A new effective solution method for fully intuitionistic fuzzy transportation problem

Mahmoodirad, Ali; Allahviranloo, Tofigh; Niroomand, Sadegh

doi:10.1007/s00500-018-3115-z

Cited by 67 publications

(20 citation statements)

References 26 publications

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“…After the invention of intuitionistic fuzzy set theory many authors (for instance, Kumar 2012a, b, c, 2013;Kumar and Hussain 2014a;Singh and Yadav 2015;Hussain 2015, 2016a;Ebrahimnejad and Verdegay 2016;Gupta and Anupum 2017;Kumar 2018b, c, d;Mahmoodirad et al 2018;Bharati and Singh 2018a;Ebrahimnejad and Verdegay 2018b;Hunwisai et al 2018;Pathade and Ghadle 2018;Abhishekh and Nishad 2018;Sidhu and Kumar 2019;Pratihar et al 2020;Smarandache and Broumi 2020) have solved transportation/linear programming problems under neutrosophic/IFE successfully. Similarly, a lot of works based on AP in IFE has been done by several researchers such as Mukherjee and Basu (2012), Hussain (2014b, c, d, 2016b, c), Kumar (2017b), Pothiraj and Rajaram (2017), Kumar (2018e, 2020a and many others.…”

Section: Historical Aspectsmentioning

confidence: 99%

Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set

Kumar

2020

Int J Syst Assur Eng Manag

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Section: Historical Aspectsmentioning

confidence: 99%

Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set

Kumar

2020

Int J Syst Assur Eng Manag

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“…Mishra and Kumar [4] pointed out that the existing approach [3] cannot be used to solve unbalanced fully triangular intuitionistic fuzzy transportation problems (unbalanced transportation problems in which each parameter is represented by a triangular fuzzy number). To overcome this limitation of the existing approach [3], Mishra and Kumar [4] proposed a new approach (named as JMD approach) for transforming an unbalanced fully triangular intuitionistic fuzzy transportation problem into a balanced fully triangular intuitionistic fuzzy transportation problem. On the same direction, to overcome the limitations and to resolve the drawback of Ebrhimnejad and Verdegay's approach [1], a new approach (named as JMD approach) has been proposed to solve unbalanced fully trapezoidal intuitionistic fuzzy transportation problems.…”

Section: Proposed Jmd Approachmentioning

confidence: 99%

JMD Approach for Solving Unbalanced Fully Trapezoidal Intuitionistic Fuzzy Transportation Problems

Mishra

Kumar

2020

Studies in Fuzziness and Soft Computing

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Ebrahimnejad and Verdegay [1] proposed an approach for solving fully trapezoidal intuitionistic fuzzy transportation problems (transportation problems in which each parameter is represented by a trapezoidal intuitionistic fuzzy number). One may claim that Ebrahimnejad and Verdegay's approach [1] can be used to solve such fully trapezoidal intuitionistic fuzzy transportation problems for which the aggregated value of the intuitionistic fuzzy transportation cost, the intuitionistic fuzzy availability and the intuitionistic fuzzy demand, provided by all the decision-makers, is available. While Ebrahimnejad and Verdegay's approach [1] cannot be used to solve such fully trapezoidal intuitionistic fuzzy transportation problems for which, instead of the aggregated data, the data of each decision-maker is provided separately. To overcome this limitation, one may modify Ebrahimnejad and Verdegay's approach [1] with the help of the existing trapezoidal intuitionistic fuzzy aggregation operator [2]. Also, one may use Ebrahimnejad and Verdegay's approach [1] to solve real-life fully trapezoidal intuitionistic fuzzy transportation problems. However, after a deep study, some limitations and a drawback have been observed in Ebrahimnejad and Verdegay's approach [1]. The aim of this chapter is (i) To make the researchers aware about the observed limitations and the drawback of Ebrahimnejad and Verdegay's approach [1]. (ii) To make the researchers aware about a drawback of the approach, used in Ebrahimnejad and Verdegay's approach [1], for comparing trapezoidal intuitionistic fuzzy numbers. (iii) To propose a valid approach (named as DAUGHTER approach) for comparing trapezoidal intuitionistic fuzzy numbers. (iv) To propose a valid approach [named as JAI MATA DI (JMD)] approach for solving unbalanced fully trapezoidal intuitionistic fuzzy transportation problems. (v) To illustrate the proposed JMD approach with the help of numerical examples.

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“…Optimization problems particularly the supply chain network design problem can be even more interesting and realistic when are tackled in uncertain environments considering uncertain values for parameters and even variables (see Boros, Fehér, Lakner, Niroomand, & Vizvári, 2016; Mahmoodirad, Allahviranloo, & Niroomand, 2019; Mahmoodirad, Dehghan, & Niroomand, 2019; Malaperdas & Zacharias, 2019; Mirzaei, Niroomand, & Zare, 2016; Niroomand, Bazyar, Alborzi, & Mahmoodirad, 2018; Niroomand, Hadi‐Vencheh, Mirzaei, & Molla‐Alizadeh‐Zavardehi, 2016; Parlak et al, 2019; Sanei, Mahmoodirad, & Niroomand, 2016, Taassori, Niroomand, Uysal, Hadi‐Vencheh, & Vizvari, (2016)). For coping with such problems with uncertain parameters, in existence of the historical information of the uncertain parameters, the approaches like fuzzy theory, stochastic programming, probability theory can be applied.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many studies have considered belief degree‐based uncertainty for modelling uncertain problems. Some important studies on this topic are Ding (2015), Ding and Gao (2014), Gao (2012), Huang and Di (2016), Mahmoodirad, Allahviranloo, and Niroomand (2019), Mahmoodirad, Dehghan, & Niroomand, 2019, and Wen, Guo, Kang, and Yang (2014). In the case of transportation problem, Mou, Zhao, and Chang (2013) studied transportation problem with uncertain transportation times.…”

Section: Introductionmentioning

confidence: 99%

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

Mahmoodirad

Niroomand²

2020

Expert Systems

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In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chanceconstrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.

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A new effective solution method for fully intuitionistic fuzzy transportation problem

Cited by 67 publications

References 26 publications

Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set

Algorithms for solving the optimization problems using fuzzy and intuitionistic fuzzy set

JMD Approach for Solving Unbalanced Fully Trapezoidal Intuitionistic Fuzzy Transportation Problems

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

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