An Integrated Framework for the Performance Evaluation of Fruits and Vegetable Store Located in a Supermarket
DOI:
https://doi.org/10.48048/tis.2022.2073Keywords:
Fruits, Vegetables, MCDM, Fuzzy AHP, Fuzzy TOPSIS, k-means clusteringAbstract
The quality of fruits and vegetable stores should be maintained with high priority for customer satisfaction. The performance evaluation of fruits and vegetable store located in a supermarket is a big challenge for the managerial personnel of the supermarket. In this paper, a new performance evaluation framework is proposed for the fruits and vegetable store located in a supermarket. The criteria for performance evaluation have been found out in a hierarchical structure through a brainstorming session among the experts. The 4 top-level criteria are storage, processing, sales and transport. These 4 top-level criteria are broken into 9 lower-level criteria. Fuzzy AHP is used to calculate the weights of criteria for each level of the hierarchy. Fuzzy TOPSIS generally ranks the alternatives. An improved fuzzy TOPSIS, which is named fuzzy k-TOPSIS, is proposed here to find out the rank as well as classification of the stores of fruits and vegetables. The proposed framework is demonstrated here with a case study for a better understanding of the complete framework.
HIGHLIGHTS
- The proposed framework integrates Fuzzy Set Theory, Analytic Hierarchy Process (AHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and k-means clustering for the performance evaluation of fruits and vegetable store located in a supermarket
- The main contribution of this paper is that the proposed framework not only ranks the alternative fruits and vegetable stores but also does a classification among the alternative fruits and vegetable stores based on their performance
- The proposed framework is demonstrated here with an illustrative example, which includes 24 stores of fruits and vegetables, 9 criteria, and the expert’s committee of 3 members
- The proposed framework can also be used in other Multi-criteria Decision Making (MCDM) problems
GRAPHICAL ABSTRACT
Downloads
Metrics
References
AH Maslow. A theory of human motivation. Psychol. Rev. 1943; 50, 370-96.
JA Pennington and RA Fisher. Classification of fruits and vegetables. J. Food Compos. Anal. 2009; 22, S23-S31.
S Strom. Weak links in China’s food chain. New York Times, Available at: http://www.nytimes.com/2014/08/01/business/international/weak-links-inchinas-food-chain.html?_r=0, accessed February 2019.
A Saba, E Moneta, M Peparaio, F Sinesio, M Vassallo and F Paoletti. Towards a multi-dimensional concept of vegetable freshness from the consumer’s perspective. Food Qual. Preference 2018; 66, 1-12.
Y Wang, W Ji and SS Chaudhry. A hybrid approach for the evaluation of supermarket food safety. J. Manag. Analytics 2014; 1, 156-67.
GM Duman, O Tozanli, E Kongar and SM Gupta. A holistic approach for performance evaluation using quantitative and qualitative data: A food industry case study. Expert syst. Appl. 2017; 81, 410-22.
RD Raut, BB Gardas, M Kharat and B Narkhede. Modeling the drivers of post-harvest losses-MCDM approach. Comput. Electron. Agr. 2018; 154, 426-33.
RD Raut, BB Gardas, VS Narwane and BE Narkhede. Improvement in the food losses in fruits and vegetable supply chain-a perspective of cold third-party logistics approach. Oper. Res. Perspect. 2019; 6, 100117.
TL Saaty. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008; 1, 83-98.
YJ Lai, TY Liu and CL Hwang. Topsis for MODM. Eur. J. Oper. Res. 1994; 76, 486-500.
EK Zavadskas, Z Turskis, J Tamosaitiene and V Marina. Selection of construction project managers by applying COPRAS-G method. Comput. Model. New Tech. 2008; 12, 22-8.
P Karande and S Chakraborty. Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Mater. Des. 2012; 37, 317-24.
A Azimifard, SH Moosavirad and S Ariafar. Selecting sustainable supplier countries for Iran’s steel industry at three levels by using AHP and TOPSIS methods. Resources Pol. 2018; 57, 30-44.
M Ligus and P Peternek. Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energ. Proc. 2018; 153, 101-6.
MC Hsieh, EMY Wang, WC Lee, LW Li, CY Hsieh, W Tsai and TC Liu. Application of HFACS, fuzzy TOPSIS, and AHP for identifying important human error factors in emergency departments in Taiwan. Int. J. Ind. Ergon. 2018; 67, 171-9.
A Khoshi, HS Gooshki and N Mahmoudi. The data on the effective qualifications of teachers in medical sciences: An application of combined fuzzy AHP and fuzzy TOPSIS methods. Data Brief 2018; 21, 2689-93.
K Mandic, B Delibasic, S Knezevic and S Benkovic. Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Econ. Model. 2014; 43, 30-7.
RP Kusumawardani and M Agintiara. Application of fuzzy AHP-TOPSIS method for decision making in human resource manager selection process. Proc. Comput. Sci. 2015; 72, 638-46.
O Taylan, AO Bafail, RM Abdulaal and MR Kabli. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl. Soft Comput. 2014; 17, 105-16.
H Karahalios. The application of the AHP-TOPSIS for evaluating ballast water treatment systems by ship operators. Transport. Res. Transport. Environ. 2017; 52, 172-84.
J Wang, K Fan and W Wang. Integration of fuzzy AHP and FPP with TOPSIS methodology for aeroengine health assessment. Expert Syst. Appl. 2010; 37, 8516-26.
LA Zadeh. Fuzzy sets. Inform. Contr. 1965; 8, 338-53.
TL Saaty. What is the analytic hierarchy process? In: G Mitra, HJ Greenberg, FA Lootsma, MJ Rijkaert and HJ Zimmermann (Eds.). Mathematical models for decision support. Springer, Berlin, Heidelberg, 1988, p. 109-121.
JJ Buckley. Fuzzy hierarchical analysis. Fuzzy Set. Syst. 1985; 17, 233-47.
A Yazdani-Chamzini and SH Yakhchali. Tunnel Boring Machine (TBM) selection using fuzzy multifactors decision making methods. Tunnelling and Underground Space Tech. 2012; 30, 194-204.
CL Hwang and K Yoon. Methods for multiple attribute decision making. In: CL Hwang and K Yoon (Eds.). multiple attribute decision making. lecture notes in economics and mathematical systems. Vol 186. Springer, Berlin, Heidelberg, 1981, p. 58-191.
K Yoon. A reconciliation among discrete compromise solutions. J. Oper. Res. Soc. 1987; 38, 277-86.
S Nădăban, S Dzitac and I Dzitac. Fuzzy topsis: A general view. Proc. Comput. Sci. 2016; 91, 823-31.
J MacQueen. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. California. 1967, p. 281-97.
H Steinhaus. Sur la division des corpmateriels en parties. Bull. Acad. Polon. Sci. 1956; 4, 801-4.
S Dewangan, S Gangopadhyay and CK Biswas. Study of surface integrity and dimensional accuracy in EDM using Fuzzy TOPSIS and sensitivity analysis. Measurement 2015; 63, 364-76.
R Simanavičienė and V Petraitytė. Sensitivity Analysis of the TOPSIS method in respect of initial data distributions. Lithuanian J. Stat. 2016; 55, 45-51.
T Chai and RR Draxler. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci. Model Deve. 2014; 7, 1247-50.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
