A hybrid method of GRA and DEA for evaluating and selecting efficient suppliers plus a novel ranking method for grey numbers

Purpose: Evaluation and selection of efficient suppliers is one of the key issues in supply chain management which depends on wide range of qualitative and quantitative criteria. The aim of this research is to develop a mathematical model for evaluating and selecting efficient suppliers when faced with supply and demand uncertainties. Design/methodology/approach: In this research Grey Relational Analysis (GRA) and Data Envelopment Analysis (DEA) are used to evaluate and select efficient suppliers under uncertainties. Furthermore, a novel ranking method is introduced for the units that their efficiencies are obtained in the form of interval grey numbers. Findings: The study indicates that the proposed model in addition to providing satisfactory and acceptable results avoids time-consuming computations and consequently reduces the solution time. To name another advantage of the proposed model, we can point out that it enables us to make decision based on different levels of risk. Originality/value: The paper presents a mathematical model for evaluating and selecting efficient suppliers in a stochastic environment so that companies can use in order to make better decisions.Peer Reviewe

Modelling multicriteria value interactions with Reasoning Maps

Idiographic causal maps are extensively employed in Operational Research to support problem structuring and complex decision making processes. They model means-end or causal discourses as a network of concepts connected by links denoting influence, thus enabling the representation of chains of arguments made by decision-makers. There have been proposals to employ such structures to support the structuring of multicriteria evaluation models, within an additive value measurement framework. However, a drawback of this multi-methodological modelling is the loss of richness of interactions along the means-end chains when evaluating options. This has led to the development of methods that make use of the structure of the map itself to evaluate options, such as the Reasoning Maps method, which employs ordinal scales and ordinal operators for such evaluation. However, despite their potential, Reasoning Maps cannot model explicitly value interactions nor perform a quantitative ranking of options, limiting their applicability and usefulness. In this article we propose extending the Reasoning Maps approach through a multilinear evaluation model structure, built with the MACBETH multicriteria method. The model explicitly captures the value interactions between concepts along the map and employs the MACBETH protocol of questioning to assess the strength of influence for each means-end link. The feasibility of the proposed approach to evaluate options and to deal with multicriteria interactions is tested in a real-world application to support the construction of a population health index

Ranking and aggregation-based multiple attributes decision making method for sustainable energy planning

In sustainable energy planning, the selection of a suitable Renewable Energy Sources (RES) for energy supply and evaluation of different RES technologies is a complex decision-making process. This is because there are many conflicting criteria that need to be considered. It becomes more complicated when qualitative data is involved in addition to quantitative data. Previous studies use Multiple Attribute Decision Making (MADM) methods for decision making, which work well with quantitative data but not with qualitative data. There are some MADM methods that can handle with both qualitative and quantitative data but suffer from complex computation burden. It becomes more difficult when more than one MADM method or more than one Decision Maker (DM) need to be considered. Different results will be obtained since different MADM methods or different DMs provide different results. This thesis proposes a new MADM method to overcome the limitations of previous methods. It consists of two parts which are ranking and aggregation techniques. The proposed ranking technique able to deal with quantitative and qualitative data through sorting process according to beneficial and non-beneficial criteria without normalizing the data. Then the proposed aggregation technique able to overcome the problem of different rankings due to different MADM methods or different DMs. The idea is to modify the preference ranking organization method for enrichment evaluations, where a preference index is assigned when comparing two alternatives at one time with respect to their ranking position instead of the criteria. Four case studies are examined to illustrate the effectiveness of the proposed ranking method while three case studies are evaluated to demonstrate the applications of the proposed aggregation method. For verification, Spearman’s rank correlation coefficient is utilized to determine an agreement of the proposed method with the existing MADM methods. The results show the strength of the proposed method as it yields a correlation coefficient of more than 0.87 in all case studies. The results show an excellent correlation with those obtained by past researchers, which specifically prove the applicability of the proposed method for solving sustainable energy planning decision problem

Prioritizing Offshore Vendor Selection Criteria for the North American Geospatial Industry

The U.S. market for geospatial services totaled US $2.2 billion in 2010, representing 50% of the global market. Data-processing firms subcontract labor-intensive portions of data services to offshore providers in South and East Asia and Eastern Europe. In general, half of all offshore contracts fail within the first 5 years because one or more parties consider the relationship unsuccessful. Despite the high failure rates, no study has examined the offshore vendor selection process in the geospatial industry. The purpose of this study was to determine the list of key offshore vendor selection criteria and the efficacy of the analytic hierarchy process (AHP) for ranking the criteria that North American geospatial companies consider in the offshore vendor selection process. After the selection of the initial list of factors from the literature and their validation in a pilot study, a final survey instrument was developed and administered to 15 subject matter experts (SMEs) in North America. The SMEs expressed their preferences for one criterion over another by pairwise comparisons, which served as input to the AHP procedure. The results showed that the quality of deliverables was the top ranked (out of 26) factors, instead of the price, which ranked third. Similarly, SMEs considered social and environmental consciousness on the vendor side as irrelevant. More importantly, the findings indicated that the structured AHP process provides a useful and effective methodology whose application may considerably improve the quality of the overall vendor selection process. Last, improved and stabilized business relationships leading to predictable budgets might catalyze social change, supporting stable employment. Consumers could benefit from derivative improvements in product quality and pricing

New Progress of Grey System Theory in The New Millennium

Purpose – The purpose of this paper is to summarize the progress in grey system research during 2000- 2015, so as to present some important new concepts, models, methods and a new framework of grey system theory. Design/methodology/approach –The new thinking, new models and new methods of grey system theory and their applications are presented in this paper. It includes algorithm rules of grey numbers based on the “Kernel” and the degree of greyness of grey numbers, the concept of general grey numbers, the synthesis axiom of degree of greyness of grey numbers and their operations; the general form of buffer operators of grey sequence operators; the four basic models of GM(1,1), such as Even Grey Model(EGM), Original Difference Grey Model(ODGM), Even Difference Grey Model(EDGM), Discrete Grey Model(DGM) and the suitable sequence type of each basic model, and suitable range of most used grey forecasting models; the similarity degree of grey incidences, the closeness degree of grey incidences and the three dimensional absolute degree of grey incidence of grey incidence analysis models; the grey cluster model based on center-point and end-point mixed triangular whitenization functions; the multi-attribute intelligent grey target decision model, the two stages decision model with grey synthetic measure of grey decision models; grey game models, grey input-output models of grey combined models; and the problems of robust stability for grey stochastic time-delay systems of neutral type, distributed-delay type and neutral distributed-delay type of grey control, etc. And the new framework of grey system theory is given as well. Findings –The problems which remain for further studying are discussed at the end of each section. The reader could know the general picture of research and developing trend of grey system theory from this paper. Practical implications – A lot of successful practical applications of the new models to solve various problems have been found in many different areas of natural science, social science, and engineering, including spaceflight, civil aviation, information, metallurgy, machinery, petroleum, chemical industry, electrical power, electronics, light industries, energy resources, transportation, medicine, health, agriculture, forestry, geography, hydrology, seismology, meteorology, environment protection, architecture, behavioral science, management science, law, education, military science, etc. These practical applications have brought forward definite and noticeable social and economic benefits. It demonstrates a wide range of applicability of grey system theory, especially in the situation where the available information is incomplete and the collected data are inaccurate. Originality/value –The reader is given a general picture of grey systems theory as a new model system and a new framework for studying problems where partial information is known; especially for uncertain systems with few data points and poor information. The problems remaining for further studying are identified at the end of each section. Keywords Grey systems theory, Operations of grey numbers, Buffer operators, Grey forecasting models, Grey incidence analysis models, Grey cluster evaluation models, Grey decision models, Combined grey models, Grey contro

A Fuzzy Modelling of a Hybrid MCDM Method for Supplier Selection = Egy hibrid MCDM-módszer fuzzy modellezése a beszállítók kiválasztásához

This article examines the significance of supplier selection in the procurement process, which has grown in prominence as a result of globalization and outsourcing. When selecting the best suppliers, supply chain managers must consider a variety of quantitative and qualitative aspects, as these have a substantial impact on supply chain performance. Multi-criteria decision-making (MCDM) approaches can help in this process by taking into account many competing considerations. However, due to uncertainties and ambiguity, supplier selection is a complex process, and fuzzy multi-criteria decision-making approaches can be used to determine the best supplier for the company's key operations. This research suggests a hybrid MCDM strategy that makes use of fuzzy modeling to help with complex decision-making processes. Organizations can improve their supply chain performance by selecting the best supplier based on numerous parameters such as cost, quality, delivery time, and supplier reputation. Jelen tanulmány a beszállítók kiválasztásának jelentőségét vizsgálja a beszerzési folyamatban, amely a globalizáció és a kiszervezés következtében egyre nagyobb jelentőségre tett szert. A legjobb beszállítók kiválasztásakor az ellátási lánc vezetőinek számos mennyiségi és minőségi szempontot kell figyelembe venniük, mivel ezek jelentős hatással vannak az ellátási lánc teljesítményére. A többkritériumos döntéshozatal (MCDM) megközelítések számos egymással versengő szempont figyelembevételével segíthetnek ebben a folyamatban. A bizonytalanságok és a többértelműség miatt azonban a beszállító kiválasztása összetett folyamat, és a fuzzy többkritériumú döntéshozatali megközelítések segítségével meghatározható a vállalat kulcsfontosságú műveleteihez legmegfelelőbb beszállító. Ez a kutatás egy hibrid MCDM stratégiát javasol, amely a fuzzy modellezést használja fel a komplex döntéshozatali folyamatok segítésére. A szervezetek számos paraméter, például a költségek, a minőség, a szállítási idő és a beszállító hírneve alapján a legjobb beszállító kiválasztásával javíthatják ellátási láncuk teljesítményét

Value of agreement in decision analysis: concept, measures and application

In multi-criteria decision analysis workshops, participants often appraise the options individually before discussing the scoring as a group. The individual appraisals lead to score ranges within which the group then seeks the necessary agreement to identify their preferred option. Preference programming enables some options to be identified as dominated even before the group agrees on a precise scoring for them. Workshop participants usually face time pressure to make a decision. Decision support can be provided by flagging options for which further agreement on their scores seems particularly valuable. By valuable, we mean the opportunity to identify other options as dominated (using preference programming) without having their precise scores agreed beforehand. The present paper quantifies this Value of Agreement and extends the concept to portfolio decision analysis and criterion weights. The new concept is validated through a case study in recruitment

Data-driven Preference Learning Methods for Multiple Criteria Sorting with Temporal Criteria

The advent of predictive methodologies has catalyzed the emergence of data-driven decision support across various domains. However, developing models capable of effectively handling input time series data presents an enduring challenge. This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. We first formulate a convex quadratic programming model characterized by fixed time discount factors, operating within a regularization framework. Additionally, we propose an ensemble learning algorithm designed to consolidate the outputs of multiple, potentially weaker, optimizers, a process executed efficiently through parallel computation. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN). It is designed to capture the evolving dynamics of preferences over time while upholding critical properties inherent to MCS problems, including criteria monotonicity, preference independence, and the natural ordering of classes. The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time, effectively amalgamating the interpretability of traditional MCS methods with the predictive potential offered by deep preference learning models. Comprehensive assessments of the proposed models are conducted, encompassing synthetic data scenarios and a real-case study centered on classifying valuable users within a mobile gaming app based on their historical in-app behavioral sequences. Empirical findings underscore the notable performance improvements achieved by the proposed models when compared to a spectrum of baseline methods, spanning machine learning, deep learning, and conventional multiple criteria sorting approaches