Developing a novel Grey integrated multi-criteria approach for enhancing the supplier selection procedure: A real-world case of Textile Company

upplier selection is one of the most essential activities in purchase management and plays a crucial role in the production phase. Supplier selection as a vital step of supply chain management is a multi-criteria decision-making issue. For any organization, the process of selecting the best supplier holds variable multilayered complications involving quantitative and qualitative criteria. This paper tackles the supplier selection problem in a Turkish Textile Company. The present study carries out a novel grey integrated multi-criteria approach for enhancing the supplier procedure within Textile Company with the help of the grey analytical hierarchy process G-AHP model for weighting the set of criteria, and the grey weighted aggregated sum product assessment WASPAS-G model for prioritizing the suppliers. The study starts with reviewing the previous works of multi-criteria decision-making MCDM methods and the list of existing criteria evaluation in supplier selection. Then, the range of criteria is selected based on the company requirements and the experts’ interview. In the case study, the consistency rate of the models is tested in order to verify the quality of experts’ judgments. The final results affirm that Grey integrated approach could be efficient and far more precise than the existing models for overcoming the supplier selection and evaluation obstacles in the supply chain management

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Improving the process of coal extraction based on the parameter optimization of mining equipment

The aim of this paper is to develop and validate methods of choosing the means of the mining face mechanization. This paper analyses existing methods of optimizing processes in mining. It was established that the effectiveness of the performance map of coal field is formed by a group of technological, operational, and economic parameters which can be represented as a vector of solutions. To find the optimal solution, it was suggested to use network models and graphs. The essence of the technique is to represent the input and output (production level, prime cost) resource flows in an organized structure. Regularities of forming technological schemes of coalfield operation with a given level of performance, taking into account the relationship between technological parameters of mining face, operational parameters of the stoping equipment, technical and economic performance are defined. We developed the system for decision- making support, which allows optimizing operational parameters, reducing the production prime cost, and selecting the structure of the mechanized complex of stoping equipment with a specified level of performance. This paper describes approaches that can be used at the design stage of mining face and in the process of operation

The reflective learning continuum: reflecting on reflection

The importance of reflection to marketing educators is increasingly recognized. However, there is a lack of empirical research which considers reflection within the context of both the marketing and general business education literature. This paper describes the use of an instrument which can be used to measure four identified levels of a reflection hierarchy: habitual action, understanding, reflection and intensive reflection and two conditions for reflection: instructor to student interaction and student to student interaction. Further we demonstrate the importance of reflective learning in predicting graduates’ perception of program quality. Although the focus was on assessment of MBA level curricula, the findings have great importance to marketing education and educators

Is it time to withdraw from china?

This research cross-employs the Social Cognitive Theory (SCT) and three major labor theories comprised of Maslow’s theory, Alderfer’s theory and Herzberg’s theory with Multiple Criteria Decision Making (MCDM) consisting of Factor Analysis (FA), Analytical Network Process (“ANP”), Fuzzy Analytical Network Process (FANP) and Grey Relation Analysis (GRA) to evaluate the four types of innovative investment strategies in China after the Domino Effect of the China’s Labor Revolution. The most contributed conclusion is that the “change of original business at the raising compensation policy” (CBRCP) is the best choice for Taiwanese manufacturers operating in China because it is the highest scores of three assessed measurements in the CBRCP. This conclusion further indicates that manufacturing enterprises have little leverage, in the interim, but to increase employment compensation and benefits to satisfy the demands from the ongoing Chinese labor revolution even though it brings about an incremental expenditure in their manufacturing costs. Therefore, the next step beyond this research is to collect additional empirical macroeconomic data to develop a more comprehensive evaluation model that takes into consideration a more in-depth vertical measurement and horizontal assessment methodologies for developing added comprehensive and effective managerial strategies for surviving in this momentous, dynamically-changing and lower-profit Chinese manufacturing market.China labor revolution; Maslow theory; Alderfer theory and Herzberg theory; Multiple criteria decision making

Development, test and comparison of two Multiple Criteria Decision Analysis(MCDA) models: A case of healthcare infrastructure location

When planning a new development, location decisions have always been a major issue. This paper examines and compares two modelling methods used to inform a healthcare infrastructure location decision. Two Multiple Criteria Decision Analysis (MCDA) models were developed to support the optimisation of this decision-making process, within a National Health Service (NHS) organisation, in the UK. The proposed model structure is based on seven criteria (environment and safety, size, total cost, accessibility, design, risks and population profile) and 28 sub-criteria. First, Evidential Reasoning (ER) was used to solve the model, then, the processes and results were compared with the Analytical Hierarchy Process (AHP). It was established that using ER or AHP led to the same solutions. However, the scores between the alternatives were significantly different; which impacted the stakeholders‟ decision-making. As the processes differ according to the model selected, ER or AHP, it is relevant to establish the practical and managerial implications for selecting one model or the other and providing evidence of which models best fit this specific environment. To achieve an optimum operational decision it is argued, in this study, that the most transparent and robust framework is achieved by merging ER process with the pair-wise comparison, an element of AHP. This paper makes a defined contribution by developing and examining the use of MCDA models, to rationalise new healthcare infrastructure location, with the proposed model to be used for future decision. Moreover, very few studies comparing different MCDA techniques were found, this study results enable practitioners to consider even further the modelling characteristics to ensure the development of a reliable framework, even if this means applying a hybrid approach

Environmental screening tools for assessment of infrastructure plans based on biodiversity preservation and global warming (PEIT, Spain).

Most Strategic Environmental Assessment (SEA) research has been concerned with SEA as a procedure, and there have been relatively few developments and tests of analytical methodologies. The first stage of the SEA is the ‘screening’, which is the process whereby a decision is taken on whether or not SEA is required for a particular programme or plan. The effectiveness of screening and SEA procedures will depend on how well the assessment fits into the planning from the early stages of the decision-making process. However, it is difficult to prepare the environmental screening for an infrastructure plan involving a whole country. To be useful, such methodologies must be fast and simple. We have developed two screening tools which would make it possible to estimate promptly the overall impact an infrastructure plan might have on biodiversity and global warming for a whole country, in order to generate planning alternatives, and to determine whether or not SEA is required for a particular infrastructure plan

Application of Grey Wolf Optimizer Algorithm for Optimal Power Flow of Two-Terminal HVDC Transmission System

This paper applies a relatively new optimization method, the Grey Wolf Optimizer (GWO) algorithm for Optimal Power Flow (OPF) of twoterminal High Voltage Direct Current (HVDC) electrical power system. The OPF problem of pure AC power systems considers the minimization of total costs under equality and inequality constraints. Hence, the OPF problem of integrated AC-DC power systems is extended to incorporate HVDC links, while taking into consideration the power transfer control characteristics using a GWO algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. The proposed algorithm is applied to two different case-studies: the modified 5-bus and WSCC 9-bus test systems. The validity of the proposed algorithm is demonstrated by comparing the obtained results with those reported in literature using other optimization techniques. Analysis of the obtained results show that the proposed GWO algorithm is able to achieve shorter CPU time, as well as minimized total cost when compared with already existing optimization techniques. This conclusion proves the efficiency of the GWO algorithm