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S. Nieto Sanchez

Bio: S. Nieto Sanchez is an academic researcher from Louisiana State University. The author has contributed to research in topics: Multiple-criteria decision analysis & Decision engineering. The author has an hindex of 2, co-authored 2 publications receiving 417 citations.

Papers
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01 Jan 1998
TL;DR: This paper provides a comprehensive survey of some methods for eliciting data for MCDM problems and also for processing such data.
Abstract: The core of operations research is the development of approaches for optimal decision making. A prominent class of such problems is multi-criteria decision making (MCDM). The typical MCDM problem deals with the evaluation of a set of alternatives in terms of a set of decision criteria. This paper provides a comprehensive survey of some methods for eliciting data for MCDM problems and also for processing such data.

436 citations

Reference EntryDOI
27 Dec 1999
TL;DR: In this paper, a general overview of Multi-attribute Decision Making (MCDM) methods is presented, and a detailed classification of MCDM methods is provided. But the authors do not discuss the specific application areas of these methods.
Abstract: The sections in this article are 1 Multiattribute Decision Making: A General Overview 2 Classification of MCDM Methods 3 Some MCDM Application Areas 4 Multicriteria Decision Making Methods 5 Sensitivity Analysis in MCDM Methods 6 Data Estimation for MCDM Problems 7 Concluding Remarks Keywords: decision making; optimization; pairwise comparisons; sensitivity analysis; operations research

6 citations


Cited by
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Journal ArticleDOI
Paul Kline1
01 Aug 1986-Nature
TL;DR: In this article, a book is one of the greatest friends to accompany while in your lonely time and when you have no friends and activities, reading book can be a great choice.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading models of man as one of the reading material to finish quickly.

1,117 citations

Journal ArticleDOI
TL;DR: This paper presents the Pareto and scalarization method, which creates multi-objective functions made into a single solution using weights, and the solution is a performance indicators component that forms a scalar function which is incorporated in the fitness function.
Abstract: Several reviews have been made regarding the methods and application of multi-objective optimization (MOO). There are two methods of MOO that do not require complicated mathematical equations, so t...

375 citations

Journal ArticleDOI
21 Jul 2016-Energies
TL;DR: In this paper, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location.
Abstract: This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative.

208 citations

Proceedings ArticleDOI
30 Jun 2011
TL;DR: This paper discusses and formalizes the issue of cloud service selection in general and proposes a multi-criteria cloudService selection methodology.
Abstract: Cloud computing despite being in an early stage of adoption is becoming a popular choice for businesses to replace in-house IT infrastructure due to its technological advantages such as elastic computing and cost benefits resulting from pay-as-you-go pricing and economy of scale. These factors have led to a rapid increase in both the number of cloud vendors and services on offer. Given that cloud services could be characterized using multiple criteria (cost, pricing policy, performance etc.) it is important to have a methodology for selecting cloud services based on multiple criteria. Additionally, the end user requirements might map to different criteria of the cloud services. This diversity in services and the number of available options have complicated the process of service and vendor selection for prospective cloud users and there is a need for a comprehensive methodology for cloud service selection. The existing research literature in cloud service selection is mostly concerned with comparison between similar services based on cost or performance benchmarks. In this paper we discuss and formalize the issue of cloud service selection in general and propose a multi-criteria cloud service selection methodology.

177 citations

Journal ArticleDOI
TL;DR: A comprehensive insights into the elements of big data characteristics according to the six ‘Vs’: volume, velocity, variety, veracity, value and variability is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems.
Abstract: The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered `big data'. To our knowledge, no study has highlighted the link between `big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six `Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.

148 citations