Fuzzy Interactive Approach Research Articles

Two-Stage Fuzzy Interactive Multi-Objective Approach under Interval Type-2 Fuzzy Environment with Application to the Remanufacture of Old Clothes

In this study, a two-stage approach is introduced to obtain a more interactive and flexible solution to deal with the multi-objective programming under interval type-2 fuzzy environment. In the first stage, the fuzzy multi-objective chance-constrained programming with regular symmetric triangular interval type-2 fuzzy set parameters is proposed and transferred into its crisp equivalent form. In the second stage, we use the fuzzy interactive approach to address the crisp multi-objective programming obtained in the first stage by introducing the trade-off rate, which helps the decision maker react via updating their reference member values to obtain a satisfactory solution. Finally, taking a remanufacture of old clothes problem as an example, the comparison of experimental results obtained using a non-interactive method and interactive method shows that the proposed approach is conducive to obtaining satisfactory solutions effectively and efficiently, which broadens the application scope of the multi-objective programming with regular symmetric triangular interval type-2 fuzzy set parameters for sustainable manufacturing.

Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon.

Software component evaluation and selection using TOPSIS and fuzzy interactive approach under multiple applications development

In this paper, a two phase approach is proposed for decision-making situation that involves optimal software component evaluation and selection for designing component-based modular software system with multiple applications. In the first phase, components are evaluated using technique for order preference by similarity to ideal solution. In the second phase, a non-linear multi-objective optimization model is developed that facilitates the decision whether “to buy commercial-off-the-shelf components” or “to build in-house components” so that the total score of alternative components is maximized while the overall cost and delivery time of the system are minimized. Many critical parameters such as reliability of various applications, reusability and compatibility of the software components are considered simultaneously in the proposed optimization model. To determine a preferred compromise solution for the multi-objective optimization problem, a fuzzy interactive approach is used. Numerical illustrations based on a small-scale case study are presented to demonstrate usefulness of the proposed optimization model for optimal “build or buy” decisions in real-world applications.

A New Possibilistic Optimization Model for Multiple Criteria Assignment Problem

This paper presents a new multiple criteria optimization model of an assignment problem with imprecise coefficients. Besides, minimizing the total cost, total time of finishing jobs, and maximization of the overall achieved quality, we introduce a new criterion that minimizes the number of workers employed to finish all jobs. It contributes significantly in multi-job assignment to adjust the number of workers assigned to at least one job for balancing work allocation among the workers. Furthermore, we employ new diversification constraints to obtain a reasonable tradeoff between the number of workers employed and number of jobs assigned. A new interactive possibilistic programming approach is developed for trapezoidal possibility distributions, which uses $\alpha$ -level sets to incorporate confidence levels of the decision maker in his fuzzy judgments leading to $\alpha$ -efficient solutions. Numerical experiments are conducted using data coming from a manpower planning problem to demonstrate working of the proposed multiple criteria assignment model and effectiveness of the fuzzy interactive approach.

Credibilistic multi-period portfolio optimization based on scenario tree

In this paper, we consider a multi-period fuzzy portfolio optimization model with considering transaction costs and the possibility of risk-free investment. We formulate a bi-objective mean-VaR portfolio selection model based on the integration of fuzzy credibility theory and scenario tree in order to dealing with the markets uncertainty. The scenario tree is also a proper method for modeling multi-period portfolio problems since the length and continuity of their horizon. We take the return and risk as well cardinality, threshold, class, and liquidity constraints into consideration for further compliance of the model with reality. Then, an interactive dynamic programming method, which is based on a two-phase fuzzy interactive approach, is employed to solve the proposed model. In order to verify the proposed model, we present an empirical application in NYSE under different circumstances. The results show that the consideration of data uncertainty and other real-world assumptions lead to more practical and efficient solutions.

A multi-objective assembly line balancing problem with worker’s skill and qualification considerations in fuzzy environment

In this study a new multi-objective assembly line balancing problem is studied. Objectives like the number of stations, the equipment purchasing cost, the worker time dependent wage, and worker dependent dis-quality level of the stations is to be minimized simultaneously with worker allocation and equipment assignment possibilities. The problem also is formulated in a fuzzy environment. To solve such problem, a new hybrid fuzzy interactive approach is proposed in two stages. In the first stage, the fuzzy formulation is converted to a crisp multi-objective formulation using a credibility-based chance constrained programming approach. Then in the second stage, the obtained crisp multi-objective formulation is solved by a new hybrid fuzzy programming approach. To evaluate the proposed approach, two generated examples and a case study from garment production industries are used for computational experiments. The extensive computational study prove the superiority of the proposed approach over the well-known approaches of the literature.

End-of-life aircraft treatment in the context of sustainable development, lean management, and global business

ABSTRACTDealing with retired aircraft taking into account the environmental, social, and economic impacts is emerging as an air transportation problem for the near future. The players involved in the problem wish to solve this challenge in a systematic way to benefit from the value extracted from the core activities of the end-of-life (EoL) aircraft treatment, decrease the environmental impacts, and at the same time maximize the social value. This paper proposes a holistic approach to EoL aircraft treatment considering lean management, sustainable development, and the global business environment. An integrated optimization framework is proposed to support decision-making at both the strategic and managerial levels. The flexibility of the framework makes it possible to compare different business models, dismantling strategies, and network structures that impact the efficiency, stability, and leanness of the recovery network. A solution methodology based on the joint application of fuzzy interactive approach and genetic algorithm is introduced. The application perspective and guidelines for pilot study are also provided. To the best of our knowledge, this is the first time that recovery of EoL aircraft is considered based on three aspects of sustainability, lean, and global business. Therefore, this study provides many promising perspectives in this context to enrich the literature of EoL aircraft as a new aviation industry challenge and opportunity.

A dynamic fuzzy interactive approach for DG expansion planning

This paper presents a dynamic multi objective model for distribution network expansion, considering the distributed generators (DGs) and network reinforcements. The proposed model simultaneously optimizes three objective functions namely, total cost, emission cost and technical satisfaction (voltage profile) by finding the optimal schemes of timing, sizing, placement and DG technologies in a long term planning period (dynamic planning). The importance of each objective function can be changed in the interactive steps. The calculation algorithm is based on Chaotic Local Search with Modified Honey Bee Mating Optimization (CLSMHBMO). The effectiveness of the proposed model and the calculation method are demonstrated through different studies and comparative analysis on an actual distribution network.

COTS selection using fuzzy interactive approach

In this paper, we introduce fuzzy mathematical programming (FMP) for decision-making related to software creation by selecting optimal commercial-off-the-shelf (COTS) products in a modular software system. Each module in such software systems have different alternatives with variations in their properties, for example, quality, reliability, execution time, size and cost. Due to these variations, component-based software developers generally deals with the problem of selecting appropriate COTS products. The development of COTS-based systems largely depends on the success of the selection process. Various crisp optimization models of COTS products selection have been proposed in literature. However, in real COTS products selection problem, it is difficult to estimate precisely the values of various model parameters due to lack of sufficient data and also because of measurement errors. Hence, instead of crisp optimization model, if we use flexible optimization model then we might obtain results which are more preferred by the decision maker. In this study, we use multiple methodologies such as quality model, analytical hierarchy process and FMP to develop fuzzy multiobjective optimization model of the COTS products selection. To determine a preferred compromise solution for the multiobjective optimization problem, an interactive fuzzy approach is used.

A fuzzy interactive approach for optimal portfolio management

Portfolio management problem is a multiobjective optimization problem in which some or all objectives are conflicting to each other. In real life, practitioners (investors) have an aim to choose his/her optimal portfolio with individual preferences for each objective. In this paper, a fuzzy interactive method is used to solve portfolio management problem. Using this approach, firstly, fuzzy goal is defined for each objective, keeping investor preferences in view, and then these fuzzy goals are aggregated using product operator. After that, the resultant problem is solved using a real coded genetic algorithm. An illustrated example is also provided.