A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.

open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the experts’ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of ‘Technology’, ‘Quality’, and ‘Operation’ have respectively the highest importance. Furthermore, the strategies for “new business models development’, ‘Improving information systems’ and ‘Human resource management’ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information

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

The intuitionistic fuzzy multi-criteria decision making based on inclusion degree

This paper introduces a new intuitionistic fuzzy multicriteria decision making method of evaluation based on degree of inclusion of two intuitionistic fuzzy sets. We have called the new technique TOPIIS (Technique to Order Preference by Inclusion of Ideal Solution). The technique is applied to develop an effective employee performance appraisal

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

RECRUITMENT AND SELECTION SYSTEM OF VILLAGES IN WONOSOBO REGENCY

Basic track problems in most of the villages in Wonosobo Regency Government are the recruitment and selection practices of other villages have not been able to encourage the inception of the village with the required standards of competence. This research aims to analyze the system of recruitment and selection of other villages, supporters and restricting factors, as well as establishing a proper and contextual model in Wonosobo Regency over the approach to the management of human resources. With descriptive method, this study found that the standard of competence has not been a consideration for the Government since the beginning of the planning process, to recruitment and selection. Almost the entire selection process, starting from the determination of the criteria of candidates, selection of administration until the written exams tend not based on competence. In addition, the necessary of the village according to the preference of the villagers also has yet to be fulfilled, thus still encountered complaints from the public. The study also identifies some of the factors supporting the recruitment and selection competency-based, among others, regulation and community support. Later, inhibitor factor, among others, the quality of human resources and organizational needs analysis Committee. Based on these conditions, the model recommendations in this study encourages the process of recruitment and selection apply competency — based in practice, in order to be able to support organizational performance towards the village government is better. Start the process of sourcing, attracting, through screening, based on the needs the competence and analyzed scientifically. Community preference is also a consideration in that process in order to involve the public opinion and build public confidence to the results of the selection. These two factors also continue to support are encouraged to be optimal. Meanwhile, an inhibitor of factor continues to be minimized through a variety of innovations

Three Decades of Fuzzy AHP: A Bibliometric Analysis

[EN] For decades, Fuzzy Sets Theory (FST) has been consistently developed, and its use has spread across multiple disciplines. In this process of knowledge transfer, fuzzy applications have experienced great diffusion. Among them, Fuzzy Analytic Hierarchy Process (fuzzy AHP) is one of the most widely used methodologies today. This study performs a systematic review following the PRISMA statement and addresses a bibliometric analysis of all articles published on fuzzy AHP in journals indexed in Web of Science, specifically in Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). The analyzed database includes 2086 articles published between 1994 and 2022. The results show the thematic clusters, the evolution of the academic conversation and the main collaboration networks. The main contribution of this article is to clarify the research agenda on fuzzy AHP. The results of the study allow academics to detect publication opportunities. In addition, the evidence found allows researchers and academics setting the field¿s agenda to advise the editors of high-impact journals on gaps and new research trends.Castello-Sirvent, F.; Meneses-Eraso, C.; Alonso-Gómez, J.; Peris-Ortiz, M. (2022). Three Decades of Fuzzy AHP: A Bibliometric Analysis. Axioms. 11(10):1-34. https://doi.org/10.3390/axioms11100525134111