-heuristic simulations. For increasing the capability of traffic flow prediction, Ahmed et al (2021) proposed a fuzzy multi-objective defuzzification method and estimate traffic flow by solving a transportation problem. They propose a novel method for defuzzifying fuzzy numbers combining a multi-objective linear programming approach with a variation of the center of gravity (COG) technique.…”

Section: Literature Reviewmentioning

confidence: 99%

A remanufacturing inventory model to control the carbon emission using cap-and-trade regulation with the hexagonal fuzzy number

Arora

Singh

Sharma

et al. 2021

BIJ

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PurposeThe awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important elements affecting the environment. Carbon emission due to production and transportation creates complicated situations for the manufacturing firms by affecting the manufacturer's carbon quota. The ecological consequences posed in a reverse logistic model are the subject of this study.Design/methodology/approachThe present study explores the fuzzy model of economic production for both remanufacturing and recycling with uncertain cost parameters under the cap-and-trade rule to control the carbon emission due to different modes of transportation. Due to imprecise cost parameters, the hexagonal fuzzy numbers are set to fuzzify the overall cost, which leads to correct decisions in a more confident way. The result is defuzzified by using graded mean integration.FindingsThis study offers an explicit condition to control the carbon emission of the manufacturer and reduce the optimum cost. The findings indicate that the collection of used goods that can be remanufactured must be increased. The model is validated numerically. Sensitivity analysis explores the various aspects of different parameters on net cost to accomplish the fuzzy production model.Originality/valueUnder fuzzy inference, the research offers a relevant contribution in the field of recycling with controlling carbon emission by using the cap-and-trade policy. This study provides a trading strategy for a manufacturer's decision to avoid losses.

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