Fuzzy Multi-objective Decision Research Articles

Construction of Online Interactive Teaching Platform for Drama, Film and Television Majors Based on Fuzzy Multi-Objective Decision Making Algorithm

Abstract This study introduces a multi-objective decision-making model tailored for developing an educational platform, blending economic considerations with the platform’s operational constraints. It employs a fuzzy set approach for algorithm design, utilizing a method that minimizes deviations in membership values for solving. The research culminates creating an online interactive teaching platform for drama and film studies. This platform is architecturally founded on the B/S framework, incorporates functional modules derived from its educational objectives, and utilizes MySQL for data flow design. Comparing it to conventional teaching methods, a comprehensive evaluation demonstrates its practical application in real-world teaching scenarios. Performance tests reveal module response times between 2.7 to 3.0 seconds, with compatibility tests ranging from 0.11 to 0.36 seconds. Under a load of 200 simultaneous users, the database connection pool maintains an optimal size of about 35 connections, with thread counts near 50 and zero idle database connections. The study also quantifies the platform’s impact on various educational aspects, showing correlation coefficients for learning attitude (0.88), motivation (0.50), behavior (0.72), and teacher-student (0.15) as well as student-student interactions (0.27) with the learning effectiveness of the platform.

A fuzzy multiobjective team decision model for CODP and supplier selection in customized logistics service supply chain

To address information vagueness and uncertainty of the order demand, the logistics service provider (LSP) service capacity and the target preference caused by emergency events, a multiobjective team optimization model is developed for the customer order decoupling point (CODP) decision, supplier selection and order quantity allocation problems in customized logistics service supply chain. Three important techniques are described: (1) Fuzzy objective coefficients due to the uncertainty of the order and objective weights. (2) Fuzzy constraint parameters due to LSP service capacity and order demand variations. (3) A fuzzy single objective weighting technique with acceptance coefficient of constraint and fuzzy parameters that reflects decision-maker preferences. The acceptance coefficients of the constraints are integrated into the single objective model, which reflects the satisfaction level of the logistics service integrator (LSI) for feasible solutions. Then, we perform fuzzy decision and feasible solution design based on the center of gravity method defuzzification and maximum membership degree principle. To optimize decisions for decision-makers, the effects of some important fuzzy objective coefficients and fuzzy constraint parameters on the CODP decision and the acceptance coefficient of constraint was explored through sensitivity analysis. Numerical experiments show that the scale effect coefficient and objective function threshold have greater effects on the CODP decision.

Optimal locating by integrating volumetric fuzzy sets and geographic coordinate system: An application to healthcare

The volumetric fuzzy set (VFS) deals with the uncertainties associated with two-dimensional variables. This study shows the application of VFS in finding the optimal coordinates for a new hospital by considering several fuzzy goals. For this purpose, we first define two types of volumetric fuzzy goals: type one following the proximity to an ideal point and type two following far from an anti-ideal point. Then, we extract the membership functions (MFs) for type-one and type-two volumetric fuzzy goals. These MFs determine the membership degrees of each point for volumetric fuzzy goals based on its distances from the ideal or anti-ideal points. We consider the Manhattan distance to extract these MFs because it better measures the real distance between two points in urban areas. Next, we design a new weighted volumetric fuzzy multi-objective decision-making (VFMODM) model considering type-one and type-two fuzzy goals. This model answers this question: what are the optimal coordinates for a new hospital to maximize overall satisfaction? Compared to existing fuzzy optimization models, the VFMODM model considers uncertainty for two-dimensional variables.

Product selection by consumers utilizing the membership values

Assessment and assortment of the most favorable, competent, and trustworthy products is getting to be more and more important for businesses in the current scenario, so the majority of the production industries do their utmost to achieve the requirements, standards, and expenditure patterns of the consumer to attain their expectations. Approaches are made to fulfill the needs of the consumer, as shown by the creation of diverse technologies for improving the capability to manage complex optimizations regarding decision attributes that involve uncertainty. The procedure of detecting the best alternative from all the feasible options is the basis of the decision-making problem. This article recommended two approaches: (a) multi-objective decision making by utilizing fuzzy technique and (b) fuzzy TOPSIS (Technique for Ordering Preferences by Similarity to Ideal Solution) method for choosing the best product while many products with many attributes are present in the marketplace. As per the need of the customer priority and their satisfaction level, this paper used linguistic quantifiers to evaluate the weight of the objective that is represented in triangular or trapezoidal fuzzy numbers. To demonstrate the technique used for the suggested approaches a case study was conducted. The simulation result shows a specific path that fuzzy logic can evolve in the problem of decision making as well as in the planning process in the product selection practice. Solutions obtained by employing both techniques were compared, which urges that the fuzzy TOPSIS approach provides more accurate information than the fuzzy MODM (multi-objective decision making) approach to find the best product from among many available products. This work helps to assist the managers to adopt suitable techniques while planning to design the product according to the customer requirements so that the consumer can get the best product as per their objectives.

Optimising sustainable renewable energy portfolios using a multi-tolerance fuzzy goal programming approach

ABSTRACT The application of fuzzy multi-objective decision-making (F-MODM) to the optimization problems of renewable energy (RE) planning with uncertainties has become increasingly popular, although it is still rare in contrast to the use of traditional multi-attribute decision-making (MADM) methods. This study contributes to the domain of F-MODM modeling and the empirical applications of RE. Methodologically, as a recent trend to enhance the solution quality of fuzzy goal programming (FGP) in F-MODM is to introduce the “tolerance” (or admissible violation) concept to membership functions (MFs), this study proposes a novel ‘FGP-MT’ (multi-tolerance FGP) model to overcome the limitations of ‘single tolerance’ during FGP modeling (i.e. being unable to fit complicated ‘multitolerance’ decision contexts and running the risk of missing better optimality). The model is first verified using a numerical example from the F-MODM literature. Empirically, an application of the FGP-MT model is then considered for a SREPO (sustainable renewable energy portfolio optimization) decision, searching for the optimal electricity generation portfolio from multiple RE resources in Algeria for sustainable RE development, subject to the special preference structure of the decision-maker (DM) that involves manifesting two or more tolerances for a fuzzy goal. As the model performs well or even better in determining the most suitable SREPO decision under this condition, insights are gained for the studied case, and future applications of the model are therefore expected.

Soft computing tool for aiding the integration of hybrid sustainable renewable energy systems, case of Putumayo, Colombia

This study contributes to the development of a soft computing tool used for the integration of hybrid renewable energy systems. The tool consists of the integration of a forecasting sub-tool for assessing quantitative and qualitative data. These data are input for a fuzzy multi-objective decision model developed in GAMS, where the total present value and the CO2 emissions of the system are minimized to obtain a set of Pareto alternatives. These alternatives are used as input for a fuzzy multi-attribute model developed in MATLAB. The CO2 emissions and the total present value, together with social, technologic, and environmental criteria are used as inputs for choosing the most sustainable alternative. Finally, the tool is tested in Puerto Guzmán, a small village of the department of Putumayo in southern Colombia. The selected and most sustainable alternative is composed of 31.2% diesel, 47.7% gasification of biomass, 7.9% solar photovoltaic and the rest with small hydro. The total present value of the system is 0.56 million USD and CO2 emissions are 0.4 million kg.

Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty

An integrated energy system (IES) is considered as an effective approach to reduce carbon emissions, lower energy supply costs and increase system flexibility. As numerous energy conversion and storage devices with various features have been developed, determining their types and capacity size and optimizing the synergic action of all selected energy devices under load uncertainty have become challenging issues during the planning and designing of a new IES. To address these issues, a load uncertainty model is constructed by integrating the regional equivalent standard building-integrated load prediction method, the robust uncertainty set method and the fuzzy C-means clustering algorithm. Based on this uncertainty model, this paper proposes a planning method that incorporates the fuzzy multi-objective decision and two-stage adaptive robust optimization methods for handling these challenging issues. The multi-objective decision method enables comprehensive optimization based on the system's economic performance, carbon emissions and grid integration level. The two-stage adaptive robust optimization method aims to optimize the single-objective problem transformed by the multi-objective decision method. The planning method is implemented in an industrial park in the Baoding City of China. The influence of load budget uncertainty, annual load growth rate and energy purchase price is then investigated. The results show that the load budget uncertainty and annual load growth rate only affect the optimal capacity size of the selected devices, whereas the energy purchase price influences both the optimal device types and their capacity size. The optimal capacity size remains constant when the load budget uncertainty exceeds 3. This study provides a methodology to select the suitable device types and determine their capacity size for IES under load uncertainty, which will benefit the design of a new IES.

Fuzzy multi-objective decision making approach for nuclear power plant installation

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.

Solving fully fuzzy multi-objective linear programming problem with fuzzy dominant degrees

In this paper, we investigate all relative relationships between two fuzzy numbers. Then, we introduce new relative measures to compare two fuzzy numbers instead of using absolute value to represent the fuzzy number. These measures address the dominant level that one fuzzy number is better than the other in terms of its position and shape. The so-called absolute fuzzy dominant degree and relative fuzzy dominant degree are developed to measure the differences between two fuzzy numbers applying for different types of constraint. These measures could capture all the shape’s characteristics and relative positions of fuzzy numbers. Finally, the fully fuzzy multi-objective decision making (FFMODM) problem is solved by using these fuzzy dominant degrees. For validation, we compare our approach to the fuzzy ranking method of the linear ranking function. Our obtained results show better performance.

Fuzzy Lattice Order Group Decision for Preference Ranking in Conflict Analysis

Methods of fuzzy multi-objective lattice order decision making (F-MOLODM) for analyses of the fuzzy and intransitive preferences of decision makers involved in a conflict analysis are devised for the preference ranking of the states in a conflict, which defines the trapezoidal fuzzy number of the preference and constructs a multi-objective group decision-making fuzzy preference matrix. An algorithm for F-LOGDM is proposed to capture uncertainty and intransitive decision problems, and the relationship among lattice elements of the preference structure under fuzzy environments is defined. The application of these decision technologies to the square dance conflict illustrate how the method proposed in this paper can be utilized in practice. The performance of the lattice order preference ranking method applied to the conflict is compared with that of traditional methods. Research shows that either lattice order preference ranking, or traditional methods acquires Nash stability. However, the stable state under traditional methods will be reached only if all players pay a certain price.

Research on the Evaluation Model of Multi-objective Optimal Allocation of Water Resources in Guizhou Province

Aiming at the characteristics of multiple water sources and multiple users in Guizhou’s urban water system, this paper establishes a multi-objective allocation model of urban water resources. The paper uses multi-objective programming to solve the model, and analyzes the impact of different target weights on the water resources allocation results of Guizhou Province in the fuzzy multi-objective decision model. The results show that the optimization model and fuzzy decision are reasonable and feasible.

Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data

Hybrid Model Combined Fuzzy Multi-Objective Decision Making with Feed Forward Neural Network (F-MODM-FFNN) For Very Short-Term Load Forecasting Based on Weather Data

A fuzzy multi-objective decision system for recoverable remanufacturing planning

This study develops a novel fuzzy multi-objective decision system (FMODS) that uses the Simplex method, Access database technology and the JAVA programming language, for recoverable product of remanufacturing planning. The system can be used to determine optimal purchase quantities and lot release sizes to make decision system for remanufacturing planning more effective, more convenient and smarter. The system has both single-objective linear programming (SOLP) and fuzzy multi-objective linear programming (FMOLP) functions to help managers in practical situations. The results in this investigation indicate that the proposed FMODS has a very favorable solution time in cases of large demand, and can significantly shorten the production planning time.

Research on the Status of Sports Economy in the National Economy Development in Multi-objective Fuzzy Decision Model

Research on the Status of Sports Economy in the National Economy Development in Multi-objective Fuzzy Decision Model

A new robust-possibilistic reliable hub protection model with elastic demands and backup hubs under risk

This research considers a new reliable backup hub under the robust-possibilistic uncertainty. The considered multi-objective model aims to minimize the total transportation cost of a p-hub median protection model while maximizing the flow between each pair node and minimizing the total transportation time. Additionally, it considers the potential disruption risks in the hub links of the transportation network for which a failure mode and effect analysis (FMEA) method is used to measure the risk of network’s arcs. Consequently, the demand is assumed to be dependent on the utility proposed by each hub. Thus, the demand elasticity is considered in this paper. As the exact values of some parameters are not known in advance, a fuzzy multi-objective decision making-based approach is proposed to optimally solve small-sized problems. Furthermore, a number of sensitivity analyses are done on a real case study in the Iranian Road Transportation Sector with useful managerial insights.

OS‐ELM‐based visual and inertial indoor localisation method with adaptive matching step

Indoor localisation has been a hot research topic because of its importance for location-based services. The proposed method takes affine transformation matrices and inertial information as input vectors to online sequential extreme learning machine (OS-ELM) and outputs relative translation between the two matching frames. The universal approximation capability and extreme learning speed of OS-ELM enable the proposed method to adapt to several matching steps and reduce the localisation error to a low scale. Fuzzy multi-objective decision making and corner detection are also used to improve the final result. Experimental results validate that the proposed method can provide a good localisation accuracy and improve the efficiency of localisation.

A collaborative design method for satellite module component assignment and layout optimization

The collaborative design of the multi-module satellite component (equipment) assignment and layout is the key aspect of the overall satellite design, and the two parts are closely related. In the past, satellite module component layout optimization usually adopted fixed component assignments, which remained constant in the layout optimization stage. If the components were improperly distributed in these modules, it would seriously affect the layout optimization. To overcome this disadvantage, a collaborative design method for the component assignment and layout design is presented for the multi-module (or multi-bearing plate) satellite component layout problem, based on a multi-agent system. First, the component assignment agent adopted a multi-objective optimization method (the non-dominated sorting genetic algorithm II, NSGA-II) to obtain the approximate Pareto solution set of the satellite component assignment scheme. Second, it adopted a fuzzy multi-objective decision method to select a high-quality component assignment scheme from the approximate Pareto solution set. Third, the layout agent employed a dual system co-evolutionary method for the layout optimization design. In the process of the layout optimization, the layout result is fed back to the component assignment design, and the component assignment is adjusted according to the result of the layout optimization. Thus, the above process is continually iterated to achieve the optimal collaborative design of the component assignment and the layout. The proposed method is applied to a simplified multi-module satellite component assignment and layout optimization problem and aims to provide a reference and technical support for other similar multi-module equipment assignment and layout optimization problems.

A novel hybrid approach based on fuzzy multi-criteria decision-making tools for assessing sustainable alternatives of power generation in San Andrés Island

This paper presents a novel approach based on fuzzy multi-criteria decision-making tools for assessing sustainable alternatives of power generation in San Andrés Island, Colombia. In the first stage, the energy system was established. A fuzzy multi-objective decision approach based on a mixed-integer linear model was implemented to obtain a set of alternatives, including solar photovoltaic, wind turbines, and the diesel plants currently used. Subsequently, the total cost and the CO2 emissions of the energy system were minimized all while considering design, operational, and efficiency constraints. The CO2 emissions and the total present value of the alternatives of energy, together with social, technological, economic, and environmental factors assessed through expert opinions were used as input in the fuzzy multi-criteria decision phase, where a novel fuzzy logic model was employed for selecting the most sustainable alternative. The results indicate that the current diesel capacity in San Andrés Island prevails; with a maximum participation of 3% and 4% of solar and wind systems, respectively.

FMADM: YAGER MODEL IN FUZZY DECISION MAKING

This paper presents Yager model, i.e. standard form of Fuzzy Multi-Atributte Decision Making (FMADM) in fuzzy decision environment. Simulasion of this model would be performed under scope of fuzzy decisionmaking process to show its existence. As academics, researchers, and practitioners know on it, besides theFMADM, so is there Fuzzy Multi-Objective Decision Making (FMODM) at where the both has their same derivation, e.i. Fuzzy Multi-Criteria Decision Making (FMCDM). Related to the matter, significant value that could be represented then gives contribution to team work-oriented principal of decision makers.Keywords: Yager Model, FMADM, FMODM, FMCDM, fuzzy decision-making.

Research on coordinated control of electronic stability program and active suspension system based on function allocation and multi-objective fuzzy decision

The stabilities of the handling and rollover are the two important performance of the vehicle and play an important role in vehicle safe driving. Focusing on improving the handling and rollover stabilities, a new approach to realize the coordinated control of the electronic stability program and the active suspension system is proposed. The vehicle model including the active suspension system has been built. The distance between vehicle centroid and the front and rear axles is estimated by the forgetting factor recursion least squares method on the basis of the vertical motion of the vehicle. The parameter self-tuning fuzzy proportional–integral–derivative control of the electronic stability program is adopted and the 2-degree-of-freedom vehicle model considering the changes of the distance between vehicle centroid and the front and rear axles is treated as the reference model. The active suspension system controller is designed according to the different functions of the active suspension system in different vehicle status areas. The function allocation controller is also designed using multi-objective fuzzy decision, which is used to realize the allocation control of the active suspension system and electronic stability program. Under the double-lane change conditions, the function allocation control has been simulated based on MATLAB/Simulink software, which results indicate that the function allocation control strategy of electronic stability program and active suspension system can significantly improve the manipulation and rollover stability of the vehicle at a high speed under emergency steering. Finally, the function allocation controller is installed to the active suspension system and electronic stability program hardware-in-loop test platform, and the hardware-in-loop test has been done, which results are consistent with the results of simulation.