348 research outputs found
A state-of-art survey on TQM applications using MCDM techniques
In today’s competitive economy, quality plays an essential role for the success business units and there are considerable efforts made to control and to improve quality characteristics in order to satisfy customers’ requirements. However, improving quality is normally involved with various criteria and we need to use Multi Criteria Decision Making (MCDM) to handle such cases. In this state-of the-art literature survey, 45 articles focused on solving quality problems by MCDM methods are investigated. These articles were published between 1994 and 2013.Seven areas were selected for categorization: (1) AHP, Fuzzy AHP, ANP and Fuzzy ANP, (2) DEMATEL and Fuzzy DEMATEL, (3) GRA, (4) Vikor and Fuzzy Vikor, (5) TOPSIS, Fuzzy TOPSIS and combination of TOPSIS and AHP, (6) Fuzzy and (7) Less frequent and hybrid procedures. According to our survey, Fuzzy based methods were the most popular technique with about 40% usage among procedures. Also AHP and ANP were almost 20% of functional methods. This survey ends with giving recommendation for future researches
Investigation on Multi-Criteria Decision Making Methods Application in Sustainable Product Design
Background: Integrating sustainability development' aspects in the design process is becoming, a growth area in companies. Consequently, sustainable product design has to consider the different aspects of sustainability throughout its life cycle phases in addition of other requirements. This integration is becoming more complicated due the difficulty of managing the constraints and alternatives related to the product and stakeholders needs. This study aims to highlights the most used Multi-Criteria Decision Making (MCDM) tools and methods used in sustainable product design process.
Contribution: Product design process involves interesting decisional tasks such as the choice of materials, standard parts, technical solutions. Hence, the contribution of this work is to help designer to adopt relevant MCDM tools and methods that can be integrated to other tools to facilitate and to justify their decisional tasks.
Method: Several methods have been affected to solve the problems related to this complexity such as MCDM. A literature review was conducted based on Siencedirect and GoogleScholar articles databases. After filtering more than 200 articles only 62 articles were considered to analyze the correlation between sustainable product design and MCDM.
Results: Classified MCDM use according to the type of choices to achieve SPD goals. This paper allowed us to find matches between MCDM methods and SPD problem. The majority of case studies result show that a large portion of sustainable design methods, techniques, and tools are applied to the sustainable product’ along its different life cycle phases
Conclusion: It is noticed that the use of MCDM methods are an important outcome in the sustainable product design process and deeply helps designers to make suitable choices. Also, several matches relating MCDM, other methods and sustainable product design sphere are discusse
A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony
Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers
A three-stage methodology for design evaluation in product development
In order to remain competitive in today's technologically driven world, the faster and more efficient development of innovative products has become the focus for manufacturing companies. In tandem with this, design evaluation plays.a critical role in the early phases of product development, because it has significant impact on the downstream development processes as well as on the success of the product being developed. Owing to the pressure of primary factors, such as customer expectations, technical specifications and cost and time constraints, designers have to adopt various techniques for evaluating design alternatives in order to make the right decisions as early as possible. In this work, a novel three-stage methodology for design evaluation has been developed. The preliminary stage screens all the criteria from different viewpoints using House of Quality (HoQ). The second stage uses a Fuzzy-Analytical Hierarchy Process (Fuzzy-AHP) to obtain the alternatives weighting and the final stage verifies the ranking of the alternatives by a Rough-Grey Analysis. This method will enable designers to make better-informed decisions before finalising their choice. Case examples from industry are presented to demonstrate the efficacy of the proposed methodology. The result of the examples shows that the integration of Fuzzy-AHP with H0Q and Rough-Grey Analysis provides a novel alternative to existing methods of design evaluation
A manufacturing system energy-efficient optimisation model for maintenance production workforce size determination using integrated fuzzy logic and quality function deployment approach
In maintenance systems, the current approach to workforce analysis entails the
utilisation of metrics that focus exclusively on workforce cost and productivity. This
method omits the “green” concept, which principally hinges on energy-efficient
manufacturing and also ignores the production-maintenance integration. The approach is
not accurate and could not be heavily relied upon for sound maintenance decisions.
Consequently, a comprehensive, scientifically-motivated, cost-effective and an
environmentally-conscious approach are needed. With this in view, a deviation from the
traditional approach through employing a combined fuzzy, quality function deployment
interacting with three meta-heuristics (colliding bodies optimisation, big-bang big-crunch
and particle swarm optimisation) for optimisation is made in the current study. The
workforce size parameters are determined by maximising workforce size’s earned-valued
as well as electric power efficiency maximisation subject to various real-life constraints.
The efficacy and robustness of the model is tested with data from an aluminium products
manufacturing system operating in a developing country. The results obtained indicate
that the proposed colliding bodies’ optimisation framework is effective in comparison
with other techniques. This implies that the proposed methodology potentially displays
tremendous benefit of conserving energy, thus aiding environmental preservation and cost
of energy savings. The principal novelty of the paper is the uniquely new method of
quantifying the energy savings contributions of the maintenance workforc
Resilience-enhancing solution to mitigate risk for sustainable supply chain-an empirical study of elevator manufacturing
As the complexity of supply chains increases, the enhancement of resilience for mitigating sustainable disruption risks in supply chains is an important issue. Quality function deployment (QFD) has been successfully applied in many domains to solve multicriteria decision-making (MCDM) problems. However, research on developing two houses of quality to connect sustainable supply chain disruption risks, resilience capacities, and resilience-enhancing features in elevator manufacturing supply chains by using the MCDM approach is lacking. This study aims to develop a framework for exploring useful decision-making by integrating the MCDM approach and QFD. By applying the framework, supply chain resilience can be improved by identifying the major sustainable risks and the key resilience to mitigate these risks. Important managerial insights and practical implications are obtained from the framework implementation in a case study of the elevator manufacturing industry. To strengthen resilience and thus mitigate key risks, the most urgent tasks are to connect the working site and the backstage to enhance product development and design and to share real-time job information. When these features are strengthened, agility, capacity, and visibility can be improved. Finally, unexpected events lead to changes in supplier delivery dates, and factors such as typhoon and lack of critical capacities/skilled employees with the greatest impact can be alleviated. This framework will provide an effective and pragmatic approach for constructing sustainable supply chain risk resilience in the elevator manufacturing industry.</p
Social sustainable supplier evaluation and selection: a group decision-support approach
Organisational and managerial decisions are influenced by corporate sustainability pressures. Organisations need to consider economic, environmental and social sustainability dimensions in their decisions to become sustainable. Supply chain decisions play a distinct and critical role in organisational good and service outputs sustainability. Sustainable supplier selection influences the supply chain sustainability allowing many organisations to build competitive advantage. Within this context, the social sustainability dimension has received relatively minor investigation; with emphasis typically on economic and environmental sustainability. Neglecting social sustainability can have serious repercussions for organisational supply chains. This study proposes a social sustainability attribute decision framework to evaluate and select socially sustainable suppliers. A grey-based multi-criteria decision-support tool composed of the ‘best-worst method’ (BWM) and TODIM (TOmada de Decisão Interativa e Multicritério – in Portuguese ‘Interactive and Multicriteria Decision Making’) is introduced. A grey-BWM approach is used to determine social sustainability attribute weights, and a grey-TODIM method is utilised to rank suppliers. This process is completed in a group decision setting. A case study of an Iranian manufacturing company is used to exemplify the applicability and suitability of the proposed social sustainability decision framework. Managerial implications, limitations, and future research directions are introduced after the application of the model
A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes
Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support
Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories
The present work articulates few case empirical studies on decision making in industrial
context. Development of variety of Decision Support System (DSS) under uncertainty and
vague information is attempted herein. The study emphases on five important decision making
domains where effective decision making may surely enhance overall performance of the
organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier
selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply
chain’s g-resilient index and v) risk assessment in e-commerce exercises.
Firstly, decision support systems in relation to robot selection are conceptualized through
adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey
set theory is also found useful in this regard; and is combined with TODIM approach to
identify the best robot alternative. In this work, an attempt is also made to tackle subjective
(qualitative) and objective (quantitative) evaluation information simultaneously, towards
effective decision making.
Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a
novel decision support framework is proposed to address g-resilient (green and resilient)
supplier selection issues. Green capability of suppliers’ ensures the pollution free operation;
while, resiliency deals with unexpected system disruptions. A comparative analysis of the
results is also carried out by applying well-known decision making approaches like Fuzzy-
TOPSIS and Fuzzy-VIKOR.
In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance-
Based’ model in combination with grey set theory to deal with 3PL provider selection,
considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is
articulated to demonstrate application potential of the proposed model. The results, obtained
thereof, have been compared to that of grey-TOPSIS approach.
Another part of this dissertation is to provide an integrated framework in order to assess gresilient
(ecosilient) performance of the supply chain of a case automotive company. The
overall g-resilient supply chain performance is determined by computing a unique ecosilient
(g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with
Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient
criteria in accordance to their current status of performance.
The study is further extended to analyze, and thereby, to mitigate various risk factors (risk
sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are
recognized and evaluated in a decision making perspective by utilizing the knowledge
acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying
parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying
parameters are assessed in a subjective manner (linguistic human judgment), rather than
exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to
various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk
factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem
context (toward e-commerce success). Risks are now categorized into different levels of
severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic).
Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect
the company’s e-commerce performance are recognized through such categorization. The
overall risk extent is determined by aggregating individual risks (under ‘critical’ level of
severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then
used to obtain structural relationship amongst aforementioned five risk sources. An
appropriate action requirement plan is also suggested, to control and minimize risks associated
with e-commerce exercises
Supplier evaluation and selection in fuzzy environments: a review of MADM approaches
In past years, the multi-attribute decision-making (MADM)
approaches have been extensively applied by researchers to the
supplier evaluation and selection problem. Many of these studies
were performed in an uncertain environment described by fuzzy sets.
This study provides a review of applications of MADM approaches
for evaluation and selection of suppliers in a fuzzy environment. To
this aim, a total of 339 publications were examined, including papers
in peer-reviewed journals and reputable conferences and also some
book chapters over the period of 2001 to 2016. These publications
were extracted from many online databases and classified in some
categories and subcategories according to the MADM approaches,
and then they were analysed based on the frequency of approaches,
number of citations, year of publication, country of origin and
publishing journals. The results of this study show that the AHP and
TOPSIS methods are the most popular approaches. Moreover, China
and Taiwan are the top countries in terms of number of publications
and number of citations, respectively. The top three journals with
highest number of publications were: Expert Systems with Applications,
International Journal of Production Research and The International
Journal of Advanced Manufacturing Technology
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