Improved Fuzzification Method for Multi-Objective Decision-Making and Its Application in Evaluation of Highway Planning

A new fuzzification method for multi-objective decision-making and selective sorting is proposed on the basis of the fuzzy consistent relation, and the specific algorithm is presented. The method is applied to the evaluation of highway planning of Zhanjiang city. To decrease the subjectivity in the process of decision-making, the LOWA operator is introduced, and a discussion on how to select appropriate weights involved in multi-objective sorting is made. It is concluded that it is feasible to apply the fuzzy consistent relation to multi-objective decision-making analysis, and the improved fuzzication method is workable.

Fuzzy multiple attribute decision-making model for synergic knowledge innovation in supply chain

The procedure of supply chain development is the process of continuously congregating knowledge and transforming knowledge.First,the precondition of synergic knowledge innovation in the supply chain is narrated.Then the characteristics of synergic knowledge innovation in the supply chain are analyzed,including complexity,accumulating and evolving process,and the cooperation of members and network integration.Due to the characteristics of multi-factors and uncertainties of the supply chain system,the fuzzy multi-attribution group decision-making model is introduced to solve the involved problem of synergic knowledge innovation in the supply chain.After elaborating on steps of using the fuzzy multiple attribute decision-making(MADM)model,the procedure of decision making for synergic knowledge innovation in the supply chain is explained from an example in the application of a fuzzy MADM model.The fuzzy MADM model,which amalgamates intuition and resolution decision-making can effectively improve the rationality of decision-making for synergic knowledge innovation in the supply chain.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Cotangent Similarity Measure of Consistent Neutrosophic Sets and Application to Multiple Attribute Decision-Making Problems in Neutrosophic Multi-Valued Setting

A neutrosophic multi-valued set(NMVS)is a crucial representation for true,false,and indeterminate multivalued information.Then,a consistent single-valued neutrosophic set(CSVNS)can effectively reflect the mean and consistency degree of true,false,and indeterminate multi-valued sequences and solve the operational issues between different multi-valued sequence lengths in NMVS.However,there has been no research on consistent single-valued neutrosophic similarity measures in the existing literature.This paper proposes cotangent similarity measures and weighted cotangent similarity measures between CSVNSs based on cotangent function in the neutrosophic multi-valued setting.The cosine similarity measures showthe cosine of the angle between two vectors projected into amultidimensional space,rather than their distance.The cotangent similaritymeasures in this study can alleviate several shortcomings of cosine similarity measures in vector space to a certain extent.Then,a decisionmaking approach is presented in viewof the established cotangent similarity measures in the case of NMVSs.Finally,the developed decision-making approach is applied to selection problems of potential cars.The proposed approach has obtained two different results,which have the same sort sequence as the compared literature.The decision results prove its validity and effectiveness.Meantime,it also provides a new manner for neutrosophic multi-valued decision-making issues.

Application of Grey Situation Decision-Making Theory in Site Selection of a Waste Sanitary Landfill

An application of an unequal-weighted multi-objective decision making method in site selection of a waste sanitary landfill is discussed. The eight factors, which affected possible options, were: size and capacity of the landfill, permeability of the stratum, the average difference in elevation between the groundwater level and the bottom of the landfill pit, quality and source of clay, the quality grade of the landfill site, the effect of landfill engineering on nearby residents, distance to the water supply and the water source as well as the cost of construction and waste transport. These are determined, given the conditions of the geological environment, the need for environmental protection and landfill site construction and transportation related to the design and operation of a sanitary landfill. The weights of the eight factors were further investigated based on the difference in their relevance. Combined with practical experience from Xuzhou city (Jiangsu province, China), the objectives, effects and weights of grey decision-making were deter- mined and the process and outcome of the landfill site selection are stated in detail. The decision-making results have been proven to be acceptable and correct. As we show, unequal-weighted multi-objective grey situation decision-mak- ing is characterized by easy calculations and good maneuverability when used in landfill site selection. The number of factors (objectives) affecting the outcome and the quantitative method of qualitative indices can be adjusted on the basis of concrete conditions in landfill site selection. Therefore, unequal-weighted multi-objective grey situation decision making is a feasible method in selecting landfill sites which offers a reference method for landfill site selection else- where. It is a useful, rational and scientific exploration in the choice of`a landfill site.

Group training on the improvement of college students'career decision-making self-efficacy

A group training was conducted on 17 college students to improve their career decision-making self-efficacy (CDMSE). The result showed that there was significant difference between the pre-test and the post-test for the experimental group (n = 17), whereas no significant difference was found between the pre-test and the post- test for the control group (n = 17). In the pre-test, there was no significant difference between the experimental group and the control group, and obvious difference between the two groups was found in the post-test. This indicated that the group training was effective on improving the CDMSE of the college students whose scores of CDMSE were below 27% point of the total students.

Aczel-Alsina Weighted Aggregation Operators of Simplified Neutrosophic Numbers and Its Application in Multiple Attribute Decision Making

The simplified neutrosophic number(SNN)can represent uncertain,imprecise,incomplete,and inconsistent information that exists in scientific,technological,and engineering fields.Hence,it is a useful tool for describing truth,falsity,and indeterminacy information in multiple attribute decision-making(MADM)problems.To suit decision makers’preference selection,the operational flexibility of aggregation operators shows its importance in dealing with the flexible decision-making problems in the SNN environment.To solve this problem,this paper develops the Aczel-Alsina aggregation operators of SNNs for MADM problems in view of the Aczel-Alsina operational flexibility.First,we define the Aczel-Alsina operations of SNNs.Then,the Aczel-Alsina aggregation operators of SNNs are presented based on the defined Aczel-Alsina operations of SNNs.Next,a MADM method is established using the proposed aggregation operators under the SNN environment.Lastly,an illustrative example about slope treatment scheme choices is provided to indicate the practicality and efficiency of the established method.By comparison with the existing relative MADM methods,the results show that the established MADM method can overcome the insufficiency of decision flexibility in the existing MADM methods and demonstrate the metric of flexible decision-making.

Multi-objective optimization for deepwater dynamic umbilical installation analysis

We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.

A new method for performance evaluation of decision-making units with application to service industry

The decision-making units(DMUs)in the modern service industries may produce desirable outputs and undesirable outputs.For the decision makers,some outputs may be more desired than others although all of them are desirable.Considering these characteristics,this work combines the data envelopment analysis(DEA)and the multiple attributes decision-making(MADM)method,to make a reasonable and comprehensive performance evaluation for DMUs.Specifically,three DEA-based models are modified to obtain more reasonable efficiency scores for DMUs.The MADM method is used to determine the weights of outputs based on the preference ratings within the outputs.The efficiency scores are then multiplied by the aggregated outputs quantities to obtain the comprehensive performance scores for evaluation.The effectiveness of the proposed models is demonstrated by extensive numerical experiments.

Research on the Infuence of Multiple Parameters on the Responses of a B-type Subway Train

To obtain improved comprehensive crashworthiness criteria for a B-type subway train,the infuence laws of the vehicle design collision weight M and empty stroke D on the train’s collision responses were investigated,and multiobjective optimization and decision-making were performed to minimize TS(total compression displacement along the moving train)and TAMA(the overall mean acceleration along the moving train).Firstly,a one-dimensional train collision dynamics model was established and verifed by comparing with the results of the fnite element model.Secondly,based on the dynamics model,the infuence laws of M and D on the collision responses,such as the energy-absorbing devices’displacements and absorbed energy,vehicles’velocity and acceleration,TS,TAMA and the coupling correlation efect were investigated.Then,surrogate models for TS and TAMA were developed using the optimal Latin hypercube method(OLHD)and response surface method(RSM),and multi-objective optimization was conducted using the particle swarm optimization algorithm method(MPOSO).Finally,the entropy method was used to obtain the weight coefcients for TS and TAMA,and multi-objective decision-making was performed.The results indicate that D and M signifcantly afect the compression displacements and energy absorption of the frst three collision interfaces,but have limited impact on the last three collision interfaces.The velocity versus time curves of vehicle M1 and M2 are shifted and parallel with diferent D.However,the velocity versus time curves of all the vehicles are shifted but gradually divergent with diferent M.The maximum collision instantaneous accelerations of the vehicles are directly determined by M,but are only slightly afected by D.Under the coupling efect,all concerned collision responses are strongly correlated with M;however,the responses are weakly correlated with D except for the compression displacement at the M2-M3 collision interface and the maximum collision instantaneous acceleration of vehicle M2.The comprehensive crashworthiness criteria of the B-type subway train were signifcantly improved after multi-objective optimization and decision-making.The research provides more theoretical and engineering application references for the subway train crashworthiness design.

Closed-loop supply chain network equilibrium strategy model with environmental protection objectives

With the increasing popularity of ecological civilization and sustainable development,enterprises should consider environmental protection measures in their operations in addition to pursue their economic interests.This paper establsihes a closed-loop supply chain network model composed of multiple suppliers,manufacturers,retailers,recyclers,and demand markets—regarding their dual goals of the profit maximization and the minimization of carbon emissions.The conditions necessary for establishing overall equilibrium and an equilibrium model of the entire closed-loop supply chain network are determined by applying variational inequality and dual theory.A modified projection contraction algorithm is used to design a model-solving program.Finally,using numerical examples,the paper conducts a comparative static analysis on important parameters such as the weight coefficients of environmental protection objectives and consumers'awareness of low-carbon environmental protection and attains some beneficial enlightenment on management.The results indicate that when the environmental protection objectives of a certain type of enterprise increases,both the economic benefits and environmental protection performance will improve;when the environmental protection objectives of all enterprises increases simultaneously,environmental protection performance improves significantly,but the changes in economic benefits of different enterprises are inconsistent and profit coordination is more complex.Although consumers’awareness of low-carbon preference could improve environmental performance,it reduces the overall profits of network members and the entire closed-loop supply chain network as a whole.The above conclusions can be used as a reference for the government in designing low-carbon environmental protection policy and in closed-loop supply chain research.

Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles(UAVs)in a dynamic environment is considered a challenging task in autonomous flight control design.In this work,an efficient method based on a Multi-Objective MultiVerse Optimization(MOMVO)algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives,the modified Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm(MSSA),Multi-Objective Grey Wolf Optimizer(MOGWO),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-Dominated Genetic Algorithm II(NSGAII)is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.

Conjunctive Use of Engineering and Optimization in Water Distribution System Design

Water Distribution Systems (WDSs) design and operation are usually done on a case-by-case basis. Numerous models have been proposed in the literature to solve specific problems in this field. The implementation of these models to any real-world WDS optimization problem is left to the discretion of designers who lack the necessary tools that will guide them in the decision-making process for a given WDS design project. Practitioners are not always very familiar with optimization applied to water network design. This results in a quasi-exclusive use of engineering judgment when dealing with this issue. In order to support a decision process in this field, the present article suggests a step-by-step approach to solve the multi-objective design problem by using both engineering and optimization. A genetic algorithm is proposed as the optimization tool and the targeted objectives are: 1) to minimize the total cost (capital and operation), 2) to minimize the residence time of the water within the system and 3) to maximize a network reliability metric. The results of the case study show that preliminary analysis can significantly reduce decision variables and computational burden. Therefore, the approach will help network design practitioners to reduce optimization problems to a more manageable size.

Mixed Least Square Method for Priority of Complementary Judgement Matrix and Its Algorithm

Based on the concept of multiplicative fuzzy consistent complementary judgement matrix, the mixed least square method （MLSM） for priority of complementary judgement matrix is proposed and proved. Then, the corresponding convergent iterative algorithm is given and its convergence is proved. Finally, some main properties of the developed priority method, such as rank preservation under strong condition, etc., ate introduced. The theoretical analyses show that the MLSM can sufficiently reflect the preference information of the decision maker, and is easy to realize on a computer.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

Fuzzy-logic Method for Global Quality Optimization Problem of the Programmed Action Investment

In order to analyze the planning of a transport linear infrastructure(railway or ordinary road),in order to optimize a relationship work-environment after-work,the study team(engineers,architects,economists,etc),realize a careful prearranged analysis about the characteristic of the site and the large area which are involved by the work project and,once one found all possible alternative solutions,he should compare them through the use of suitable technical,economical and environmental parameters,choosing that one which maximize the global utility of the public investment.In this paper we study a fuzzy-logic method in order to help the decision maker in the analysis of the programmed action public investment.

Using Two-layer Minimax Optimization and DEA to Determine Attribute Weights

This study aims to explore a novel method for determining attribute weights,which is a key issue in constructing and analyzing multiple-attribute decision-making(MADM)problems.To this end,a hybrid approach combining the data envelopment analysis(DEA)model without explicit inputs(DEA-WEI)and a two-layer minimax optimization scheme is developed.It is demonstrated that in this approach,the most favorable set of weights is first considered for each alternative or decision-making unit(DMU)and these weight sets are then aggregated to determine the best compromise weights for attributes,with the interests of all DMUs simultaneously considered in a fair manner.This approach is particularly suitable for situations where the preferences of decision-makers(DMs)are either unclear or difficult to acquire.Two case studies are conducted to illustrate the proposed approach and its use for determining weights for attributes in practice.The first case concerns the assessment of research strengths of 24 selected countries using certain measures,and the second concerns the analysis of the performance of 64 selected Chinese universities,where the preferences of DMs are either unknown or ambiguous,but the weights of the attributes should be assigned in a fair and unbiased manner.

Attribute of Big Data Analytics Quality Affecting Business Performance

With an accelerating increase of business benefits produced from big data analytics (if used appropriately and intelligently by businesses in the private and public sectors), this study focused on empirically identifying the big data analytics (BDA) attributes. These attributes were classified into four groups (i.e., value innovation, social impact, precision, and completeness of BDA quality) and were found to influence the decision-making performance and business performance outcomes. A structural equation modeling analysis using 382 responses from a BDA related to practitioners indicated that the attributes of representativeness, predictability, interpretability, and innovativeness as related to value innovation greatly enhanced the decision-making confidence and effectiveness of decision makers who make decisions using big data. In addition, individuality, collectivity, and willfulness, which are related to social impact, also greatly improved the decision-making confidence and effectiveness of the same decision makers. This shows that the value innovation and social impact, which have received relatively less attention in previous studies, are the crucial attributes for BDA quality as they influence the decision-making performance. Comprehensiveness, factuality, and realism, which are linked to completeness, also have similar results. Furthermore, the higher the decision-making confidence of the decision makers who used big data was, the higher the financial performance of their companies. In addition, high decision-making confidence using big data was found to improve the nonfinancial performance metrics such as customer satisfaction and quality levels as well as product development capabilities. High decision-making effectiveness with big data was also shown to improve the nonfinancial performance metrics.

Long-term sustainability and resilience enhancement of building portfolios

The role of community building portfolios in socioeconomic development and the growth of the built environ-ment cannot be overstated.Damage to these structures can have far-reaching consequences on socioeconomic and environmental aspects,requiring a long-term perspective for recovery.As communities aim to enhance their resilience and sustainability,there is a cost burden that needs to be considered.To address this issue,this pa-per proposes a community-level performance enhancement approach that focuses on optimizing the long-term resilience and sustainability of community building portfolios,taking into account recurrent seismic hazards.A Gaussian process surrogate-based multi-objective optimization framework is utilized to optimize the cost objec-tive while considering performance indicators for resilience and sustainability.The proposed framework involves using performance-based assessment methods to evaluate the socioeconomic and environmental consequences under stochastic and recurrent seismic hazard scenarios.These evaluated indicators are then used to efficiently optimize the community resilience and sustainability,taking into account the retrofit costs.Finally,approximate Pareto-optimal solutions are extracted and utilized for decision-making.In summary,this paper presents a novel approach for optimizing the long-term resilience and sustainability of community building portfolios by consid-ering recurrent seismic hazards.The proposed framework incorporates performance-based assessment methods and multi-objective optimization techniques to achieve an optimal balance between cost,resilience,and sustain-ability,with the ultimate goal of enhancing community well-being and decision-making in the face of seismic hazards.

Optimal microgrid planning for enhancing ancillary service provision

Microgrids have presented themselves as an effective concept to guarantee a reliable,efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks.Moreover,microgrids could offer effective ancillary services(AS)to the power utility,although this will not be possible before the traditional planning and operation methodologies are updated.Hence,a probabilistic multi-objective microgrid planning(POMMP)methodology is proposed in this paper to contemplate the large number of variables,multiple objectives,and different constraints and uncertainties involved in the microgrid planning.The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode,while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized,respectively.For that purpose,probabilistic models and a true multi-objective optimization problem are implemented in the methodology.The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ)optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem.