Multi-criteria Decision-making Algorithm Research Articles

An Innovative Algorithm Based on Octahedron Sets via Multi-Criteria Decision Making

Octahedron sets, which extend beyond the previously defined fuzzy set and soft set concepts to address uncertainty, represent a hybrid set theory that incorporates three distinct systems: interval-valued fuzzy sets, intuitionistic fuzzy sets, and traditional fuzzy set components. This comprehensive set theory is designed to express all information provided by decision makers as interval-valued intuitionistic fuzzy decision matrices, addressing a broader range of demands than conventional fuzzy decision-making methods. Multi-criteria decision-making (MCDM) methods are essential tools for analyzing and evaluating alternatives across multiple dimensions, enabling informed decision making aligned with strategic objectives. In this study, we applied MCDM methods to octahedron sets for the first time, optimizing decision results by considering various constraints and preferences. By employing an MCDM algorithm, this study demonstrated how the integration of MCDM into octahedron sets can significantly enhance decision-making processes. The algorithm allowed for the systematic evaluation of alternatives, showcasing the practical utility and effectiveness of octahedron sets in real-world scenarios. This approach was validated through influential examples, underscoring the value of algorithms in leveraging the full potential of octahedron sets. Furthermore, the application of MCDM to octahedron sets revealed that this hybrid structure could handle a wider range of decision-making problems more effectively than traditional fuzzy set approaches. This study not only highlights the theoretical advancements brought by octahedron sets but also provides practical evidence of their application, proving their importance and usefulness in complex decision-making environments. Overall, the integration of octahedron sets and MCDM methods marks a significant step forward in decision science, offering a robust framework for addressing uncertainty and optimizing decision outcomes. This research paves the way for future studies to explore the full capabilities of octahedron sets, potentially transforming decision-making practices across various fields.

Why should imminent international funds for solar photovoltaics go to families and the private sector and not to the government to stop the electricity crisis in Lebanon?

In Lebanon, the failure to supply oil-based electricity was mostly due to flawed management, increasing costs of bills of private generators, the rise of fuel prices, and the absence of subsidies. In response, individuals have started funding small-scale rooftop PV (RPV) to increase the solar share beyond the national target while the government has struggled for years to pass bidding phases for 12 utility-scale PV (USPV). In addition to publicly unavailable details, specifications, assessments, and termination plans about the 12 solar farms, their geographical sitting was not technically evaluated. Therefore, this study mapped USPV suitability in Lebanon using a multi-criteria decision-making algorithm. Although the highly suitable lands can supply 40% of the total land needed to cover the peak demand, the areas were not equally distributed throughout the governorates. Even if USPV can help reach national solar targets, investing in public-related projects can be perilous, especially with historical patterns of inappropriate allocation of funding, political instability, and absence of accountability. Thus, investing in RPV can be more attractive due to its efficiency, grid independence, and flexibility and should be oriented primarily towards low-income families and small entrepreneurs and later to wealthier people to ensure social equity and welfare.

Multi-criteria decision-making and artificial bee colony algorithm for optimization of process parameters in pyramid solar still

This study focuses on optimizing process parameters for pyramid solar still using Multi-Criteria Decision-Making Grey Relational Analysis (MCDM-GRA) and bio-inspired metaheuristic Artificial Bee Colony (ABC) algorithm. The objective is to enhance the performance of the pyramid solar still (PSS) performance integrated with a peripheral solar water heater under different operational parameters, including saline water flow rate, temperature, and solar intensity. Experiments are designed using Taguchi's L9 (33) orthogonal array (OA), and the outputs are optimized with MCDM-GRA. The optimum conditions for maximized productivity and system efficiency are a solar intensity of 1000 W/m2, a saline water mass of 3 kg, and a saline water temperature of 40 °C. With these settings, the system achieves an efficiency of 65.33 % and produces 1.96 kg of potable water. The ABC Algorithm, on the other hand, suggests that the optimal conditions for achieving maximum productivity and system efficiency involve a solar intensity of 1000 W/m2, a mass of 3 kg for the saline water, and a water inlet temperature of 30 °C. In this scenario, the system produced 1.92 kg of potable water with an efficiency rate of 64 %. Analysis of variance (ANOVA) shows that solar intensity contributes significantly towards productivity by 81.72 % with an error of 3.82 % and regression coefficient of 96.18 %. Regression models are developed from the results obtained and fed to ABC for further optimization, yielding results similar to MCDM-GRA.

A novel multi-criteria group decision making algorithm for enhancing supply chain efficiency under high uncertainty during crisis based on q-rung orthopair fuzzy information

In recent years, q-rung orthopair fuzzy sets (q-ROFSs) have been becoming gradually more advantageous in handling information with high uncertainty. Several multi-criteria group decision-making (MCGDM) algorithms under q-ROFS information have been proposed in literature and used to solve various real-life problems. However, several shortcomings of some existing MCGDM algorithms under certain circumstances also emerged which limit their applicability. To overcome these challenges and deal with information under high uncertainty more accurately and reasonably, we propose a novel MCGDM algorithm under q-ROF information, i.e., (q-ROF-MCGDM), that retains the advantages of currently available methods, but extend its applicability by introducing a new q-rung orthopair fuzzy weighted averaging aggregation operator (q-ROFWAAO) along with a new entropy measure. The proposed q-ROF-MCGDM algorithm involves the steps: firstly, the input requirement in terms of alternatives, criteria and expert evaluations are required. Secondly, aggregating expert opinions using weights and normalizing decision matrices, and finally, calculating entropy weights and aggregating overall evaluations for final prioritization. Further, new operational laws have been developed and several necessary properties of the proposed q-ROFWAAO are also proved. Moreover, a sensitivity and comparative analysis has been carried out for validity and effectiveness of the proposed q-ROF-MCGDM algorithm. The proposed q-ROF-MCGDM algorithm has been implemented to enhance the efficiency of the United Arab Emirates (UAE) food industry under high uncertainty during the recent crisis. Finally, an evaluation of identifying and prioritizing the most severe food SC disruptions and appropriate mitigation strategies under crisis is provided to demonstrate applicability of the proposed q-ROF-MCGDM algorithm, and the obtained real case results confirm its usefulness. Findings of the study offer valuable insights to both food industry researchers and managers in developing effective recovery strategies, mitigating risks, and improving overall efficiency to ensure the survival of food businesses during the crisis.

An Integrated Building Information Modeling and Life-Cycle Assessment Approach to Facilitate Design Decisions on Sustainable Building Projects in Canada

In the context of the digital and sustainable transformation of building projects, the integrated approach of Building Information Modeling (BIM) and life-cycle assessment (LCA) has been widely studied. Recent advancements in such integrated modeling processes and techniques have not yet provided reliable and robust decision-making capabilities for designers to intuitively choose between material alternatives. This study develops a new design framework that integrates BIM, LCA, and multi-criteria decision-making (MCDM) algorithms to facilitate sustainable design processes in building projects. A case study using a single-family housing project in the British Columbia province of Canada was implemented to test the designs to demonstrate the effectiveness of the proposed integrated framework, including a three-way comparison of design scenarios—conventional hot-roll steel, recycled steel, and timber. The results indicate a significant performance improvement with the adoption of recycled structural steel materials, surpassing conventional steel designs and demonstrating a similar performance to that of timber designs. The study underscores the importance of informed decision-making in material selection, driven by the quantitative analysis of digital designs and multi-criteria evaluation (e.g., social carbon cost). This integrated framework offers a valuable tool for designers, engineers, and builders to achieve sustainability when designing building projects through the systematic and rapid comparison of environmental performance.

Efficiency of self-compacting concrete made with variable sustainable cementitious materials: A TOPSIS algorithm approach

The use of supplementary cementitious materials as a partial replacement for cement is taking the lead in the concrete production industry. Being known as by-products from various manufacturing properties, concrete produced with such materials is considered a sustainable alternative to normal concrete. Three different by-product materials have been investigated in this study for the production of sustainable self-compacting concrete (SCC), namely, ground granulated blast furnace slag (GGBS), fly ash (FA), and silica fume (SF). Compressive strength, production cost, and global warming potential (GWP) were the main testing parameters for the mixes' performances. Multi-criteria decision-making algorithm was implemented using the TOPSIS model to investigate the ultimate performance of those SCC mixes considering all tested parameters simultaneously. Results showed the best SCC performance can be attained when using about 60 % of GGBS as a partial replacement for cement.

Research needs for enhancing pre-occupancy evaluation of buildings

PurposeMany buildings fail to meet user expectations, causing a performance gap. Pre-occupancy evaluation (PrOE) is believed to have the potential to close the gap. It enables designers to obtain end-user feedback in the design phase and improve the design for better performance. However, PrOE implementation faces challenges due to still maturing knowledgebase. This study aims to understand the state-of-the-art knowledge of PrOE, thereby identifying future research needs to advance the domain.Design/methodology/approachA systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework was conducted. A thorough search in five databases and Google Scholar retrieved 90 articles, with 30 selected for systematic review after eliminating duplicates and irrelevant articles. Bibliometric analyses were performed using VOSviewer and Biblioshiny on the article metadata, and thematic analyses were conducted on their contents.FindingsPrOE is a vehicle for engaging building end-users in the design phase to address the credibility gap caused by the discrepancies between the expected and actual performance of buildings. PrOE has gained limited applications in healthcare, residential, office and educational building design for two broad purposes: design management and marketing. Using virtual reality technologies for PrOE has demonstrated significant benefits. Yet, the PrOE domain needs to mature in multiple perspectives to serve its intended purpose effectively.Originality/valueThis study identifies four knowledge gaps for future research to advance the PrOE domain: (1) developing a holistic PrOE framework, integrating comprehensive performance evaluation criteria, useable at different stages of the design phase and multi-criteria decision algorithms, (2) developing a mixed reality tool, embodying the holistic PrOE framework, (3) formulating a PrOE framework for adaptive reuse of buildings and (4) managing uncertainties in user requirements during the lifecycle in PrOE decisions.

Enhancing Decision Quality in Multi-Criteria Decision Making through CISDAC-WSM Algorithm

This paper introduces CISDAC-WSM, an innovative extension of the Weighted Sum Method (WSM) within the Multi-Criteria Decision Making (MCDM) framework. CISDAC-WSM integrates principles from Principal Component Analysis (PCA) to enhance decision outcomes by identifying the Most Significant Discriminating Axes. The algorithm operates under the assumption that alternative rankings should exhibit a monotonic trend in the scores of the Most Significant Discriminating Axes. In addition to leveraging PCA, CISDAC-WSM introduces an interval-based conflict resolution mechanism for alternatives with similar rankings. Unlike traditional outranking algorithms like PROMETHEE, CISDAC-WSM focuses on comparing each alternative only with those ranked superior, resulting in more targeted evaluations. Empirical comparisons and performance evaluations demonstrate that CISDAC-WSM consistently outperforms existing MCDM algorithms. Through its emphasis on identifying the Most Significant Discriminating Axes and the innovative conflict resolution strategy, the algorithm showcases enhanced decision-making capabilities and efficiency. While CISDAC-WSM is an extension rather than an entirely new algorithm, its contributions lie in refining established methods, incorporating PCA insights, and offering a more localized approach to outranking. This makes it a promising advancement in the field of MCDM, presenting a refined and innovative technique for achieving more informed and effective decision outcomes across various domains.

Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms.

Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.

Relation on Fermatean Neutrosophic Soft Set with Application to Sustainable Agriculture

The need for cutting-edge tools for decision-making to address the difficulties of sustainable agriculture is the driving force behind this study. We aim to integrate soft relations into fermatean neutrosophic hypersoft sets, improving the management of uncertainty. Using this approach, we provide a novel Multi-Criteria Decision-Making (MCDM) algorithm that evaluates agricultural techniques holistically by considering environmental, social, and economic variables. We highlight the significance of taking uncertainty into account when making decisions by demonstrating the algorithm’s efficiency in optimizing the choice of agricultural inputs through a real-world case study. Our solution handles such uncertainty better than conventional ways. In the future, we hope to improve the algorithm even further and investigate integrating cutting-edge technology for real-world use in changing agricultural circumstances.

Multi-dichotomous sequencing method MDSM for ordering the importance of variants’ properties in multi-criteria decision-making

There are plenty of Multi-Criteria Decision-Making (MCDM) methods that help to choose the most suitable solution assessed by several criteria (e.g. Saaty 1990; Simos 1990; Pamučar et al. 2018). They are applied in cases where several scales of different units describe the variants or the variants' properties are represented by linguistic, non-numbered terms. The inherent part of the MCDM algorithms is calculating the weights of the variants' properties, necessary for ordering the variants. If - in a certain problem - there are several properties to consider, sequencing their importance becomes a problem itself. The innovative method of sequencing is proposed in the article based on dichotomous splitting of the properties' importance. If made several times, it leads to the coherent - internally and with the decision-maker's intention - order of the properties' importance. Then the weights of the properties can be calculated with the use of different MCDM methods. The description of the method can be shortened as follows:•Divide the full set of features into two dichotomous subsets of lower and higher importance•Continue dichotomous divisions until there are only the subsets containing one element or subsets containing elements of equal importance

Agrivoltaic systems potentials in Sweden: A geospatial-assisted multi-criteria analysis

Agrivoltaic systems represent an intelligent solution combining electricity production from solar photovoltaic technology with agricultural production to avoid land use conflicts. Geographic Information System technologies can support the implementation and spread of agrivoltaic systems by identifying the most suitable areas using useful spatially explicit information concerning techno-agro-socio-economic criteria.In this study, we have developed a procedure to identify and classify suitable areas for agrivoltaic systems in Sweden. An Ordinal Priority Approach based multi-criteria decision-making algorithm is established to calculate the weights of the selected evaluation criteria through expert interviews. The land use data refers to the Corine Land Cover 2018 product.The results show that about 8.6% of the Swedish territory, approximately 38,485 km2, is suitable for installing agrivoltaic systems. Among this area, about 0.2% is classified as “excellent”, about 15% as “very good”, about 72% as “good”, about 13% as “moderate”, and about 0.1% as “poor”. Most “excellent”-classified areas are in Kalmar, Skåne, and Gotland. In contrast, most “very good” sites are in Skåne, Kalmar, and Östergötland.By deploying vertically mounted agrivoltaic systems with bifacial photovoltaic modules, the total potential installed capacity for “excellent” areas is about 2.5 GWp, while for areas classified “excellent” and “very good” is about 221 GWp. The total “excellent” areas can potentially supply about 2.4 TWh of electricity against the electricity consumption in 2021 of about 143 TWh. On the other hand, the land classified as “excellent” and “very good” could potentially provide about 207 TWh. The County of Västra Götaland shows the greatest potentials in terms of total potential electricity supply from agrivoltaic systems with about 227 TWh, followed by Skåne with a total potential of 206 TWh.

Multi-Criteria Dynamic Service Migration for Ultra-Large-Scale Edge Computing Networks

Multi-access edge computing service migration is a technology whose key objective is to support ultra-low-latency access to services. However, the complex ultra-large-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultra-densified edge nodes in 5G and beyond. In this paper, we propose a novel dynamic service migration optimization architecture for ultra-large-scale multi-access edge computing networks. We develop a new multi-criteria decision-making algorithm: Technique for Order of Preference by Similarity to Ideal Solution with Attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.

Emission and in-cylinder combustion characteristics of a spark ignition engine operated on binary mixtures of gas and liquid fuels

In this study, three different gas fuels such as hydrogen, propane, methane and six different liquid fuels such as, benzene, gasoline, toluene, hexane, methanol and ethanol have been combined and combustion characteristics have been simulated for a spark ignition engine (SIE). Variation of in-cylinder temperature, in-cylinder pressure, CO, CO2 and NO has been investigated. The gas fuel ratios in the mixtures were changed between 0 and 100% by mass. It was investigated that how the addition of gaseous fuels to liquid fuels at different ratios affects the combustion characteristics and emission formation. The gas fuel ratios considerably affected emission and combustion specifications of the engine. Increasing liquefied gas fuel ratio in the mixture leads to decrement in the temperature of charge. The maximum pressure first increases with increasing gas fuel ratio and then start to minimize after maximum pressure. The maximum peak pressure values are observed at 50% gasoline and 50% methane, 75% gasoline and 25% propane, 50% gasoline-50% hydrogen as 48.1 bar, 46 bar and 62.4 bar for different gas fuel mixtures. The minimum peak pressure value is displayed at 100% methanol as 44.06 bar. The maximum temperature value is observed at 100% benzene as 2582 K and its minimum value seems at 100% hydrogen as 2059 K. The maximum mass fractions of CO, CO2, NO are observed at 100% benzene as 0.0271, 0.161, 0.0076, the minimum mass fraction of CO2 is observed at 100% methane as 0.095. The minimum mass fraction of CO is observed at 100% methanol as 0.01243. The minimum mass fraction of NO is observed at 100% hydrogen as 0.00085. The fuels are ranked using a multi-criteria decision-making algorithm called AHP after evaluating the values of all performance criteria and their respective sub-criteria, including in-cylinder pressure, in-cylinder temperature, NO, CO2 and CO to get preference rank between fuels.

Thermally conductive and electrically resistive acrylonitrile butadiene styrene (ABS)/boron nitride composites: Optimal design using a multi-criteria decision-making approach

The purpose of this work is to propose a decision-making algorithm to select the optimal composite material for thermally conductive but electrically insulating applications, such as microelectronic packaging heat sinks, diodes, and other electronic devices. In particular, an algorithm based on the criteria importance through inter-criteria correlation (CRITIC) and additive ratio assessment (ARAS) methods are used to evaluate several conflicting attributes. The evaluated samples were acrylonitrile butadiene styrene (ABS) composites filled with 0–30 vol% of boron nitride (BN) particles and prepared through melt compounding. The performance attributes considered through testing were heat conductivity, electrical resistivity, density, hardness, and tensile properties (Young's modulus, tensile strength, and elongation). As expected, the composite containing 30 vol% BN exhibited the highest heat conductivity, electrical resistivity, and Young's modulus. Meanwhile, unfilled ABS had the highest elongation at break, tensile strength, and lowest density. With respect to hardness, the 1 vol% BN-loaded composite proved to be superior. Therefore, the experimental data revealed a considerable compositional dependence with no obvious trend. The optimal composition was identified by adopting the CRITIC-ARAS multi-criteria decision-making algorithm, based on which the 30 vol% BN-containing composite was dominant among all the prepared samples. A validation through other decision-making techniques was performed to support the robustness of the proposed technique. Additionally, a sensitivity analysis was carried out on several weight exchange scenarios to see the stability of the ranking results.

An Integrated Multi-Criteria Decision Making Model for the Assessment of Public Private Partnerships in Transportation Projects

Public–private partnership (PPP) infrastructure projects have attracted attention over the past few years. In this regard, the selection of private partners is an integral decision to ensure its success. The selection process needs to identify, scrutinize, and pre-qualify potential private partners that sustain the greatest potential in delivering the designated public–private partnership projects. To this end, this research paper proposes an integrated multi-criteria decision-making (MCDM) model for the purpose of selection of the best private partners in PPP projects. The developed model (HYBD_MCDM) is conceptualized based on two tiers of multi-criteria decision making. In the first tier, the fuzzy analytical network process (FANP) is exploited to scrutinize the relative importance of the priorities of the selection criteria of private partners. In this respect, the PPP selection criteria are categorized as safety, environmental, technical, financial, political policy, and managerial. In the second tier, a set of seven multi-criteria decision-making (MCDM) algorithms is leveraged to determine the best private partners to deliver PPP projects. These algorithms comprise the combined compromise solution (CoCoSo), simple weighted sum product (WISP), measurement alternatives and ranking according to compromise solution (MARCOS), combinative distance-based assessment (CODAS), weighted aggregate sum product assessment (WASPAS), technique for order of preference by similarity to ideal solution (TOPSIS), and FANP. Thereafter, the Copeland algorithm is deployed to amalgamate the obtained rankings from the seven MCDM algorithms. Four real-world case studies are analyzed to test the implementation and applicability of the developed integrated model. The results indicate that varying levels of importance were exhibited among the managerial, political, and safety and environmental criteria based on the nature of the infrastructure projects. Additionally, the financial and technical criteria were appended as the most important criteria across the different infrastructure projects. It can be argued that the developed model can guide executives of governments to appraise their partner’s ability to achieve their strategic objectives. It also sheds light on prospective private partners’ strengths, weaknesses, and capacities in an attempt to neutralize threats and exploit opportunities offered by today’s construction business market.

Novel MCDM Methods and Similarity Measures for Extended Fuzzy Parameterized Possibility Fuzzy Soft Information with Their Applications

Complicated uncertainties arising in the multicriteria decision-making (MCDM) problems that show distinct possible satisfaction of the subjects to favorable and equally unfavorable parameters with varying preferences require reliable decision-making under comprehensive mathematical tools. For such complications, this work aims to develop a novel fuzzy parameterized possibility fuzzy bipolar soft set model as a fuzzy parameterized bipolar soft extension of possibility fuzzy sets. The proposed model efficiently depicts the possibility of fuzzy belongingness of alternatives under fuzzy parameterized bipolar parameters (or attributes). The respective operations and properties such as subset, complement, union, and intersection are presented along with their numerical illustrations. Two logical operations namely “AND” and “OR” operations followed by two corresponding MCDM algorithms have been developed and implemented. Furthermore, similarity measures between fuzzy parameterized possibility fuzzy bipolar soft sets are proposed and applied to an agricultural land selection scenario. Finally, a comparative analysis of current work with existing ones is discussed in detail to show the eminent quality of the proposed work over them.

Multi-Objective Optimization of High-Power Microwave Sources Based on Multi-Criteria Decision-Making and Multi-Objective Micro-Genetic Algorithm

Multi-Objective Optimization of High-Power Microwave Sources Based on Multi-Criteria Decision-Making and Multi-Objective Micro-Genetic Algorithm

Intuitionistic fuzzy EM-SWARA-TOPSIS approach based on new distance measure to assess the medical waste treatment techniques

Due to the potential harm to public health as well as the environment, medical waste treatment have emerged as a critical concern, especially in developing nations. The selection of a suitable medical waste treatment technique (MWTT) is now characterized as a critical multi-criteria decision-making (MCDM) problem because of the incorporation of several competing criteria. This study provides a hybrid MCDM technique for assessing MWTTs concerning social, environmental, economic, and technical criteria in an intuitionistic fuzzy environment. The intuitionistic fuzzy set (IFS) is a potent tool for expressing ambiguous and vague information efficiently and has proven effective for MCDM problems. In IFS environment a new distance measure and an entropy measure (EM) are introduced by employing the generalized Csiszar f-divergence measure, which are further used to construct the MCDM algorithm. The introduced entropy measure is used to examine the objective weights of criteria, and the distance measure is employed to construct the technique for order performance by similarity to ideal solution (TOPSIS) for the assessment of the alternatives. Furthermore, the significance of the criteria for the proposed MCDM technique is evaluated by integrated weights (combining objective and subjective weights). The Stepwise Weight Assessment Ratio Analysis (SWARA) technique is used to obtain the subjective weights for criteria. Thus, a hybrid MCDM technique, namely IF-EM-SWARA-TOPSIS, is developed. To exhibit the effectiveness and applicability of the suggested framework, a MWTT selection problem in Indian context is depicted. The comparative study and sensitivity assessment are conducted to show the sustainability and stability of the proposed IF-EM-SWARA-TOPSIS technique for ranking the preference order of the MWTTs. Moreover, the practical implications of the proposed method are discussed.

A modified artificial bee colony algorithm based on a non-dominated sorting genetic approach for combined economic-emission load dispatch problem

The multi-objective optimization algorithms (MOO) are used to obtain the best compromising solutions when two or more objective functions need to be optimized simultaneously. The convergence and diversity are critical factors to consider while solving the MOO problems because they determine the possibility of obtaining an evenly distributed Pareto front. This paper proposes a hybrid optimization algorithm to solve a multi-objective economic emission dispatch (MOEED) problem of electrical power systems. The electrical power generated by consuming fossil fuels is very costly and also burning of these fuels contributes to global warming. Hence, electrical power is generated at the least cost and emission. The MOEED problems have been solved in the past by using swarm intelligence and evolutionary algorithms. However, the solutions reported in the literature, are either inferior or the constraints are violated. The algorithm proposed in this paper is an integration of an Artificial Bee Colony optimization algorithm and a Non-Dominated Sorting Genetic Algorithm-II. The effectiveness of the proposed algorithm is evaluated by applying it to three test systems having 6, 10, and 40 coal-based generators. Additionally, various multi-criteria decision-making algorithms are used to identify the best non-dominated solutions obtained by the proposed algorithm and compared with previously reported results. The best fuel costs obtained by the proposed approach, for a 6-unit test system with and without transmission losses are found to be 605.9983 and 600.11140 $/h respectively. While the best emission values, for this test system, with and without transmission losses are found to be 0.1941 and 0.1942 tons/h. Moreover, the best fuel costs obtained by the proposed approach, for 10 and 40-unit test systems are found to be 111181.9871 and 121369.0838 $/h respectively. Furthermore, the best emission values for these test systems are found to be 3932.24322 and 176682.264 tons/h respectively. All these results are obtained without constraint violations and within 10–600 iterations.