Multi-criteria Decision-making Algorithm Research Articles

Composite decision-making algorithms for optimisation of hybrid renewable energy systems: Port of Takoradi as a case study

Ghana’s prevailing power issues have incited the government’s interest in integrating renewable energy sources into the country’s present energy mix for sustainable energy supply and to minimise carbon emissions. Hybridising renewable energy resources has recently gained attention due to the intermittent nature of some resources. The objective of this study is to rank the performance of ten hybrid renewable energy systems (HRES) for sustainable port operations in Ghana using multicriteria decision-making algorithms. All chosen criteria for decision-making analysis emanate from technical, economic, environmental, socio-cultural, and policy pillars extensively employed in evaluating new technologies towards sustainability. Fifteen unique decision-making algorithms were tested on the decision matrix to rank the HRES. After that, the Spearman rank correlation coefficient was applied to validate the ranks obtained from the algorithms to make the final selection. The final rankings indicated that PV/wind/battery/natural gas is the optimal HRES for Takoradi port compared to the other HRES. This indicates that extensive investment in PV/wind/battery/natural gas systems has a reasonable potential to achieve sustainable energy for the Ghanaian industrial sector. The findings are vital for decision-makers, policymakers, and investors in developing Ghana’s renewable energy sector.

Weight Influence of Logarithmic and Exponential Functions on the Selection of Wireless Networks Using Multi-Criteria Decision-Making Methods

This research aims to study the influence of logarithmic and exponential functions on the multi-criteria decision-making algorithm that changes the linear to the nonlinear method. It is carried out to better understand the multi-criteria decision-making (TOPSIS) algorithm whereby these functions may influence the criteria weights during the selection of the best network. The experiment is applied under different network types to evaluate the most optimum network that leads to better throughput, low latency, minimum BER, and low price per MB. The algorithms are assessed in MATLAB simulation environments. In addition, the adoption of the Wi-Fi networks standard is determined by factors such as bandwidth, signal to noise ratio and the channel modulation technique during the decision-making process. The simulation results showed that the exponential function had produced approximately similar results to that of linear TOPSIS algorithm because both keep the weights to demonstrate positive values. However, logarithmic TOPSIS produced different results and a worst-case scenario, as the weights have negative values which lead to a phase shift of 180⁰ during the decision process. Thus, linear TOPSIS was found to have the best results while logarithmic TOPSIS had the worst outcome.

Editorial Notes

Editorial Notes Craig S. Revels Welcome to the eighty-fourth volume of the Yearbook of the Association of Pacific Coast Geographers. As always, it remains a privilege to bring you the research and perspectives of our members as evidence of how vibrant and wide-ranging our Association continues to be. This year's issue also includes the welcome return of the abstracts, awards, and various reports from our 2021 annual meeting in San Diego, which itself was a celebrated achievement after the pandemic-related cancellation of the previous year. The resumption of our annual meetings provides the opportunity to lead off the Yearbook once again with the APCG Presidential Address. In his wide-ranging essay, Michael Pretes shares with us his enthusiasm for the dedications, notes, and letters found in the books of several storied geographers. In particular, he shares with us the ways these often-overlooked snippets can reveal the personalities of the authors as well as provide new insights into the history of geography itself. Tate Pashibin, Geoff Buckley, and Yolonda Youngs consider the contentious process surrounding the creation of Canyonlands National Park in the 1960s. This process, they argue, was a key moment in the debate over multiple-use agreements in protected landscapes. The final form of the park, one that rejected multiple-use concepts, reflects the compromises and inherent contradictions that continue to shape resource management issues in the western United States. At the San Diego meeting, Dan Arreola brought together a group of former students and like-minded geographers to offer their reflections on Donald Meinig's Southwest as well as their own experiences with the volume and their relationship with Meinig himself. We find their thoughts gathered here in this volume as an appreciation of both the work and the scholar, reminding us of the pervasive influence of well-written regional geography and the sometimes unexpected but always rewarding ways our personal relationships with students and colleagues help shape our own geographical perspectives. Mark Bjelland and Madelyn Vander Veen take us from arid climes to the much different world of the Pacific Northwest. Here, they survey the evolution of Portland's post-industrial neighborhoods, examining key social and economic variables to tell the story of these renewed urban landscapes and provide perspective on the evolving nature of cities themselves. [End Page 9] Finally, Soheil Boroushaki illustrates the complexities of spatial analysis and GIS, exploring the theoretical underpinnings of a multicriteria decision-making algorithm and demonstrating its application in identification of disadvantaged communities in the San Fernando Valley. His article is a reminder that there are increasingly refined and innovative ways to improve the tools and processes we use to approach geographical problems. Though the articles in this volume of the Yearbook are few, I believe they illustrate the breadth and far-reaching interests of both our Association and discipline. As we move away from the constraints on research and travel created by the pandemic, I look forward to sharing an increasing number of our collective interests, ideas, and explorations. The resumption of our annual meetings is a welcome return to the collegiality and support our Association provides its members, and I encourage those presenting to consider the Yearbook as a venue for your research. To the APCG membership at large, I offer the same exhortation, and look forward to including your essays, perspectives, and reviews in future volumes. Every year, the Yearbook represents a collective achievement, and I offer sincere thanks to all who contributed their work to this volume, those who generously offered their time to review manuscripts, and everyone else who helped this volume see the light of day. Thanks also to Dave Deis for his work on yet another cover of the Yearbook. And as always, my sincere thanks and appreciation to Rick Cooper for pulling everything together to craft the final volume, and also for helping me keep the bus from going completely over the proverbial cliff. [End Page 10] Craig S. Revels Central Washington University Copyright © 2022 Association of Pacific Coast Geographers

A new approach to three‐way decisions making based on fractional fuzzy decision‐theoretical rough set

The main aim of the proposed work is to develop the new technique based on decision-theoretical rough sets (DTRSs) and their applications in three-way decision-making problems. This study first develop a fractional fuzzy set (FFS) and their operations, the FFS is a more generalized and accurate tool for describing uncertainty in real-life data information. A new form of decision technique for dealing with the issue of choice based on DTRSs is included in the three-way decisions. The loss function of DTRSs is being used in the proposed decision method model. Initially, the idea of fractional fuzzy α-covering (FF α-covering), fractional fuzzy α–neighborhood (FF α–neighborhood) was introduced. Under the fractional fuzzy state, we integrated the loss function of DTRSs with covering-based fractional fuzzy rough sets. Furthermore, we proposed and established performance characteristics for a new fractional fuzzy α-covering decision-theoretical rough sets model (FFCDTRSs). Then, according to the level of fractional fuzzy numbers (FFN's) positive and negative membership and related three-way decision-making, four methods to solve the expected loss expressed in the form of (FFNs) are described. We have developed a multicriteria decision algorithm (MCDM) based on FFCDTRS. Then an example is used to prove the feasibility of the four methods to solve the MCDM problem. Finally, the results of four distinct decision procedures with various loss functions are compared. The proposed three-way decision-making models are more accurate as compared with particular fuzzy sets.

Improved Routing Algorithm in IoT Network by TOPSIS method.

Low energy utilization and reliability in packet transmission are factors that seriously affect the performance of Wireless Sensor Network (WSN) and Quality of Service (QoS) in general. Regarding routing strategy, the efficient forwarding methods can cause more throughput, less packet loss with less energy usage. To this purpose multi-attribute, decision-making (MADM) methods are proposed in hop-by-hop sending data scenarios, as the most efficient methods in selecting the reliable path towards the sink node. In such a decision-making environment, various routing metrics are taken into account. This paper compares Wise Route routing protocol and modified-Wise Route (MwiseRoute) by TOPSIS with indexes (signal to noise ratio, residual energy, distance from the node, farness from the sink node) balances by self-sets weights (0.3, 0.5, 0.1,0.1) respectively. The evaluation has been tested on Mixim 3.2 in Omnet++.4.6. The result shows MwiseRoute with TOPSIS is more appropriate in gaining more throughput and significantly decreases packet drop in mac by less power drain. References Aziz, L. and Aznaoui, H. (2020) 'Efficient Routing Approach Using a Collaborative Strategy', Journal of Sensors, 2020, pp. 2547061. doi: 10.1155/2020/2547061 Foubert, B. and Mitton, N. (2021) 'RODENT: a flexible TOPSIS based routing protocol for multi-technology devices in wireless sensor networks', ITU Journal on Future and Evolving Technologies, 2(1). doi. Available at: https://hal.inria.fr/hal-03165426 Ghafoor, K. Z., Kong, L., Sadiq, A. S., Doukha, Z. and Shareef, F. M. 'Trust-aware routing protocol for mobile crowdsensing environments'. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 15-19 April 2018, 82-87. Hajji, F. E., Leghris, C. and Douzi, K. (2018) 'Adaptive Routing Protocol for Lifetime Maximization in Multi-Constraint Wireless Sensor Networks', Journal of Communications and Information Networks, 3(1), pp. 67-83. doi: 10.1007/s41650-018-0008-3 Kamble, A. and Patil, B. ( 2020) 'Selection of Cluster Head Techniques in WSN: A Review', 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), 3, pp. 7. doi: http://dx.doi.org/10.2139/ssrn.3734304 Karami, A. 'UTILIZATION AND COMPARISON OF MULTI ATTRIBUTE DECISION MAKING TECHNIQUES TO RANK BAYESIAN NETWORK OPTIONS Master Degree Project in Informatics One year Level ECTS 30 Spring term Year 2011'. Kaur, L. and Malhotra, J. (2015) 'Comparison of Wise Route and Flooding Network Type of Convergecast Routing in Wireless Sensor Network', International Journal of Sensor and Its Applications for Control Systems, 3, pp. 1-10. doi: 10.14257/ijsacs.2015.3.2.01 Kazmi, S., Liscano, R. and Ren, J. (2014) 'Determining Fuzzy Link Quality Membership Functions in Wireless Sensor Networks', Procedia Computer Science, 34, pp. 149-156. doi: https://doi.org/10.1016/j.procs.2014.07.073 Khan, B. M., Bilal, R. and Young, R. (2018) 'Fuzzy-TOPSIS based Cluster Head selection in mobile wireless sensor networks', Journal of Electrical Systems and Information Technology, 5(3), pp. 928-943. doi: https://doi.org/10.1016/j.jesit.2016.12.004 Kim, B.-S., Kim, K.-I., Chang, G., Kim, K. H., Roh, B. and Ham, J.-H. (2019a) 'Comprehensive Survey on Multi Attribute Decision Making Methods for Wireless Ad Hoc Networks', 2019, 20(5), pp. 14. doi. Available at: https://jit.ndhu.edu.tw/article/view/2139 Kim, B., Kim, K.-I., Chang, G., Kim, K. H., Roh, B. and Ham, J. (2019b) 'Comprehensive Survey on Multi Attribute Decision Making Methods for Wireless Ad Hoc Networks', Journal of Internet Technology, 20, pp. 1575-1588. doi: Kodali, R. K. and Malothu, V. K. 'MIXIM framework simulation of WSN with QoS'. 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 25-27 May 2016, 128-131. Odu, G. O. (2019) 'Weighting methods for multi-criteria decision making technique', 23 No. 8 (2019). doi: 10.4314/jasem.v23i8.7 Shelebaf, A. and Tabatabaei, S. (2020) 'A Novel Method for Clustering in WSNs via TOPSIS Multi-criteria Decision-Making Algorithm', Wireless Personal Communications, 112(2), pp. 985-1001. doi: 10.1007/s11277-020-07087-7 Smolka, T. a. s. (2011) 'MiXiM Tutorial', pp. 8. doi: Temur, G. T. (2016) 'A novel multi attribute decision making approach for location decision under high uncertainty', Applied Soft Computing, 40, pp. 674-682. doi: https://doi.org/10.1016/j.asoc.2015.12.027 Varga, A. and Hornig, R. (2008) An overview of the OMNeT++ simulation environment.

Selection of elemental composition of a Nickel-based super alloy through neural networks and multi-criteria decision making

ABSTRACT The present work aims to develop nickel-based superalloys by studying the impact of their alloying elements in providing the desired physical and mechanical characteristics. At present, materials are being developed using experiment-driven trial and improvement. The long time duration in their development rules out the planning of new materials and thus leading engineers to select the commercially available materials with a compromise towards the properties. The computationally developed materials have the potential to meet the needs of the materials engineers, and now the materials are being brought into the centre of the planning method. The selection of suitable elemental composition seems to be a multi-criterion decision-making problem with conflicting and diverse objectives as different elements execute different properties at varied conditions. Nickel is an adaptable element that can alloy with most metals. The present study deals with the procedure that can be applied to identify suitable elemental composition for a Ni based super alloy, allowing them to adjust for required properties in order to enhance its use in a particular application. The methodology deals with the selection of ‘n’ number of alternatives to give desired properties and applying multi-criteria decision-making algorithms to rank and select one among the alternatives developed. The Spearman rank correlation coefficient in the range of 0.74-1.00 shown a strong correlation between the methods adopted. From the analysis Cr (wt. 12%), Co (wt. 16.5%), Mo (wt. 2.5%), W (wt. 1.5%), Nb (wt. 1.5%), Al (wt. 2%), Ti (wt. 3%), Fe (wt. 1.5%), C (wt. 0.2%), and B (wt.0.03%) can be developed to get an yield strength of 504.94 MPa, Ultimate tensile strength of 678.62 MPa, % elongation 18.53 and Creep resistance at 1000 hours work life of 263 MPa to get better ductile properties. Also Cr (wt. 18%), Co (wt. 17%), Mo (wt. 4%), W (wt. 2%), Al (wt. 2%), Ti (wt. 1.5%), Fe (wt. 2%), C (wt. 0.1%) and B (wt.0.02%) can be developed to get an yield strength of 451.25 MPa, Ultimate tensile strength of 669.57 MPa, % elongation 25.18 and Creep resistance at 1000 hours work life of 309 MPa to get better strength properties.

Demand-Side Flexibility Impact on Prosumer Energy System Planning

Apart from numerous technical challenges, the transition towards a carbon-neutral energy supply is greatly hindered by limited economic feasibility of renewable energy sources. This results in their slow and bounded penetration in both commercial and residential sectors that are responsible for over 40% of final energy consumption. This paper aims to demonstrate that combined application of sophisticated planning methodologies at building-level and presents incentive mechanisms for renewables that can result in prosumers, featuring hybrid renewable energy systems (HRES), with economic performance comparable to that of conventional energy systems. The presented research enhances existing planning methodologies by integrating appliance-level demand side management into the decision process and investigates its effect on the planning problem. Moreover, the proposed methodology features an innovative and holistic approach that simultaneously assess electrical and thermal domain in both an isolated and grid-connected context. The analyzed hybrid system consists of solar photovoltaic, wind turbine and battery with thermal supply featuring solar thermal collector and a ground-source heat pump. Overall optimization problem is modeled as a mixed-integer linear program, while ranking of all feasible alternatives is made by the multicriteria decision-making algorithm against several technological, economic, and environmental criteria. A real-life scenario of energy system retrofit for a building in the United Kingdom was employed to demonstrate overall cost savings of 12% in the present market and regulation context.

Multi-criteria feasibility of real use of self-compacting concrete with sustainable aggregate, binder and powder

Replacing natural raw materials with industrial by-products can increase the sustainability of Self-Compacting Concrete (SCC), although its fresh and hardened behavior will usually worsen. The benefits of increased sustainability must therefore outweigh any reduction in concrete flowability and strength. These aspects can be analyzed through Multi-Criteria Decision-Making (MCDM) algorithms. In all, 19 SCC mixes were studied. One reproduced commercial SCC (limestone filler and conventional cement), the others were produced with more sustainable materials: 100% coarse Recycled Concrete Aggregate (RCA); 0%, 50% or 100% fine RCA; 45% Ground Granulated Blast-furnace Slag (GGBS); and sustainable aggregate powders such as limestone fines 0/0.5 mm and RCA powder 0/0.5 mm. Decreased flowability at 15 and at 60 min, compressive strength, modulus of elasticity, carbon footprint, and cost of mix were all studied. Both the carbon footprint and the cost were calculated considering only the composition of the SCC, without including aspects that depend on each particular case study, such as transport distances. These aspects constituted the decision-making criteria of the MCDM analysis, under which 14 scenarios were evaluated with different requirements for SCC, using 3 different algorithms (TOPSIS, AHP, and PROMETHEE). The results suggested that the ideal choice for fast concreting is a combination of GGBS, 100% coarse RCA and limestone fines, although if SCC has to be transported to the concreting point, then conventional cement should be used. Strength and stiffness can be maximized by limiting the fine RCA content to 50%. Finally, considering a versatile choice, only SCC with coarse RCA, limestone fines, GGBS and 0% fine RCA could compete with conventional SCC. Adapting the design to minimize the detrimental effects of by-products is therefore essential to promote sustainable SCC that is also commercially competitive.

Optimal location and dimensioning of capacitors in microgrids using a multicriteria decision algorithm

This work presents a methodology for optimal compensation of reactive power in Electric Microgrids using a multicriteria decision algorithm based on heuristic methods. It was verified the optimal location and dimensioning of fixed capacitor banks in a microgrid with 14 buses with objective functions based on cost, efficiency and quality criteria in a maximum demand operating condition and also considering the constraints of objective variables in minimum demand scenarios. The proposed capacitance to be installed was considered as a discrete variable. The location of discretized reactive capacitances was simulated at candidate nodes analyzing the power flow in the case study, resulting in a wide solution space that was tackled by means of multicriteria optimization, using dominance elimination techniques and the weighted sum method for decision making. The variables analyzed were: cost, maximum and average deviations of the voltage profile, power factor, total losses in the lines of the system and THD. All these variables were also verified in minimum demand scenarios. The proposed solution provides significant improvements in the variables analyzed and verifies the optimal performance of the technique. The mathematical analysis demonstrates the need of addressing the reactive compensation problem by means of multicriteria decision and the proposal provides a very novel tool for calculating the location and dimensioning of reactive compensation devices in distribution systems and microgrids. The programming was done in the Matlab environment with simulations using Simulink. The case study analyzed is a very novel validated Microgrid system of which it is known the variables that take part of this analysis, as an approximation of the study of a very real Microgrid.

A fuzzy aspect‐based approach for recommending hospitals

The process to assess a hospital performance usually needs the interaction of a lot of experts and patients and is very costly and time-consuming. Nevertheless, the availability of patient opinions on the Internet offers a great opportunity to develop systems that evaluate hospitals based on user feedback. The content of these opinions is very challenging, including information about the hospital services but also stories about their own patients, their families, and personal feelings or beliefs before or after leaving a hospital. Therefore, the task of recommending hospitals according to the quality of their services becomes really complicated. This study describes an application for ranking hospitals based on the user preferences about the different offered services as well as the opinions about them. First, it semiautomatically classifies all predefined hospital aspects, calculates the sentiment orientation, and represents their associated polarity by intuitionistic fuzzy sets. Second, by means of the user preferences towards the different aspects, an aggregation operator, and a multicriteria decision-making algorithm, all hospitals are ranked. To assess this methodology, a large set of reviews about hospitals have been collected. Further, considering all patient ratings about the different hospitals, an algorithm for ranking them is proposed, which develops baselines for comparison. In addition, an interval-valued Pythagorean fuzzy approach has been also implemented to compare the obtained results. These results confirm the soundness of the proposal.

Multi-criteria decision-making algorithm based on aggregation operators under the complex interval-valued q-rung orthopair uncertain linguistic information

The paper aims to present a concept of a Complex interval-valued q-rung orthopair uncertain linguistic set (CIVQROULS) and investigated their properties. In the presented set, the membership grades are considered in terms of the interval numbers under the complex domain while the linguistic features are added to address the uncertainties in the data. To further discuss more, we have presented the operation laws and score function for CIVQROULS. In addition to them, we present some averaging and geometric operators to aggregate the different pairs of the CIVQROULS. Some fundamental properties of the proposed operators are stated. Afterward, an algorithm for solving the decision-making problems is addressed based on the proposed operator using the CIVQROULS features. The applicability of the algorithm is demonstrated through a case study related to brain tumors and their effectiveness is compared with the existing studies.

Optimal Location and Dimensioning of Capacitors in Microgrids Using a Multicriteria Decision Algorithm

This work presents a methodology for optimal compensation of reactive power in Electric Microgrids using a multicriteria decision algorithm based on heuristic methods. It was verified the optimal location and dimensioning of fixed capacitor banks in a microgrid with 14 buses with objective functions based on cost, efficiency and quality criteria in a maximum demand operating condition and also considering the constraints of objective variables in minimum demand scenarios. The proposed capacitance to be installed was considered as a discrete variable. The location of discretized reactive capacitances was simulated at candidate nodes analyzing the power flow in the case study, resulting in a wide solution space that was tackled by means of multicriteria optimization, using dominance elimination techniques and the weighted sum method for decision making. The variables analyzed were: cost, maximum and average deviations of the voltage profile, power factor, total losses in the lines of the system and THD. All these variables were also verified in minimum demand scenarios. The proposed solution provides significant improvements in the variables analyzed and verifies the optimal performance of the technique. The mathematical analysis demonstrates the need of addressing the reactive compensation problem by means of multicriteria decision and the proposal provides a very novel tool for calculating the location and dimensioning of reactive compensation devices in distribution systems and microgrids. The programming was done in the Matlab environment with simulations using Simulink. The case study analyzed is a very novel validated Microgrid system of which it is known the variables that take part of this analysis, as an approximation of the study of a very real Microgrid.

A Novel Optimization Algorithm for Solar Panels Selection towards a Self-Powered EV Parking Lot and Its Impact on the Distribution System

This paper proposes an original multi-criteria decision-making optimization algorithm to select the best solar panels in an existing market and optimally size the photovoltaic (PV) system for an electric vehicle parking lot (EVPL). Our proposed algorithm is called rank-weigh-rank (RWR), and it is compared to the well-known technique for order of preference by similarity to ideal solution (TOPSIS) optimization algorithm under the same conditions for validation purposes. Results show that the speed of our proposed algorithm (RWR) in finding the best solution increases exponentially compared to TOPSIS when the numbers of alternatives and criteria increase. Moreover, 77% is the probability of obtaining results with more than 80% accuracy compared to TOPSIS, which validates the efficiency of our algorithm. In addition, we were able to design an EVPL with a power self-sufficiency ratio of 60.8%, the energy self-sufficiency ratio of 74.7%, and a payback period of 10.58 years. Moreover, the renewable energy-based EVPL was able to reduce the power losses on the network by 95.7% compared to an EVPL without a renewable energy system and improve the voltage deviation.

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity.

The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.

A Weight Influence of Logarithmic and Exponential Functions on the Selection of Wireless Networks using Multi-Criteria Decision-Making Methods

This research aims to study the influence of logarithmic and exponential functions on the multi-criteria decision-making algorithm that changes linear method to nonlinear method. It is carried out to better understand the multi-criteria decision-making, namely the technique for preference by similarity to ideal solution (TOPSIS) algorithm whereby in which these functions may influence the criteria weights during the selection of the best network. The investigation is applied under different network types to evaluate the most optimum network that leads to better throughput, low latency, minimum BER, and low price per MB. The algorithms are assessed in MATLAB simulation environments. The study also considered the adoption of the Wi-Fi networks standard which is factors such as bandwidth, signal to noise ratio and the channel modulation technique were determined during the decision-making process. The simulation results show that the exponential function had produced approximately similar results to that of linear TOPSIS algorithm because both methods keep the weights to demonstrate positive values. However, logarithmic TOPSIS produced different results as the weights have negative values which lead to a phase shift of 180⁰ during the decision process. Thus, linear TOPSIS was found to have the best results while logarithmic TOPSIS had the worst outcome.

VMFS: A VIKOR-based multi-target feature selection

This paper proposed a Multi-Criteria Decision-Making (MCDM) modeling to deal with multi-target regression problem. This model offered a feature ranking approach for multi-target regression by one of the famous MCDM algorithms called VIKOR. In fact, in this approach, the multi-target feature selection has become an MCDM model in which features are evaluated based on relations with targets. Our proposed method, VIKOR-based Multi-target Feature Selection (VMFS), first uses cosine similarity to construct the decision matrix for the MCDM process. Then VIKOR method is applied to the decision matrix to rank the features in the multi-target space. To illustrate the optimality and efficiency of the proposed method, we have compared our approach with some multi-output feature selection methods. The results show that our method in terms of average RRMSE is superior to other similar methods and performs in a short time, and it is more efficient than the other methods.

Reducing carbon dioxide emissions from electricity sector using demand side management

ABSTRACT This paper discusses the Clean Power Plan (CPP) legislation and its repeal on the emissions from electricity generation sources. Demand side management (DSM) is probed to illustrate an alternative technique to achieve lower emissions goals. This work discusses the Climate Action Plan (CAP) corresponding to Fort Collins, Colorado, U.S. and its timeline targets. Therefore, the paper provides a technique for prioritizing DSM options through a Multi-Criteria Decision-Making (MCDM). In that regard, a framework is created to examine several options of DSM in reducing carbon footprints. The algorithm states that affecting Greenhouse Gases (GHG) in electric power system is the main goal and this can be achieved by implementing DSM technologies in distribution systems. MCDM algorithms have been proposed to prioritize alternatives and select the ones that achieve suitable emissions reduction. The findings from Analytic Hierarchy Process (AHP) show that the “communication and intelligence” option has a global priority of 0.341 making it the potential optimal alternative in achieving the goal. The second alternative is “electric stationary storage” with global priority of 0.207. The results obtained from Analytic Network Process (ANP) suggest that “communication and intelligence” is the optimum technology to reach the target with a priority of 0.284.

Sustainability metrics for rapid manufacturing of the sand casting moulds: A multi-criteria decision-making algorithm-based approach

Additive Manufacturing has significantly developed over the years and is widely used in most industrial applications. Rapid Tooling refers to manufacturing the tools (moulds and dies) using Additive Manufacturing techniques. An essential application of Rapid Tooling is the 3D printing of sand moulds for castings. Metal casting is an energy-intensive process; and a lot of research has gone into the sustainability assessment of traditional sand castings. In this work, a robust decision-making approach is developed and implemented for sand mould manufacturing. Sustainability metrics for the mould production are formulated, and the conventional sand moulds are compared against the 3D printed sand moulds. A Multi-Criteria decision-making algorithm is implemented, and the effect of the batch size in the mould manufacturing is also studied. The discussed approach can help decision-makers choose the best mould manufacturing technique for the intended number of moulds to be manufactured.

Tribological performance evaluation on brake friction material by using multi-objective optimization methods

PurposeThe purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM).Design/methodology/approachIn this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. Multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM.FindingsThe present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively.Originality/valueAn efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Abstract Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.