Multiobjective Fuzzy Research Articles

A fuzzy multi-criteria decision-making for optimizing supply chain aggregate production planning based on cost reduction and risk mitigation

In an increasingly complex and uncertain business environment, decision makers require robust strategies for supply chain aggregate production planning that account for the interdependencies and coordination across multiple echelons of the supply chain network. Traditional approaches often inadequately address the uncertainties and risks inherent in supply chain operations, leading to suboptimal performance and increased costs. This study introduces a business model incorporating open innovation dynamics to achieve cost-effectiveness and resilient supply chain performance in the face of uncertainties. A Multi-Objective Fuzzy Linear Programming model was proposed by integrating the Chance-Constraint Programming and Zimmermann’s approach designed to assist decision makers in optimizing the production plan, material flows and resource allocation across the entire supply chain network. The model focuses on both cost and risk minimization based on unsymmetrical triangular fuzzy numbers. Specifically, it targets the downside risk of uncertainty, aiming to reduce the likelihood of negative outcomes or financial losses due to fluctuations, unpredictability, and unforeseen circumstances, which can drive-up supply chain operation costs. The efficacy of the model is demonstrated through a case study. It highlights various imprecise factors such as costs, customer demands, and machine operating hours. The findings underscore the model's capability to provide decision makers with a comprehensive supply chain aggregate production plan that not only optimizes the supply chain network and enhances operational efficiency and reliability but also significantly reduces costs and mitigates unsymmetrical skewness of risks associated with operational uncertainties.

Improved of Multiobjective Fuzzy Probabilistic Solid Transportation Models in Transportation Systems

Transportation activities are stages in the product distribution system. The purpose of the transport system is to minimize the total cost. If there is more than one objective function and consider more than one type of vehicle, it is called a multiobjective solid transportation problem. In some cases, the parameter transportation model is under uncertainty. A probabilistic and fuzzy approach can be used. This research introduces a probabilistic fuzzy multiobjective solid transportation model where the source, destination and vehicle parameters follow the Pareto distribution. A triangular fuzzy number expresses the coefficient of the objective function. The obtained model is applied to the problem of the metal crate transportation system. There are two objective functions; the first is the objective function to minimize the total cost. The second is the objective function to minimize the total transportation time. Three types of vehicles are considered: HDL, Engkel and Wingbox. The result is that the total cost is Rp. 3836595, and the total time is 757.245 minutes or 13 hours.

Multi-Objective Fuzzy Probabilistic Programming Approach for Obtaining Optimum Crops Pattern with Water Replenishment

Multi-Objective Fuzzy Probabilistic Programming Approach for Obtaining Optimum Crops Pattern with Water Replenishment

Using Genetic Algorithms and Core Values of Cooperative Games to Solve Fuzzy Multiobjective Optimization Problems

A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mechanical procedure to avoid this subjective biases. We are going to formulate a cooperative game using the α-level functions of the multiple fuzzy objective functions. Under this setting, the suitable coefficients can be determined mechanically by involving the core values of the cooperative game, which is formulated using the multiple fuzzy objective functions. We shall prove that the optimal solutions of the transformed scalar optimization problem are indeed the nondominated solutions of fuzzy multiobjective optimization problem. Since the core-nondominated solutions will depend on the coefficients that are determined by the core values of cooperative game, there will be a lot of core-nondominated solutions that will also depend on the corresponding coefficients. In order to obtain the best core-nondominated solution, we shall invoke the genetic algorithms by evolving the coefficients.

Analysis of MOFTP using Modern Zero Suffix Method

Abstract: In this paper, a new algorithm is introduced for dealing with the Multi-Objective Fuzzy Transportation Problem (MOFTP) where all parameters such as transportation cost, demand and supply are in triangular fuzzy numbers. The approach involves modern zero suffix method using Harmonic Mean. First the Multi-objective fuzzy transportation problem is converted into a crisp value by using a Robust’s Ranking Method. Then the crisp value is solved by Modern Zero Suffix method. The effectiveness of the data is examined by a numerical example.

A Novel Approach to Solve Multi-objective Fuzzy Stochastic Bilevel Programming Using Genetic Algorithm

A Novel Approach to Solve Multi-objective Fuzzy Stochastic Bilevel Programming Using Genetic Algorithm

Fuzzy Mathematical Approach for Solving Multi-Objective Fuzzy Linear Fractional Programming Problem with Trapezoidal Fuzzy Numbers

Fuzzy Mathematical Approach for Solving Multi-Objective Fuzzy Linear Fractional Programming Problem with Trapezoidal Fuzzy Numbers

New Approach to Solving Fuzzy Multiobjective Linear Fractional Optimization Problems

In this paper, an iterative approach based on the use of fuzzy parametric functions is proposed to find the best preferred optimal solution to a fuzzy multiobjective linear fractional optimization problem. From this approach, the decision-maker imposes tolerance values or termination conditions for each parametric objective function. Indeed, the fuzzy parametric values are computed iteratively, and each fuzzy fractional objective is transformed into a fuzzy non-fractional parametric function using these values of parameters. The core value of fuzzy numbers is used to transform the fuzzy multiobjective non-fractional problem into a deterministic multiobjective non-fractional problem, and the ε-constraint approach is employed to obtain a linear single objective optimization problem. Finally, by setting the value of parameter ε, the Dangtzig simplex method is used to obtain an optimal solution. Therefore, the number of solutions is equal to the number of used values, and the optimal solution is chosen according to the preference of the decision-maker. We have provided a didactic example to highlight the step of our approach and its numerical performances.

Enhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Method

Optimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

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.

Economical heuristics for fully interval integer multi-objective fuzzy and non-fuzzy transportation problems

The single-objective fuzzy or non-fuzzy transportation problem (TP) is not capable of dealing with real-life decision-making problems due to our current competitive market state. In this article, we investigate a fully integer interval multiobjective transportation problem (FIIMOTP) and a fully fuzzy integer multi-objective transportation problem (FFIMOTP). We also provide two solution approaches for solving the FIIMOTP and FFIMOTP. Numerical examples are provided to validate these two approaches. Our results show that the proposed algorithm hugely outperforms the best solution approaches.

Optimization of time based fuzzy multi-objective reliability redundancy allocation problem for [formula omitted] system using tuning and neighborhood based fuzzy MOPSO algorithm

In this paper, a time based fuzzy multi objective reliability redundancy allocation problem (FMORRAP) is proposed for the xj−out−of−m series–parallel system. The main objective is to maximize the system reliability with minimization of system cost and system repairing cost by optimizing the number of redundant components at each stage. The objectives are achieved by satisfying entropy constraints with limited redundant components at each stage and same for the whole system. Here the uncertainty of reliability, cost and repairing cost of each component is maintained by using triangular fuzzy number (TFN). The decreasing factor of component reliability and cost follows the change in the length of radius for the inverse logarithmic spiral with respect to time. Similarly the increasing factor of component repairing cost follows the same for the logarithmic spiral. Tuning and Neighborhood based Fuzzy Multi-Objective Particle Swarm Optimization (TNF-MOPSO) algorithm is proposed to solve fuzzy multi-objective optimization problems (MOOP). The proposed problem and proposed algorithm are illustrated by using a bench mark problem. The proposed algorithm produced better membership of objective functions for most of the time values and high satisfaction level of reliability for all values of the time parameter. It also outperforms the standard approaches MOPSO and NF-MOPSO.

Fuzzy Multi-Objectives Topology Optimization of Slider Pallet in the Picking Machine of Camellia Fruit

Fuzzy Multi-Objectives Topology Optimization of Slider Pallet in the Picking Machine of Camellia Fruit

A Hybrid Model Based on Multi-Attribute Decision Making and Fuzzy Multi-Objective Mathematical Programming for Stock Portfolio Evaluation and Selection Using Z-Number Approach

A Hybrid Model Based on Multi-Attribute Decision Making and Fuzzy Multi-Objective Mathematical Programming for Stock Portfolio Evaluation and Selection Using Z-Number Approach

Presenting a Fuzzy Multiobjective Mathematical Model of the Reverse Logistics Supply Chain Network in the Automotive Industry to Reduce Time and Energy

The current research aims to design a fuzzy multiobjective model of the reverse logistics supply chain network in the automotive industry, taking into account the energy and time reduction approach. The automobile industry is one of the industries with a high demand worldwide. To continue the competition, the manufacturers of the leading car equipment should strive for better product quality by continuously improving their production processes, directing the production of greenhouse gases with low carbon levels, and increasing sustainability. In this regard, reverse supply chain networks and closed-loop chains have unique features that are very useful in the industry under review. The goal is to transform this model into a supply chain of a secure link in the automotive industry. Deterministic methods, genetic algorithm, particle swarm algorithm, and several scenarios with different aspects have been used to solve the model. The results show that the effectiveness of the three ways in terms of solution time is higher in the deterministic solution method. Proper use of the proposed process can help managers effectively manage the flow of recycled products concerning environmental considerations, and this process provides a sustainable competitive advantage for companies.

Integrating Fuzzy Multiobjective Programming and System Dynamics to Develop an Approach for Talent Retention Policy Selection: Case on Health-Care Industry.

The demand for medical services has been increasing yearly in aging countries. Medical institutions must hire a large number of staff members to provide efficient and effective health-care services. Because of high workload and pressure, high turnover rates exist among health-care staff members, especially those in nonurban areas, which are characterized by limited resources and a predominance of elderly people. Turnover in health-care institutions is influenced by complex factors, and high turnover rates result in considerable direct and indirect costs for such institutions (Lo and Tseng 2019). Therefore, health-care institutions must adopt appropriate strategies for talent retention. Because institutions cannot determine the most effective talent retention strategy, many of them simply passively adopt a single human resource (HR) policy and make minor adjustments to the selected policy. In the present study, system dynamics modeling was combined with fuzzy multiobjective programming to develop a method for simulating HR planning systems and evaluating the suitability of different HR policies in an institution. We also considered the external insurance policy to be the parameter for the developed multiobjective decision-making model. The simulation results indicated that reducing the turnover rate of new employees in their trial period is the most effective policy for talent retention. The developed procedure is more efficient, effective, and cheaper than the traditional trial-and-error approaches for HR policy selection.

Multi-Strategy Dynamic Evolution-Based Improved MOEA/D Algorithm for Solving Multi-Objective Fuzzy Flexible Job Shop Scheduling Problem

A scheduling model was developed to optimize the maximum completion time, total machine load, and maximum machine load for the fuzzy flexible job shop problem with uncertain processing times. To solve this problem, a multi-strategy dynamic evolution-based improved multi-objective evolutionary algorithm based on decomposition(IMOEA/D) was proposed. In order to enhance the quality of the non-dominated solution set and improve the algorithm efficiency. The algorithm firstly employs a strategy based on minimum processing time and workload, along with a non-dominated solution prioritization mechanism to generate the initial population. Secondly, three evolutionary strategies are incorporated, and their probabilities are dynamically adjusted with the increase of evolution generations. Finally, a variable neighborhood search method is introduced to improve the search performance of the algorithm. The effectiveness of the proposed algorithm was demonstrated through experimental validation.

Dynamic Resource Allocation Strategy of Multi-Objective Fuzzy Optimization Based on Markov Decision Process

Dynamic Resource Allocation Strategy of Multi-Objective Fuzzy Optimization Based on Markov Decision Process

Multiobjective Fuzzy Random Bimatrix Games Based on Possibility/Necessity Measure

Multiobjective Fuzzy Random Bimatrix Games Based on Possibility/Necessity Measure