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In this study, Interval number and Fuzzy number Linear Programming problems are addressed. We deal with some convenient methods to solve the addressed problems. We focus on preparing an improving approach based on Tang Shaocheng method to solve linear programming problems with interval or fuzzy numbers. Also, some applications of our proposed approach in fractional programming are presented. Particularly, we propose an algorithm using our improved approach to solve a fuzzy multi-objective linear fractional programming problem. Finally, we illustrate the proposed approach using numerical and some real life examples. [ABSTRACT FROM AUTHOR]

One of the special cases of type-2 fuzzy sets is the interval type-2 fuzzy sets, which are less complicated and easier to understand than T2FSs. In this study, we explore the interval type-2 fuzzy linear programming problem with the resources vector that has imprecision of the vagueness type. These types of vagueness are expressed via membership functions. First, we review the three available methods, including the Figueroa and Sarani methods. Then, using the three ideas of Verdegay, Werners, and Guu and Wu for solving fuzzy linear programming problems with vagueness in the resources vector, we propose three new methods for solving interval type-2 fuzzy linear programming problems with vagueness in the resources vector. Finally, we demonstrate the effectiveness of our proposed methods by solving an example and comparing the results obtained with each other and with those of previous methods. [ABSTRACT FROM AUTHOR]

This research addresses the Inventory-Routing Problem (IRP) with priority customers under conditions of uncertainty. The problem involves devising a plan to supply a set of customers from a facility, establishing precedence rules, and making transportation and inventory management decisions while considering uncertainty in customer demand. Priority in deliveries is assigned to customers in the health sector in accordance with government regulations. Companies must uphold product quality for health sector customers while simultaneously aiming to minimize total costs. We propose a fuzzy mixed-integer linear programming model to solve the IRP with priority customers and uncertainty in demand rates, modeled using triangular fuzzy numbers. To convert the fuzzy linear programming model into a deterministic one, an ordering method for fuzzy numbers is applied. The model is implemented using CPLEX, and a set of benchmark instances is solved. The primary results indicate that prioritizing customers leads to an increase in total costs due to visiting these customers first on each route created. Additionally, routes without priority customers exhibit higher average vehicle capacity utilization. In most cases, the solutions obtained maintain quality within the specified time limits. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Turkish Operations Management (JTOM) is the property of Ankara Yildirim Beyazit University Journal of Turkish Operations Management (JTOM) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are several approaches to address fuzzy linear programming problems (FLPP). However, due to using standard interval arithmetic (SIA), these methods have some limitations and are not complete solutions. This article establishes a new approach to fuzzy linear programming via the theory of horizontal membership functions and the multidimensional relative-distance-measure fuzzy interval arithmetic. Furthermore, we propose a multidimensional solution based on the primal Simplex approach that satisfies any equivalent form of FLPP. The new solutions of FLPP are also compared with the results of existing methods. Some numerical examples have been illustrated to show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

An alternative new method for solving an epistemic uncertainty such as fuzzy-based linear programming problem with equality constraints has been proposed here. In this approach, the optimal solution of the considered fuzzy problem has been achieved by solving a related crisp problem. Moreover, the objective function of the crisp problem has been obtained by taking the linear combination of the fuzzified version of the original uncertain objective function. The coefficients of the linear combination are considered as strictly positive real numbers. And the constraints of the crisp problem are obtained equivalently from the equality constraints of the fuzzy problem. Triangular fuzzy numbers are considered for this analysis. Comparisons have been made with the results of Kumar et al. (Appl Math Model 35:817–823, 2011), Najafi and Edalatpanah (Appl Math Model 37:7865–7867, 2013), Ezzati et al. (Appl Math Model 39:3183-3193, 2015) and Ghoushchi et al. (Mathematics 9:2937, 2021. https://doi.org/10.3390/math9222937). [ABSTRACT FROM AUTHOR]

In this work, we propose a 3D dynamic optimization model that enables the design of an underground mine ore pass system with uncertainties. Ore transportation costs and ore pass development costs are quantified by triangular fuzzy numbers. Transportation costs are treated as production costs, and they vary over the duration of mining operation, while development costs of ore passes are treated as an investment, and they are treated as constant. The developed model belongs to the class of fuzzy 0–1 linear programming models, where the fuzzy objective cost function achieves a minimum value, with respect to given set of techno-dynamic constraints. Searching for optimal value in the fuzzy environment is a hard task, and because of that, we developed a new ranking function which transforms the fuzzy optimization model into a crisp one. A triangular fuzzy number can be presented as a triangular graph G(V,E) composed of vertices and edges. The x-coordinate of the Torricelli point of a triangular graph presents the crisp value of a triangular fuzzy number. The use of this model lets us know the optimal number of ore passes, optimal location of ore passes, and optimal dynamic ore transportation plan. [ABSTRACT FROM AUTHOR]

The necessarily optimal solution is known as the most reasonable solution to linear programming problems with interval/fuzzy objective function coefficients. As it remains optimal against the certain fluctuations of objective function coefficients, the necessarily optimal solution can be seen as a robust optimal solution. In this paper, we demonstrate that the necessary optimality degree of a non-degenerate basic feasible solution can be obtained easily by utilizing the tolerance approach. The necessary optimality degree evaluates to what extent the solution remains optimal against the fluctuations of objective function coefficients. Several types of fuzzy subsets showing the possible range of the objective function coefficient vector are considered. For each type of fuzzy subset, an efficient calculation method of necessary optimality degree is proposed. Numerical examples are given to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, new concepts of (ˆ-feasibility and ã-efficiency of solutions for fuzzy mathematical programming problems are used, where ã is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted much interest. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts to a Multi-Objective Linear Programming (MOLP) problem, and then in phase II a weighting technique for the reduced program is introduced. We saw that it was observed that using this concept as a generalization of the parametric approach in linear programming provides a more appropriate tool for modeling real problems and improving the solving process. Thus, by using this two-phase approach, we achieve better utilization of available resources. Further, the solution resulting from these two approaches is always an ã-efficient solution. Finally, an example in the real world is described to express this approach. [ABSTRACT FROM AUTHOR]

Demand forecasting is of crucial importance for make-to-stock companies because product demand is uncertain and it changes with time. Fuzzy linear programming (FLP) can be an optimum approach for such uncertain situations. In this study, the FLP model was used for more accurate demand forecasting in a make-to-stock company. Demand forecasting study was carried out according to the FLP method and linear programming (LP) method. Solutions of FLP and LP were compared in terms of imputed shortage cost, inventory carrying cost, and net profit. Results show that the applied FLP method is more advantageous than LP as it provides a 67% decrease in costs and a 15% increase in net profit. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging technique that realizes the image reconstruction of conductivity distribution in the field. The existing EIT algorithms ignore the hidden fuzzy features during imaging, making the EIT technique exhibit a high degree of uncertainty, imprecision, incompleteness, and inconsistency in the actual use process, resulting in a low spatial resolution of the reconstructed images. In order to solve this problem, we introduce fuzzy linear programming into EIT imaging. On the basis of analyzing the fuzzy features of EIT in detail, a new model of fuzzy optimization is built up, whose optimal solution is obtained by using constrained coefficient fuzzy linear programming. To this end, we devise two types of simulation experiments to verify the performance of the optimization algorithm. Experimental results prove that compared with the traditional Tikhonov regularization algorithm, the correlation coefficient of the reconstructed image of the proposed algorithm is higher, the relative error value is smaller. [ABSTRACT FROM AUTHOR]

When some coefficients of the constraints are uncertain with only their possible ranges being given, a conventional linear programming (LP) problem can be generalised to the one with set-inclusive constraints. We consider the case where the possible ranges are given by fuzzy sets in this paper. The set-inclusive constraints with fuzzy coefficients have been treated by a necessity measure. However, the usual necessity measure cannot express well the decision-maker's requirement about the trade-off between the robustness level and the satisfaction level of the constraints. We extend the necessity measure to incorporate the trade-off between the robustness level and the satisfaction level of the constraints. We apply the extended necessity measure to LP problems with fuzzy coefficients. After the problem is formulated and reduced to the conventional programming problem, we propose a solution algorithm. Numerical examples are given to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

One of the various optimization methods that addresses optimization under uncertainty is fuzzy linear programming. This model can be used when there is ambiguity in the situation because it is not precisely specified or when the problem does not require an exact value. With fuzzy linear programming, there is a range of grey between the two extremes as opposed to binary models, where an event may only be either black or white. As a result, it broadens the range of potential applications because most scenarios involve a spectrum of values rather than a bipolar state. In this article, a new FLP-based method is developed using a single MF, called modified logistics MF. The modified MF logistics and its modifications taking into account the characteristics of the parameter are from the analysis process. This MF was tested for useful performance by modeling using FLP. The developed version of FLP provides confidence in the existing IPPP application. This approach to resolving the IPPP can get feedback from the decision maker, the implementer and the analyst. In this case, this process can be called FLP interaction. FS self-assembly for MPS problems can be developed to find satisfactory solutions. The decision maker, researcher and practitioner can apply their knowledge and experience to get the best results. [ABSTRACT FROM AUTHOR]

A selection of suitable sustainability management systems (SMS) is a major part of supply chain strategies to create a competitive advantage, reduce total costs, and manage long-term sustainability. A framework and method for prioritizing supply chain SMSs are presented in this research. Analytic hierarchy process (AHP) is the most common method for alternative selection in multi-criteria decision-making (MCDM). However, complex information is mixed with ambiguity and uncertainty, which makes decision makers unable to use precise or crisp numbers, so fuzzy numbers are presented to remedy this difficulty. Therefore, this research proposes a fuzzy additive preference programming (FAPP) to select the optimum SMS for a supply chain. FAPP method can produce the unique normalized optimal priority vector of fuzzy pairwise comparison matrices for SMS selection effectively with linear constraints. The additive linear constraints can eliminate the weaknesses of existing methods and equalize the upper and lower triangular fuzzy judgments. In addition, the proposed method can identify abnormal pairs of fuzzy judgments that cause inconsistency. The proposed methodology can prioritize the key criteria which lead to the selection of the most appropriate SMS. An example of SMS selection in a rubber factory demonstrates the feasibility and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, the intensification of a competitive environment in markets in conjunction with sustainability issues has forced organizations to concentrate on designing sustainable closed-loop supply chains. In this study, a sustainable closed-loop supply chain network is configured under uncertain conditions based on fuzzy theory. The proposed network is a multi-product multi-period problem which is formulated by a bi-objective mixed-integer linear programming model with fuzzy demand and return rate. The objectives are to maximize the supply chain profit and customer satisfaction at the same time. Moreover, the carbon footprint is included in the first objective function in terms of cost (tax) to affect the total profit and treat the environmental aspect. Fuzzy linear programming and Lp-metric method are then applied to deal with the uncertainty and bi-objectiveness of the model, respectively. In order to validate the methodology, a case study problem in the dairy industry is investigated where the proposed Lp-metric is also compared to goal attainment method. The obtained results demonstrate the superiority of Lp-metric against goal attainment method as well as the applicability and efficiency of the proposed methodology to treat a real case study problem. Furthermore, from the management perspective, outsourcing the production during high-demand periods is highly recommended as an efficient solution. [ABSTRACT FROM AUTHOR]

Resource-Constrained Multi-Project Scheduling Problem (RCMPSP) is considered a significant topic in project management studies and many types of research have been carried out in this field in which numerous approaches have been proposed so far. However, in most of these approaches, the viewpoints of clients and contractors, i.e., two important stakeholders of the project, are not directly considered. The current research attempts to introduce a new approach, namely RCMPSP, to schedule the project portfolio and allocate the budget as a limited resource simultaneously. In doing so, first, clients and contractors' budget satisfaction is defined. Then, a number of budget allocation models have been proposed to maximize the satisfaction of clients and contractors. These models consider such constraints as minimum cost required for each project, maximum budget for each period, and exibility of the start date of each project. To illustrate the viability of the proposed models, a real case of the project portfolio is considered. [ABSTRACT FROM AUTHOR]

In this study, we first show that the existing arithmetic operations of trapezoidal fuzzy numbers do not satisfy the basic properties. Then, for trapezoidal fuzzy numbers, we define new arithmetic operations. Furthermore, we demonstrate that the existing Simplex method for addressing fully fuzzy linear programming problems has some drawbacks. Finally, we provide a new strategy for solving fully fuzzy linear programming problems and compare our results with the existing methods. [ABSTRACT FROM AUTHOR]

Network planning is an important design-step wherein various network resources are provisioned at different network locations. Traffic demand for all node pairs in the network is an important determining factor for network planning, and typically, a static traffic demand matrix consisting of long-term average traffic values for all node pairs is employed. However, assessment of traffic demand matrix may not be accurate (precise). In this paper, we propose an optimization framework to minimize traffic congestion in a wavelength-routed wavelength-division multiplexing WDM optical backbone network using such approximate traffic demands. Modeling such a network is constrained by limited network resources, and can be formulated in general as a fuzzy optimization problem. Moreover, decision maker might want to relax traffic congestion goal by a small margin in presence of approximate traffic demand matrix, so as to reach some aspiration level, rather missing the crisp target by a very small amount. We use the network planning model to minimize traffic congestion so as to achieve an aspiration level of traffic congestion. We use Zimmermann's fuzzy programming technique to explore mixed integer linear programing based optimization model incorporating fuzzified constraints and objective function. All related constraints, such as traffic and lightpath routing, wavelength selection, restriction due to average propagation delay, lightpath degree and maximum hop count constraints are taken into consideration. Following the proposed model, under fuzzy environment, we implement reliable and efficient network planning and traffic provisioning, and estimate traffic congestion more accurately. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model. [ABSTRACT FROM AUTHOR]

In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cells in a cellular manufacturing system (CMS) where several automated guided vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This article investigates the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and intercellular movements of parts. Due to the NP-hardness of the problem, a hybrid genetic algorithm (GA/heuristic) and a whale optimization algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem. [ABSTRACT FROM AUTHOR]

In this manuscript, three contributions are proposed. First contribution is proposing a good evaluation between the fuzzy and neutrosophic approaches using a novel fuzzy-neutrosophic transfer. Second contribution is introducing a general framework for solving the neutrosphic linear programming problems using the advantages of the method of Abdel-Basset et al. and the advantages of Singh et al.'s method. Third contribution is proposing a new neutrosophic exterior point simplex algorithm NEPSA and its fuzzy version FEPSA. NEPSA has two paths to get optimal solutions. One path consists of basic not feasible solutions but the other path is feasible. Finally, the numerical examples and results analysis show that NEPSA more than accurate FEPSA. [ABSTRACT FROM AUTHOR]

Copyright of Al-Dananeer is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental concerns are on the rise around the world, as carbon emissions are increasing rapidly and which is one of the leading causes of global warming. These emissions come from different sectors but the biggest contribution comes from the transport sector. As countermeasures, countries are shifting toward electric vehicles, which are much greener modes of transportation. Currently, different types of electric vehicles are available in the market. The selection of a viable type of electric vehicle for a developing country like Pakistan needs proper evaluation. In this study, two techniques are used. Firstly, fuzzy Strength, Weaknesses, Opportunities, and Threats (SWOT) approach is used for the selection of a suitable vehicle for the future market of Pakistan. Secondly, fuzzy linear programming is used to check the viability of electric vehicles in the current market scenario. Using these techniques, hydrogen fuel cell vehicle is selected as the most advantageous vehicle for Pakistan's future market and the hybrid electric vehicle comes out as the best alternative for the current market. The techniques and the case study of Pakistan are the main novelty of this study, as no literature exists on electric vehicles with these methods on Pakistan's perspective. Based on the results, different recommendations are given to the policymakers of Pakistan. The recommended policies could make electric vehicles a prime competitor to the combustion vehicles in the current and future markets. [ABSTRACT FROM AUTHOR]

Recently, Ghanbari et al. (IEEE Transactions on Fuzzy Systems 27:1286–1294, 2019) have proposed modified Kerre's method for comparison of LR fuzzy numbers. Here, we make use of the modified Kerre's method to solve fuzzy linear programming problems with LR coefficients. In an approach to solve a fuzzy linear program with fuzzy LR coefficients, a bi-objective optimization problem is formulated. For the associated bi-objective optimization problem, we present a time variant multi-objective particle swarm optimization (TV-MOPSO) algorithm to compute the Pareto front, a set containing a large number of solutions. Contrary to methods that change the fuzzy optimization problem to a crisp problem by use of a ranking function, using modified Kerre's method, the fuzzy optimization problem is solved directly, with no need for changing it to a crisp program. A comparative investigation using illustrative examples with triangular fuzzy coefficients show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Minimum-cost capacitated fuzzy network is formulated as a fuzzy linear programming problem. A novel fuzzy linear programming formulation for minimum-cost capacitated fuzzy network where the total resource constraints are fuzzy is proposed. The proposed model is then implemented to minimize the operations cost of American Airlines. The research is helpful to identify the most profitable destinations for the American Airlines. Twelve origin/destination pairs are taken into considerations namely Atlantic (A), Latin American (L), Pacific (P) and Domestic (D). Flight operations capacity, Available Seat Miles ASM, is taken as a measure of capacity. The goal is to minimize the flight operations cost while ensuring maximum flight operations capacities to all destinations. This is followed by perspective data analytics for "What American Airline should do to be more profitable?" Perspective analytics suggest the airline to extend flight operations capacity in certain origin/destination pairs, whereas to maintain the previous approximate average capacity for those having high operations costs. The solution of the proposed fuzzy model suggests that flight operations capacity ASM can be significantly increased by 22345148 (000) with relatively small increase 1539356 (000) USD in operations cost. The fuzzy model is superior for it emphasizes to increase flight operations capacity ASM for the origin/destination pairs with minimum flight operations costs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Baghdad College of Economic Sciences University is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, a multi-element wind turbine blade that consists of NACA 6411 and NACA 4412 leading-edge slat design is investigated computationally. Optimum design parameters of the slatted wind turbine blade leading to maximum value of CL/CD related to the turbine power are obtained. In the optimization process, a new fuzzy logic linear programming methodology integrating with fuzzy linear regression and 2D CFD analysis is proposed. The aerodynamic characteristics of the slatted blade are computed by using Incompressible Navier–Stokes equations and k-omega turbulence modeling. Results are compared with the results of linear programming method and direct search optimization method. The computational results reveal that the proposed methodology for performance optimization is more effective than other methods to obtain high-performance value of the CL/CD. The maximum value of the CL/CD is obtained as 25.1 leading the maximum efficiency of 0.52. [ABSTRACT FROM AUTHOR]

We investigate various types of fuzzy linear programming problems based on models and solution methods. First, we review fuzzy linear programming problems with fuzzy decision variables and fuzzy linear programming problems with fuzzy parameters (fuzzy numbers in the definition of the objective function or constraints) along with the associated duality results. Then, we review the fully fuzzy linear programming problems with all variables and parameters being allowed to be fuzzy. Most methods used for solving such problems are based on ranking functions, α -cuts, using duality results or penalty functions. In these methods, authors deal with crisp formulations of the fuzzy problems. Recently, some heuristic algorithms have also been proposed. In these methods, some authors solve the fuzzy problem directly, while others solve the crisp problems approximately. [ABSTRACT FROM AUTHOR]

The parameters of decision models are not clearly known and they are not exact for most real-life problems. Therefore, this article presents a model of linear programming with grey interval numbers for right-hand-side parameters under an uncertainty case. The purpose of this article is to compare the results of a fuzzy linear programming model and those of four proposed grey linear programming models and to find effective solutions for alternative production planning. The results produced a more effective plan to use as an industrial solution according to a fuzzy linear programming model. The objective function value is about 3% more effective than with the fuzzy linear programming method, and the customer satisfaction of total demand is about 25% more effective for the number of served customers. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of determining the optimal production quantity under uncertainties. The treated problem has a critical effect on the achievement of business goals, which further propagates to the competitive advantage of manufacturing company. The considered problem is stated as the fuzzy linear programming task. The objective function is defined as the maximization of all profit over the time period. The set of constraints consists of constraints deriving from the enterprise (available capacities) and from the market (demand). The fuzzy rating coefficient values of objective function of each pair of the considered products are described by linguistic expressions which are modelled by triangular fuzzy numbers. Handling of uncertainties of the stated fuzzy pair-wise comparison matrix is performed by using the extent analysis. The right-hand-side values of the capacity constraints are determined by decision makers and modelled by the triangular fuzzy numbers. The market constraint values are given according to evidence data. Determination of the optimal quantities of considered products for each time period is based on the concept of equal possibilities. The proposed model is illustrated by an example with real-life data. [ABSTRACT FROM AUTHOR]

Copyright of DSI Technical Bulletin / DSİ Teknik Bülteni is the property of DSI Technical Bulletin / DSI Teknik Bulteni and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although assembly lines are designed to be highly efficient and very cost effective, they have been criticised for their effects on workers. Assembly workers usually perform repetitive tasks and sit or stand for long periods of time, therefore they are at high risk of work-related injuries such as musculoskeletal disorders. In this study, we present a new assembly line balancing problem that incorporates not only task precedence and processing times but also physical workloads in order to reduce work-related injuries. We consider the physical workload of a task as a fuzzy concept and propose a fuzzy linear programming model. The proposed model is applied to an assembly line balancing problem in a textile company and to some of the benchmark problems in order to illustrate the effects of physical workloads. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the Faculty of Economics & Business in Zagreb / Zbornik Ekonomskog Fakulteta u Zagrebu is the property of Ekonomski Fakultet u Zagrebu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we focus on a polynomial order even penalty function for solving a fuzzy linear programming problem and develop an algorithm which gives a better rate of convergence to achieve the optimal solution to the problem in hand. Some numerical examples are included to exhibit the efficiency of the new algorithmic procedure developed by us. [ABSTRACT FROM AUTHOR]

In this article, we consider some well-known approaches for solving fuzzy linear programming (FLP) problems. We present some of the difficulties of these approaches. Then, crisp linear programming problems are suggested for solving FLP problems. A new algorithm is also given. The proposed approach has advantages over the other methods. For example, we can achieve higher membership degrees for objective function and constraints. Moreover, we show that the fuzzy optimal solutions obtained by the proposed approach are efficient enough. Also, we see that unlike the previous methods, our method finds efficient solutions by solving only one crisp linear problem instead of solving two or three crisp problems. Finally some numerical examples are presented to show the efficiency of the given approach over the other approaches. [ABSTRACT FROM AUTHOR]

The growing awareness about the adverse effects of fossil fuels and the ensuing pollution has encouraged and promoted the use of renewable energies throughout the world. This in turn has encouraged governments and investors to show more interest in generation of renewable energy through construction of facilities such as wind farms. The novelty of this research work is the budget constraints consideration for the first time in wind energy projects. This work is done for the Khorasan provinces of Iran. This study is based on regional and municipal data pertaining to the year 2016, and uses the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to deal with the uncertainty of the data. The fuzzy TOPSIS method is used to evaluate and rank the candidate cities, and the results are then used to develop a budget-constrained model by fuzzy linear programming with the Maxmin objective function. The resulting mathematical model is optimised with GAMS software. The modelling results show that in the presence of budget constraints, of 10 Khorasan cities considered as candidate locations, Afriz, Rudab, and Fadashk exhibit the best conditions for the construction of wind farms. [ABSTRACT FROM AUTHOR]

Fuzzy linear regression analysis helps to produce successful assumptions in situations with uncertainty between variables and provides researchers with a flexible perspective. In this study, 305 days milk yield estimation studies were carried out using partial lactation records of Jersey cattle with the fuzzy linear regression method. Calving age, number of lactation, days of milk, calving season, and the first four milk test days records were used as the independent variables in the study. Also,305 days milk yield was used as the dependent variable. Reliability of the obtained estimates was discussed using graphical representations of h values and three different statistical error criteria (RMSE, MAPE and R). In addition, the estimated values obtained were compared with the observed values. The fuzzy linear regression equations developed for 10 different h values shows that the equations obtained for the h = 0.4 and h = 0.5 values are the closest observed values to305 days milk yield. The difference between the predicted values and the observed values was statistically insignificant (p>0.05). These results show that the fuzzy linear regression method can be successfully used to predict 305 days milk yield at the beginning of the lactation. [ABSTRACT FROM AUTHOR]

An innovative solution to provide a demand response for power system is proposed in this paper by considering the feasibility of two-way direct load control (TWDLC) of central air conditioning chiller system for wide-area in real-time manner. Particularly, the proposed TWDLC scheme is designed to tackle the load shedding ratio optimization problem for all under-controlled customers, aiming to satisfy the target load curtailment defined in each scheduling step. Another notable contribution of this work is the utilization of constraint loosening concept on actual load, curtailed to overcome the uncertainties of load reduction during TWDLC. Given the presence of fuzzy constraints, the proposed load shedding ratio optimization problem can be tackled using fuzzy linear programming. A delicate strategy is then formulated to transform the proposed fuzzy linear programming problem into a regular linear programming problem. A selection scheme used to obtain the feasible candidates set for load shedding at every sampling interval of TWDLC is also designed along with the fuzzy linear programming. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mansour Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The real life transportation problems (TP) face a lot of problems due to uncertainties and lack of precise data. The present paper focuses on the two methods for solving intuitionistic fuzzy TP. One of the methods uses intuitionistic fuzzy programming technique together with the three different membership functions-linear, exponential and hyperbolic and the other method uses crisp linear programming taking intuitionistic fuzzy data in both the methods for the cost objective functions in the TP. The first method uses the membership and non-membership degrees separately to find the crisp solution using the fuzzy programming technique and then the optimal solution is calculated in terms of intuitionistic fuzzy data with the help of defined cost membership functions using the different membership functions. The satisfaction degree is then calculated to check the better solution. The second method directly solves the TP to find crisp solution considering a single objective function. The cost objective function is taken as intuitionistic fuzzy data and the methods have been used as such for the first time. A large scale real life intuitionistic TP has been solved using the two methods. The results obtained for different membership functions have been compared. [ABSTRACT FROM AUTHOR]

Energy and energy management is one of the most important subjects which concerns all the countries around the world. As fossil fuels are depleted, attention toward other energy sources increases. Renewable energy sources have become one of the popular topics in this field. Although these sources are renewable by nature, several difficulties are faced when countries attempt to implement these systems. One of these problems is the changing efficiency of renewable energy sources due to availability and quality of the sources such as sunlight intensity, wind speed, and so on. This study considers various renewable energy sources and different needs in a country. Next, a linear programming model is used to optimize the problem in deterministic form. After that, because efficiency of RES is not deterministic, in this article, we use fuzzy linear programming to handle these difficulties. To achieve this, we transform our linear model to fuzzy linear programming using theorems from literature. In this study, we used six RES plus nuclear energy to meet different electricity demand. In the end to test our FLP model, a case study in Iran, which bifurcates this country to east and west sections, each with different energy potentials, is done and the results are presented. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 953-966, 2017 [ABSTRACT FROM AUTHOR]

A multi item profit maximization inventory model is developed in fuzzy stochastic environment. Demand is taken as Stock dependent demand. Available storage space is assumed to be imprecise and vague in nature. Impreciseness has been expressed by linear membership function. Purchasing cost and investment constraint are considered to be random and their randomness is expressed by normal distribution. The model has been formulated as a fuzzy stochastic programming problem and reduced to corresponding equivalent fuzzy linear programming problem. The model has been solved by using fuzzy linear programming technique and illustrated numerically. [ABSTRACT FROM AUTHOR]

Short-term production plans are the basis for operational mine production schedules. They concentrate on making long-term mine plans operationally feasible. It ensures a steady flow of product for meeting blending targets. Due to the quality variation of material, blend optimization is an uncertainty based optimization problem. There are different approaches toward uncertainty management and the current paper investigates a portfolio optimization model in order to minimize the risks in short-term plans. In this paper, a fuzzy linear programming model is formulated to provide a set of options for the mine plan. These blending options are treated as portfolios. Then a model for the optimal selection of a portfolio is introduced. The objective of the model is to maximize the expected return of the portfolio under constraints limiting its variance. The model is applied in a limestone mine complex. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the altitude control algorithm for quadrotor to be able to fly at a particular altitude. Several previous researchers have conducted studies on quadrotor altitude by using PID control but there are problems in the overshoot and oscillation. To optimize the control, tunning on PID algorithm must be first conducted to determine proportional and derivative constants. Hence, the paper presents altitude control modification by using PID-like fuzzy without tuning. The PID algorithm is a control algorithm for linear systems. While, system to be controlled is a non-linear, so that linearization is needed by using equilibrium. The proposed algorithm is a modification of the PID algorithm used as an altitude control which enables quadrotor to be stable when hovering. The algorithm used is not PID algorithm with tuning using fuzzy, but this is a single input single output (SISO) control PID-like fuzzy linear programming. The result of the research shows that quadrotor can hover in a rapid raise time, steady state and settling time without performing overshoot and oscillation. [ABSTRACT FROM AUTHOR]

Climate change exposes economic systems to numerous risks, including reduced agricultural production and electric power supply shortages. The interdependent nature of economic systems causes disruptions in any sector to cascade to other sectors via forward and backward linkages. This work develops an optimization model with which allocation of scarce goods or resources can be optimized; the model uses an overall index of satisfaction of fuzzy economic output goals under conditions of scarcity caused by climatic disruptions. The proposed model includes a vulnerability measure that integrates information elicited from expert judgment. A case study based on a scenario of drought-induced electricity shortage in the Philippine economy is examined. Results show that trade, transportation and service-oriented industries suffer losses in gross domestic product in the Philippine case. [ABSTRACT FROM AUTHOR]

Transportation problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at destinations using available supplies at origins in a crisp environment. In real life situations, the decision maker may not be sure about the precise values of the coefficients belonging to the transportation problem. The aim of this paper is to introduce a formulation of TP involving interval-valued trapezoidal fuzzy numbers for the transportation costs and values of supplies and demands. We propose a fuzzy linear programming approach for solving interval-valued trapezoidal fuzzy numbers transportation problem based on comparison of interval-valued fuzzy numbers by the help of signed distance ranking. To illustrate the proposed approach an application example is solved. It is demonstrated that study of interval-valued trapezoidal fuzzy numbers transportation problem gives rise to the same expected results as those obtained for TP with trapezoidal fuzzy numbers. [ABSTRACT FROM AUTHOR]

The management of the electrical and electronic waste (WEEE) problem in the uncertain environment has a critical effect on the economy and environmental protection of each region. The considered problem can be stated as a fuzzy non-convex optimization problem with linear objective function and a set of linear and non-linear constraints. The original problem is reformulated by using linear relaxation into a fuzzy linear programming problem. The fuzzy rating of collecting point capacities and fix costs of recycling centers are modeled by triangular fuzzy numbers. The optimal solution of the reformulation model is found by using optimality concept. The proposed model is verified through an illustrative example with real-life data. The obtained results represent an input for future research which should include a good benchmark base for tested reverse logistic chains and their continuous improvement. [ABSTRACT FROM AUTHOR]