Fuzzy Goal Programming Approach Research Articles

Multi objective optimization approach for multi-item inventory control: A case study in leather industry

PT ASA is a leather tanning company. Almost 65% of the company's assets are allocated for the procurement of raw leather, which consists of goat and sheepskin. In addition to being expensive, the availability of raw leather is also very limited. The company faces a trade-off where, on one hand, the raw material is easily decayed, but on the other hand, its availability is extremely limited, and if there is not enough inventory, the production process will be disrupted. In this research, a multi-objective optimization model is developed for controlling the inventory of raw leather using the Fuzzy Goal Programming approach. The objectives to be achieved are to minimize the total inventory cost, maximize the total quantity of raw leather that meets standards, and minimize the total cost of losses due to decayed raw leather. Based on the calculations, the fuzzy goal programming membership function value is obtained at 0.9155, with a total inventory cost over the planning horizon of IDR 10,341,630,000, a total of 1,279,542 sq ft of raw leather meeting standards, and a total loss cost due to decayed raw leather of IDR 142,911,691.

Neutrosophic linear fractional optimization problem using fuzzy goal programming approach

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm.

Optimizing the Cropping Pattern in Nangarhar Province Based on the Perspective of Sustainable Agricultural Development: Fuzzy Goal Programming Approach

Optimizing the Cropping Pattern in Nangarhar Province Based on the Perspective of Sustainable Agricultural Development: Fuzzy Goal Programming Approach

Optimisasi Portofolio Saham Syariah dengan Pendekatan Fuzzy Goal Programming

This study discusses about solving the problem of stock portfolio optimization. The portfolio optimization problem can be modeled as a multiobjective model with two objective functions, they are maximizing return and minimizing risk. This study uses the mean-variance Markowitz model to model the portfolio. The fuzzy goal programming approach is used to solve stock portfolio problems by converting a multi-objective model into a fuzzy mathematical programming model which is solved using a nonlinear programming method. The results of implementing the model on stocks listed on the Jakarta Islamic Index show that the best investment proportion forms an optimal portfolio according to the level of aspirations. Several different optimal portfolios can be formed using the fuzzy goal programming approach. The difference in the results of these portfolios depends on the level of aspiration of investors.

Fuzzy multi-objective deterministic inventory model with time dependent demand and holding cost under Pythagorean fuzzy goal programming approach

Fuzzy multi-objective deterministic inventory model with time dependent demand and holding cost under Pythagorean fuzzy goal programming approach

A Weighted Neutrosophic Fuzzy Goal Programming Approach to Optimise Multi-Objective Assignment Problem

A Weighted Neutrosophic Fuzzy Goal Programming Approach to Optimise Multi-Objective Assignment Problem

Interactive fuzzy goal programming approach for solving possibilistic multi-objective minimum cost flow problems

Interactive fuzzy goal programming approach for solving possibilistic multi-objective minimum cost flow problems

Enhancing neutrosophic fuzzy goal programming approach for solving multi-objective probabilistic linear programming problems

This article considers a stochastic multi-objective linear programming problem in a neutrosophic framework. The right-hand sides of the constraints are random variables associated with their means and variances, and the single-valued trapezoidal neutrosophic numbers are included in the coefficients of the objective functions to investigate the problem. The problem is successfully converted into the related deterministic programming model based on the formulation of the scoring function and several distributions, such as the Uniform, Exponential, and Gama distributions. Applying fuzzy goal programming, the deterministic problem is solved. An example is then solved to demonstrate the solution methodology.

Interactive Fuzzy Goal Programming Approach for Solving Possibilistic Multi-Objective Minimum Cost Flow Problems

Interactive Fuzzy Goal Programming Approach for Solving Possibilistic Multi-Objective Minimum Cost Flow Problems

A risk-averse sustainable perishable food supply chain considering production and delivery times with real-world application.

In recent years, a relatively novel paradigm known as sustainable development has been introduced in response to concerns regarding the adverse impact of industrial activities on the environment and society. Managers in the food sector have been attempting to incorporate the principle of sustainable development in their supply chains owing to the paramount importance of social and environmental considerations in creating a competitive advantage for food products. To this end, we propose a multi-objective linear mathematical model considering the three dimensions of sustainability, i.e. economic, environmental, and social, to design a sustainable food supply chain. Given today's volatile business environment, we employ a robust optimization model by incorporating Conditional Value-at-Risk into the configuration of two-stage stochastic programming to tackle uncertainty and take up a risk-averse strategy in supply chain design. The model aims to identify the optimal production and delivery times of the products, investigate the effects of their perishability characteristic on inventory decisions, and assess the financial and environmental advantages of transportation decisions to improve the sustainability of logistics operations. A novel version of fuzzy goal programming approach is applied to solve the proposed model. Next, the applicability of the proposed model and its solution method is verified based on computational experiments on a real-world case study of a processed food company. Lastly, conflicts between the sustainability aspects are examined, and several sensitivity analyses on risk-aversion parameters are performed to provide managerial insights for industry executives seeking to optimize their network concerning sustainability issues and well-performance under worst-case scenarios.

Traditional and fuzzy goal programming approaches for resource dependent assembly line balancing problem

In this study, two goal programming models are proposed for balancing resource dependent assembly lines with precise and fuzzy goals in order to provide flexibility for decision makers based on their decision environment and preferred priorities. Three conflicting goals, namely, total number of utilized workstations, cycle time and total cost of additional resources (equipment and assistant workers) are considered. The proposed models are validated on illustrative examples and scenario analyses are performed with different priority levels of the goals. The results show that the proposed goal programming formulations are valid and useful for balancing resource dependent assembly lines.

Measures to promote energy, economic and environmental sustainability for an optimal workforce portfolio between Algerian sectors at the 2030 horizon using fuzzy goal programming approach

Navigating towards a sustainable future, Algeria engages in strategic resource management to fulfill its 2030 vision. This study delineates a Fuzzy Goal Programming (FGP) model, inte-grating economic objectives with ecological and energy sustainability. The framework priori-tizes renewable energy, marking a decisive pivot from traditional hydrocarbon reliance to meet global environmental mandates. It also maps out a strategic redistribution of the workforce, signifying a shift towards a diversified, innovation-led economy that embraces sustainable methodologies. The implementation of the model offers a robust, empirical tool for decision-makers, harmonizing Algeria's specific socio-economic attributes with broader, global sus-tainability trends. The outcome is a blueprint for a robust, renewable-centric economy, posi-tioning Algeria at the forefront of sustainable development and energy reform.

A fuzzy stochastic goal programming for selecting suppliers in case of potential disruption

ABSTRACT Due to globalization and the new characteristics of the business, companies face various challenges to ensure their continuity and competitive advantages. COVID-19 pandemic can be an extreme event that will eventually force many businesses and all industries to redesign and transform their global supply chain model? Challenges concerning mainly reducing the operating cost which is based on selecting the optimal suppliers to provide a reliable product. This study contributes to solving a supplier selection problem under disruption risk due to the lack of literature reviews with a lack of multi-methodological perspective for the fuzzy stochastic notions and quantitative techniques for the quantification of risk alternatives. Prior studies are neglecting to consider the value of risk and prefer to discover chances for optimizing anticipated costs or profits. This study proposed a fuzzy stochastic goal programming approach for selecting the optimal supplier under disruption risk. The proposed model incorporates multiple criteria such as capacity, stochastic demand, and probability of disturbance. The problem of stochastic combinatorial optimization obtained is presented as a program of fuzzy random aim by integrating techniques of value at risk and conditional risk value. Numeric samples and calculation results are included. The results of the models help the decision-maker to optimize the selection of suppliers in the event of a disturbance risk problem by an estimated value at risk and by simultaneously minimizing the conditional value of the risk and demonstrate the efficacy and acceptability of the created risk-averse technique as well as the effects of risk factors on our model behavior.

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

Fuzzy Goal Programming Approach for Identifying Target Unit in Combined-Oriented DEA Models: Application in Bank Industry

Fuzzy Goal Programming Approach for Identifying Target Unit in Combined-Oriented DEA Models: Application in Bank Industry

Designing an integrated blockchain-enabled supply chain network under uncertainty

With the development of communication infrastructure, the design of supply chains has changed significantly. Blockchain technology, as one of the most cutting-edge technologies, can promote transparency among members of the supply chain network. To the best of our knowledge, this is the first study that tries to develop a novel bi-objective optimization model to integrate the transparency resulting from the use of blockchain for designing a three-level supply chain network. The first objective function is to minimize total cost while the second objective function seeks to maximize transparency based on the application of blockchain technology. Moreover, it is worth noting that it is the first attempt to investigate the role of a blockchain model under stochastic conditions. The bi-objectiveness and stochastic nature of the proposed model are then treated using Fuzzy Goal Programming (FGP) and Chance-Constrained programming (CCP) approaches, respectively. To tackle the problem, an improved Branch and Efficiency (B&E) algorithm is developed by incorporating transparency along with cost and service. The impacts of blockchain exclusively through transparency (Case 1) or through transparency, cost, and benefits (Case 2) in Supply Chain Design (SCD) are compared. The results demonstrated that the first case has less computational complexity and better scalability, while the second case has more transparency, less congestion, and more security. As one of the main implications, supply chain managers who are focused on cost minimization as well as transparency maximization are advised to take into account the trade-off between featuring costs and benefits of blockchain technology.

Application of fuzzy goal programming approach in the real-life problem of agriculture sector

Goal: The present study aimed to demonstrate the applicability of the fuzzy goal programming to frame the decision support system for the decision-makers to deal with the real-life problem of the agriculture sector namely the apple cultivation planning problem and to obtain an optimum solution. Design / Methodology / Approach: The proposed method occurred within the apple-producing sector in the Kashmir valley of India and included the collection of data through interviews and surveys with various farmers. Also, the results were drawn with the help of LINGO 18.0. Results: The current finding implies that all of the desired objectives have been met, as well as an optimal solution. The proposed model offers a significant approach for designing plans to determine various agricultural activities in a fuzzy decision environment. Finally, the current study conducts a case study in the apple cultivation sector to obtain various competing objectives. Sensitivity analysis was also performed on its preferential weight parameters. Limitations of the investigation: It should be noted that the parameters related to production cost, transportation cost, and cost of material may change over the years. The selling price may also vary according to the quality of apples and over the years. Because of natural factors, the annual production of apples may also vary. Practical implication: The current study shows that using fuzzy goal programming techniques in an apple production process has a huge potential to increase farmers' income. It can be concluded that the current study can help decision-makers to deal with real-life planning issues in the agricultural sector. Future research can also take advantage of other initiation strategies. Originality/Value: The study looks at the applicability of the fuzzy goal programming paradigm in a new field of apple production in the agriculture sector. According to our knowledge, this is the first time the optimization model has been used in the apple cultivation sector.

Fuzzy Goal Programming Approach for Solving Stochastic Transportation Problem with Additional Budgetary Constraints

Fuzzy Goal Programming Approach for Solving Stochastic Transportation Problem with Additional Budgetary Constraints

Analyzing the Application of the Sustainable Development Goals for Egypt Using a Neutrosophic Goal Programming Approach

Sustainable development necessitates the implementation of appropriate policies that integrate multiple competing objectives on economic, environmental, energy, and social criteria. Multi-criteria decision analysis with goal programming is a popular and widely used technique for studying decision problems with multiple competing objectives. Real-world situations frequently involve imprecise and incomplete information, making neutrosophic goal programming models the most appealing option. We presented a novel neutrosophic goal programming model that incorporates optimal resource allocation to simultaneously satisfy prospective goals on economic development, energy consumption, workforce, and greenhouse gas emission reduction by 2030, as applied to Egypt’s key economic sectors in this paper. We also compared the outcomes of fuzzy goal programming and neutrosophic goal programming. We show that neutrosophic goal programming approach is more accurate than fuzzy goal programming approach because it deals with incomplete and indeterminate information and has three independent degrees: truth membership degree, indeterminacy–membership degree, and falsity–membership degree. The presented model examines opportunities for improvement and the effort required to implement sustainable development plans. The model also provides valuable insights to decision makers for strategic planning as well as investment allocations for sustainable development. Numerical illustration is also provided for validation and application of the proposed model.

Fuzzy goal programming approach to solve fully fuzzy multi-objective quadratic programming problem

Fuzzy goal programming approach to solve fully fuzzy multi-objective quadratic programming problem