Multi-objective vendor managed inventory system with interval type-2 fuzzy demand and order quantities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zubair Ashraf ◽  
Mohammad Shahid
Keyword(s):  
Fuzzy Numbers ◽  
Vendor Managed Inventory ◽  
Content Type ◽  
Multi Objective ◽  
Demand Rate ◽  
Order Quantities ◽  
Interval Type ◽  
Pareto Fronts

PurposeThe proposed IT2FMOVMI model intends to concurrently minimize total cost and warehouse space for the single vendor-retailer, multi-item and a consolidated vendor store. Regarding demand and order quantities with the deterministic and type-1 fuzzy numbers, we have also formulated the classic/crisp MOVMI model and type-1 fuzzy MOVMI (T1FMOVMI) model. The suggested solution technique can solve both crisp MOVMI and T1FMOVMI problems. By finding the optimal ordered quantities and backorder levels, the Pareto-fronts are constructed to form the solution sets for the three models.Design/methodology/approachA multi-objective vendor managed inventory (MOVMI) is the most recognized marketing and delivery technique for the service provider and the retail in the supply chain in Industry 4.0. Due to the evolving market conditions, the characteristics of the individual product, the delivery period and the manufacturing costs, the demand rate and order quantity of the MOVMI device are highly unpredictable. In such a scenario, a MOVMI system with a deterministic demand rate and order quantity cannot be designed to estimate the highly unforeseen cost of the problem. This paper introduces a novel interval type-2 fuzzy multi-objective vendor managed inventory (IT2FMOVMI) system, which uses interval type-2 fuzzy numbers (IT2FNs) to represent demand rate and order quantities. As the model is an NP-hard, the well-known meta-heuristic algorithm named NSGA-II (Non-dominated sorted genetic algorithm-II) with EKM (Enhanced Karnink-Mendel) algorithm based solution method has been established.FindingsThe experimental simulations for the five test problems that demonstrated distinct conditions are considered from the real-datasets of SAPCO company. Experimental study concludes that T1FMOVMI and crisp MOVMI schemes are outclassed by IT2FMOVMI model, offering more accurate Pareto-Fronts and efficiency measurement values.Originality/valueUsing fuzzy sets theory, a significant amount of work has been already done in past decades from various points of views to model the MOVMI. However, this is the very first attempt to introduce type-2 fuzzy modelling for the problem to address the realistic implementation of the imprecise parameters.

An interval type-2 fuzzy ANP approach to evaluate enterprise technological innovation ability

Kybernetes ◽  
2016 ◽  
Vol 45 (9) ◽  
pp. 1486-1500 ◽  
Author(s):  
Tong Wu ◽  
Xinwang Liu
Keyword(s):  
Fuzzy Sets ◽  
Evaluation Model ◽  
Influence Factors ◽  
Content Type ◽  
Fuzzy Anp ◽  
Enterprise Technology ◽  
Innovation Ability ◽  
Interval Type

Purpose The purpose of this paper is to overcome the drawbacks of analytic hierarchy process in solving complex decision-making problems, especially for the evaluation of enterprise technology innovation ability (ETIA). Because interval type-2 fuzzy sets (IT2 FSs) can handle uncertainty linguistic variables in a more flexible and precise way than type-1 fuzzy sets with their second fuzzy membership functions, a fuzzy ANP method with IT2 FSs is proposed to evaluate the ETIA. Design/methodology/approach The criteria of evaluation on ETIA are identified and an evaluation model for ETIA is constructed on the basis of the application analysis of ETIA and theoretical design of ANP. In addition, two different ranking methods of IT2 FSs are applied in processing the relationships between influence factors of ETIA. Findings By using the proposed interval type-2 fuzzy ANP (IT2 FANP) method, the efficiencies of the whole evaluation of ETIA can be measured and the important factors in the ETIA can also be determined. Compared with the type-1 FANP through the ranking results, the proposed IT2 FANP is more reasonable and robust for the evaluation of ETIA. Practical implications The proposed IT2 FANP method is applied on the evaluation of ETIA. With respect to the application, the proposed method can be used to evaluate many more complex problems that contain feedback and circular relationships. Originality/value The proposed IT2 FANP approach can solve the complexities and uncertainties at the same time. Considering the subjective initiative of decision-makers and the feedback between influence factors, the proposed method is more efficient than the existing type-1 approaches in the literature.

Multi-criteria decision-making method based on type-2 fuzzy sets

Filomat ◽  
2017 ◽  
Vol 31 (2) ◽  
pp. 431-450 ◽  
Author(s):  
Jing Wang ◽  
Qing-Hui Chen ◽  
Hong-Yu Zhang ◽  
Xiao-Hong Chen ◽  
Jian-Qiang Wang
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Computational Models ◽  
Fuzzy Numbers ◽  
Multi Criteria Decision Making ◽  
Interval Numbers ◽  
Interval Type ◽  
Uncertainty Information

Type-2 fuzzy sets (T2FSs) are the extension of type-1 fuzzy sets (T1FSs), which can convey more uncertainty information in solving multi-criteria decision-making (MCDM) problems. Motivated by the extension from interval numbers to triangular fuzzy numbers, three-trapezoidal-fuzzy-number-bounded type-2 fuzzy numbers (TT2FNs) are defined on the basis of interval type-2 trapezoidal fuzzy numbers (IT2TFNs), and they can convey more uncertainty information than T1FSs and IT2FSs. Moreover, the drawbacks of the existing computational models of generalized fuzzy numbers are analyzed, and a new computational model of fuzzy numbers is proposed, which is further extended to TT2FNs. Besides, a MCDM method is proposed to deal with the evaluation information given in the form of TT2FNs. Finally, an illustrative example and comparison analysis are provided to demonstrate the feasibility and validity of the proposed method.

Comparison of modified Karnik-Mendel algorithm-based interval type-2 ANFIS and type-1 ANFIS

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammet Öztürk ◽  
İbrahim Özkol
Keyword(s):  
Fuzzy Inference ◽  
Nonlinear Problems ◽  
Mathematical Operation ◽  
Inference System ◽  
Content Type ◽  
Highly Nonlinear ◽  
Interval Type ◽  
First Time

Purpose This study aims to propose, as the first time, the interval type-2 adaptive network-fuzzy inference system (ANFIS) structure, which is given better results compared to previously presented in the open literature. So, the ANFIS can be used effectively for training of interval type-2 fuzzy logic system (IT2FLS) parameters. Design/methodology/approach Karnik–Mendel algorithm (KMA) is modified to use in interval type-2 ANFIS. The modified Karnik–Mendel algorithm (M-KMA) is implemented to change the uncertain ANFIS parameters into known ones. In this way, the interval type-2 ANFIS removes uncertainties of IT2FLS. Therefore, the interval type-2 ANFIS is reduced to a simple one, i.e. less mathematical operation required. Only consequent parameters are trained, and the consequent parameters are chosen in the form of crisp. Findings By applying the mentioned procedure, it can be shown that interval type-2 ANFIS has generally better results compared to type-1 ANFIS. However, it was noticed that the worst results obtained in the case of interval type-2 ANFIS are equal to the best result obtained in the case of type-1 ANFIS. Therefore, users in this field can use this approach in solving nonlinear problems. Practical implications The interval type-2 ANFIS can be used as controller for highly nonlinear systems such as air vehicles. Originality/value As stated in the open literature, it is ineffective to use ANFIS for IT2FLS. In this study, the KMA is modified for IT2FLS, and it is seen that the ANFIS can be used effectively for IT2FLS.

Different methods to fuzzy X¯-R control charts used in production

2018 ◽  
Vol 31 (6) ◽  
pp. 848-866 ◽  
Author(s):  
Hatice Ercan Teksen ◽  
Ahmet Sermet Anagun
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Quality Characteristic ◽  
Content Type ◽  
Fuzzy Methods ◽  
Interval Type ◽  
Control Limits

PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits ofX¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to theX¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used inX¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to theX¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods onX¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered forX¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such asX¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets onX¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

scholarly journals Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Nur Hidayah Mohd Razali ◽  
Lazim Abdullah ◽  
Zabidin Salleh ◽  
Ahmad Termimi Ab Ghani ◽  
Bee Wah Yap
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Cusum Chart ◽  
Statistical Process ◽  
Interval Type

Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

A New Method for Ranking Interval Type-2 Fuzzy Numbers Based on Mellin Transform

2020 ◽  
Vol 28 (04) ◽  
pp. 591-611
Author(s):  
Yanbing Gong ◽  
Lin Xiang ◽  
Shuxin Yang ◽  
Hailiang Ma
Keyword(s):  
Fuzzy Sets ◽  
Degrees Of Freedom ◽  
Mellin Transform ◽  
Fuzzy Numbers ◽  
Mean Value ◽  
Distance Measures ◽  
Mean Values ◽  
Interval Type

Interval type-2 fuzzy sets provide us with additional degrees of freedom to represent the uncertainty and the fuzziness of the real word than traditional type-1 fuzzy sets. Interval type-2 fuzzy numbers ranking has an important role in the decision making analysis. In this paper, the probatilistic mean value and variance of interval type-2 fuzzy numbers are proposed based on the Mellin transform for type-1 fuzzy numbers. The interval type-2 fuzzy number with the higher mean is ranked higher. If the mean values are equal the one with the smaller variance is judged higher rank. On this basis, some new distance measures and possibility degree formula are proposed to comparing interval type-2 fuzzy numbers based on their Mellin mean value and variance. Some benchmarking numerical examples are given, and some interpretation issues are explained.

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

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