Supplier Selection Using Integrated Fuzzy TOPSIS and MCGP: A Case Study

An Integrated Fuzzy MCDM Hybrid Methodology to Analyze Agricultural Production

A hybrid model was developed by combining multiple-criteria decision-making (MCDM) with the analytic hierarchy process (AHP) and a fuzzy set to give decision support for choosing sustainable solutions to agricultural problems. Six steps were taken to build the suggested hybrid model: identifying and weighing criteria; normalizing data using fuzzy membership functions; calculating the weighting of the criteria using AHP; and selecting the best alternative for the agricultural problem. The objective of this case study is to demonstrate how agricultural production techniques (APTs) are becoming more complex as agricultural production becomes more complex. Organic agriculture aims to protect both the environment and consumer satisfaction by utilizing organic management practices that do not have the negative effects associated with conventional and genetic engineering production. Meanwhile, products obtained through conventional and genetic engineering techniques are more cost-effective. To present the superiority of the proposed fuzzy MCDM hybrid model, this problem is used as the causative agent’s dataset. Because the challenge involves a large number of competing quantitative and qualitative criteria, the assessment approach should improve the ratio of input data to output data. As a result, agricultural productivity should be controlled holistically. However, because the problem may contain both qualitative and quantitative facts and uncertainties, it is necessary to represent the uncertainty inherent in human thinking. To achieve superior outcomes, fuzzy set theory (FST), which enables the expression of uncertainty in human judgments, can be integrated with). The purpose of this study is to present a novel MCDM approach based on fuzzy numbers for analyzing decision-making scenarios. The proposed methodology, which is based on Buckley’s fuzzy analytic hierarchy process (B-FAHP) and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), uses Buckley’s fuzzy analytic hierarchy process (B-FAHP) and fuzzy TOPSIS to determine weights and rank alternatives, respectively. As a result, we attempted to include both the uncertainty and hesitancy of experts in the decision-making process through the use of fuzzy numbers. We have three main criteria in this study: Satisfaction (C1), Economy (C2), and Environment (C3). An important objective of the current research is to build a complete framework for evaluating and grading the suitability of technologies. A real-world case study is used to demonstrate the suggested paradigm’s validity.

An integrated approach to site selection for a big data center using PROMETHEE-MCGP methodology

In today’s economy, information technology (IT) is vitally important, and the increasing use of the Internet, telecommunications services, and internal IT networks in organizations have led to rapid growth in the demands on big data processing. In general, site selection is a fundamental part of the design of a big data center (BDC), and a poor site decision can affect the sustainability of the facility. To construct a comprehensive assessment framework for a BDC, the following three categories of indicators are determined based on the “Specification for Design of Data Center” in GB50174-2017 of China: economic factors, natural climate environment factors, and energy resources factors. After explaining the rationality of choosing these indicators in detail, an integrated method that combines the multi-criteria decision-making (MCDM) method and the multi-choice goal programming (MCGP) model is proposed. The proposed approach uses two phases to conduct the decision procedure. First, the preference ranking organization method for enrichment evaluation (PROMETHEE) method is applied to evaluate the economic factors. Then, the evaluation results are added to the MCGP model as one of the goals of multi-objective programming. Second, the remaining five sub-indicators and the evaluation results generated from the first phase are formulated as a complete MCGP model. Finally, an empirical study on the site selection for the BDC is implemented based on the proposed method. The result shows that Guiyang is the most suitable place for locating a BDC in China.

An Integrated Kano Model, Fuzzy Analytical Hierarchy Process, and Decision Matrix for Sustainable Supplier Selection in Palm Oil Industries Indonesia, a Case Study

Industries have to integrate environmental, social, and economic aspects into their supply chain management to achieve sustainability. Hence, the industry needs to take appropriate actions in choosing the right suppliers. The aim of this study is to develop a framework for selecting sustainable suppliers by integrating quality management tools using the Kano model, Fuzzy Analytical Hierarchy Process, and Decision Matrix Method. To identify the critical sustainability criteria, the Kano model by the clustering the criteria for sustainable selection supplier was used. We then used the Fuzzy Analytical Hierarchy Process to determine the weight of each criterion and applied the Decision Matrix Method to select the most sustainable supplier. Afterward, the appropriate proposed framework was implemented in one of the palm oil industries in Indonesia to validate that the framework is applicable and useful. The study shows that the environmental dimension is the most sustainable supplier criteria followed by economy and social dimension. Quality, pollution control, and information disclosures were found to be important sub-dimensions for sustainable supplier selection.

Evaluating and ranking the digital content generation components for marketing the libraries and information centres’ goods and services using fuzzy TOPSIS technique

Since content audiences, including libraries and information centres, are increasingly geared to digital environments and virtual networks, the production and delivery of high-quality digital content are becoming continuously important. So far, several components have been introduced by researchers for evaluating the quality of digital content generation. However, due to the uncertainty of the importance rate and value of each of these components, it has not yet been possible to use them effectively to evaluate the content produced. This study aimed to rank the components of content generation to allow accurate evaluation of them for users as well as content providers and distributors including libraries and marketers. The ranked content can motivate digital content producers and distributors to better evaluate the quality of digital content, better attract customers and make more effective decisions about the quality of digital content use based on their specific goals. Initially, 42 of the most important components were identified from the literature. Then, the next steps were taken to rank these components, and based on three rounds of Delphi interviews, the experts’ views on the importance rate of each of the components were obtained, analysed and ranked. Since in this ranking, the importance of a wide range of components should be highlighted towards each other, the fuzzy TOPSIS technique was emphasised for analysing the views of 16 experts in the field of content generation in Iran. This ranking indicated that components such as ‘findable and access’, ‘non-disturbing and helpful’, ‘clear’ and ‘remarkable’ are the main pillars of content generation and are of the utmost importance. The results can be used as an effective tool to improve the quality of content. Moreover, it increases audience engagement in digital environments and social networks, and encourages them to make more use of the digital content of libraries.

Suppliers selection based on intuitionistic fuzzy dimensional analysis

Supplier selection is a crucial activity in the supply chain management, and fundamentally it is a multi-criteria decision-making problem. However, the interaction of the criteria, the uncertain regarding to subjectivity and the imprecise information generally present in the provider selection process is still an open issue. The main purpose of this paper is to arise a new method to support the provider selection process. In this sense, this paper introduces the intuitionistic fuzzy version of dimensional analysis under a group decision making and multi-criteria environment applied to suppliers selection. Likewise, a set of decision makers reveal their preferences about the criteria and options in evaluation by mean of a linguistic grade that are mapped via intuitionistic numbers. Consequently, the importance of each decision maker and criteria is considered in the method. Hence, the methodology assumes that exist a choice better than the rest which is called ideal alternative. Each choice in evaluation, is compared with that ideal to induce an index of similarity. Additionally, the alternative with highest index of similarity is proposed as solution to the supplier selection problem. The method offer a systematic mode to deal with the interrelationship between the criteria attached into the decision making sphere. Finally, a numerical example is addressed to supplier selection to facilitate the understanding of the proposed method.

The LR-Type Fuzzy Multi-Objective Vendor Selection Problem in Supply Chain Management

Vendor selection is an established problem in supply chain management. It is regarded as a strategic resource by manufacturers, which must be managed efficiently. Any inappropriate selection of the vendors may lead to severe issues in the supply chain network. Hence, the desire to develop a model that minimizes the combination of transportation, deliveries, and ordering costs under uncertainty situation. In this paper, a multi-objective vendor selection problem under fuzzy environment is solved using a fuzzy goal programming approach. The vendor selection problem was modeled as a multi-objective problem, including three primary objectives of minimizing the transportation cost; the late deliveries; and the net ordering cost subject to constraints related to aggregate demand; vendor capacity; budget allocation; purchasing value; vendors’ quota; and quantity rejected. The proposed model input parameters are considered to be LR fuzzy numbers. The effectiveness of the model is illustrated with simulated data using R statistical package based on a real-life case study which was analyzed using LINGO 16.0 optimization software. The decision on the vendor’s quota allocation and selection under different degree of vagueness in the information was provided. The proposed model can address realistic vendor selection problem in the fuzzy environment and can serve as a useful tool for multi-criteria decision-making in supply chain management.

Development of the critical success decision criteria for an equitable order sharing in an extended enterprise

Purpose

This study identifies and ranks the appropriate critical success decision criteria (CSDC) for the bulk order distribution (sharing) amongst multiple manufacturers (suppliers) working as an extended enterprise (EE).

Design/methodology/approach

The study deploys a qualitative approach to generate the appropriate decision criteria. The balanced scorecard and Pareto's chart (using Minitab® version 18) were used for gathering and analysing the pertinent criteria.

Findings

The process of evaluating and selecting the right manufacturers is essential. Manufacturer (supplier) selection is no longer decided solely based on cost/price criterion; currently, the quality and delivery criteria prevail. Additional incorporated criteria include price/cost, technical capability, production facilities and capacity, customer satisfaction and impression, geographical location, management and organisation, financial position, environmental concern, performance history, repair service, information technology and communication systems, procedural compliance, labour relation record, reputation, flexibility or diversification, attitude, operating controls, business desire, packaging ability, past business records, trust and loyalty, training aids, complaint handling service, warranties and claim policies, reciprocal arrangements, research and development and innovation, modern slavery concern, sustainable capability, collaborative/partnership and responsiveness. The study proposed a conceptual framework of an EE alongside how manufacturers working as a single virtual entity can consider the supply chain operations reference (SCOR®) model.

Research limitations/implications

The identified CSDC are suitable for order allocation to domestic manufacturers. The deployed approaches could be extended to the mixed and quantitative approaches for increasing the generalisability.

Originality/value

The study establishes the pertinent CSDC that are important to execute equitable order distribution to manufacturers in an EE framework.

A multi-criteria sensory assessment of Cucumis melo (L.) using fuzzy-Eckenrode and fuzzy-TOPSIS methods

The paper introduces a multi-criteria assessment system that can be used for sensory analysis by fuzzy-Eckenrode and fuzzy-TOPSIS methods. Respondents evaluated the sensory characteristics of Cucumis melo (L.), which included aroma, colour, taste, texture, and overall acceptance, after six days of storage. The product was stored under three different temperature conditions: 10°C (B1), 14°C (B2), and room temperature (27–30°C) (B3). The product was also stored at three types of packaging: unpackaged stem (A1), packaged fruit with one layer of banana stem (A2), and packaged fruit with two layers of banana stem (A3). The best result was demonstrated by the Cucumis melo that was stored at 14°C and packaged in a two-layered banana stem (A3B2). Both fuzzy-Eckenrode and fuzzy-TOPSIS method provided an easy, fast, and unambiguous calculation of multi-criteria sensory assessment.

A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study

Supplier selection is one of the critical processes in supplier chain management which is associated with the flow of goods and services from the supplier of raw material to the final consumer. The purpose of this paper is to present a novel approach and improves the supplier selection in a multi-item/multi-supplier environment, and provide the importance and the reliability of the criteria by handling vagueness and imperfection of information in decision making process. First, principal component analysis (PCA) method is used to reduce the number of supplier selection criteria in pharmaceutical companies. Next, using the most important criteria resulted from the PCA method, the importance and the reliability of the selected criteria are assessed by a group of decision-maker (DM). Then, the importance value of each supplier with respect to each product is obtained via the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) based on the concept of Z-numbers called Z-TOPSIS. Finally, these values are used as inputs in a mixed integer linear programming (MILP) to determine the suppliers and the amount of the products provided from the related suppliers. To validate the proposed methodology, an application is performed in a pharmaceutical company. The results show that the proposed method could provide promising results in decision making process more appropriately.

Latent human error analysis and efficient improvement strategies by fuzzy TOPSIS in aviation maintenance tasks

The purposes of this study were to develop a latent human error analysis process, to explore the factors of latent human error in aviation maintenance tasks, and to provide an efficient improvement strategy for addressing those errors. First, we used HFACS and RCA to define the error factors related to aviation maintenance tasks. Fuzzy TOPSIS with four criteria was applied to evaluate the error factors. Results show that 1) adverse physiological states, 2) physical/mental limitations, and 3) coordination, communication, and planning are the factors related to airline maintenance tasks that could be addressed easily and efficiently. This research establishes a new analytic process for investigating latent human error and provides a strategy for analyzing human error using fuzzy TOPSIS. Our analysis process complements shortages in existing methodologies by incorporating improvement efficiency, and it enhances the depth and broadness of human error analysis methodology.