Metode Dekomposisi Dan Metode Big-muntuk Menyelesaikan Program Linier Variabel Fuzzy Triangular Studi Kasus: Home Industri Borobudur Furniture, Bogor, Indonesia

Fuzzy Variable Linear Programming (FVLP) with triangular fuzzy variable is part of not fully fuzzy linear programming with decision variables and the right side is a fuzzy number. Solving FVLP with triangular fuzzy variables used Decomposition Methods and Big-M Methods by using Robust Ranking to obtain crisp values. DecompositionMethods of resolving cases maximization and minimization FVLP by dividing the problems into three parts CLP. Solving FVLP with Big-M Methods to directly solve the minimization case FVLP do without confirmation first. The optimal solution fuzzy, crisp optimal solution, optimal objective function fuzzy and crisp optimal objective function generated from Decomposition Methods and Big-M Methods for minimizing case has same solution. Decomposition Methods has a longer process because it divides the problem into three parts CLP and Big-M Methods has a fewer processes but more complicated because the process without divide the problems into three part

System Optimum Fuzzy Traffic Assignment Problem

This paper focuses on converting the system optimum traffic assignment problem (SO-TAP) to system optimum fuzzy traffic assignment problem (SO-FTAP). The SO-TAP aims to minimize the total system travel time on road network between the specified origin and destination points. Link travel time is taken as a linear function of fuzzy link flow; thus each link travel time is constructed as a triangular fuzzy number. The objective function is expressed in terms of link flows and link travel times in a non-linear form while satisfying the flow conservation constraints. The parameters of the problem are path lengths, number of lanes, average speed of a vehicle, vehicle length, clearance, spacing, link capacity and free flow travel time. Considering a road network, the path lengths and number of lanes are taken as crisp numbers. The average speed of a vehicle and vehicle length are imprecise in nature, so these are taken as triangular fuzzy numbers. Since the remaining parameters, that are clearance, spacing, link capacity and free flow travel time are determined by the average speed of a vehicle and vehicle length, they will be triangular fuzzy numbers. Finally, the original SO-TAP is converted to a fuzzy quadratic programming (FQP) problem, and it is solved using an existing approach from literature. A numerical experiment is illustrated.</p

Crisp and fuzzy advanced hierarchhy process for the design of an industrial building based timber and steel elements.

Sustainable Development (SD) emerged in the late 90’s, as a response to severe environmental problems worldwide and the public pressure they created. SD introduced the notion of environmental and social consequences of anthropogenic activity, affecting the paradigm of ’business as usual’ and increasing the complexity of design and implementation of new, environmentally and socially responsible, strategies. Decision making under these new coordinates has to tackle with both quantitative and qualitative information, as well as the relationship between the two. A combination of different knowledge domains, and the different methodological options they introduce, is necessary for tackling complex problems. This research focuses on this (new) challenge, specifically for the construction sector. Two methods, crisp and fuzzy Analytical Hierarchical Process (AHP), are used for the evaluation of the design of a (generic) industrial building. A decision making process is developed, where a problem hierarchy is created, expert knowledge is gathered and evaluated and final priorities of alternative solutions are produced, through a crisp and a fuzzy handling of data. The case study offers a first exploration, indicating the applicability and easy of use of the methods, presenting preliminary results and proposing further research trajectories

A New Dynamic Random Fuzzy DEA Model to Predict Performance of Decision Making Units

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and ‎expected values of the objective functions. In the initial proposed†â€DRF-DEA model, the inputs and outputs are assumed to be ‎characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this ‎assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-‎objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective ‎functions are the expected values of functions of normal random variables. In order to improve in computational time, we then ‎convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives ‎programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic ‎Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results ‎show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of ‎runtime and solution quality. â€

A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms

We study the preventive maintenance scheduling problem of wind farms in the offshore wind energy sector which operates under uncertainty due to the state of the ocean and market demand. We formulate a fuzzy multi-objective non-linear chance-constrained programming model with newly-defined reliability and cost criteria and constraints to obtain satisfying schedules for wind turbine maintenance. To solve the optimization model, a 2-phase solution framework integrating the operational law for fuzzy arithmetic and the non-dominated sorting genetic algorithm II for multi-objective programming is developed. Pareto-optimal solutions of the schedules are obtained to form the trade-offs between the reliability maximization and cost minimization objectives. A numerical example is illustrated to validate the model

Moments and Semi-Moments for fuzzy portfolios selection

The aim of this paper is to consider the moments and the semi-moments (i.e semi-kurtosis) for portfolio selection with fuzzy risk factors (i.e. trapezoidal risk factors). In order to measure the leptokurtocity of fuzzy portfolio return, notions of moments (i.e. Kurtosis) kurtosis and semi-moments(i.e. Semi-kurtosis) for fuzzy port- folios are originally introduced in this paper, and their mathematical properties are studied. As an extension of the mean-semivariance-skewness model for fuzzy portfolio, the mean-semivariance-skewness- semikurtosis is presented and its four corresponding variants are also considered. We briefly designed the genetic algorithm integrating fuzzy simulation for our optimization models.Fuzzy moments, Credibility theory, Portfolios, Asset allocation, multi-objective optimization

Optimizing Triangular Parabolic Fuzzy EOQ Model with Shortage Using Nearest Interval Approximation

Many Re-searchers have introduced different topics using fuzzy numbers. Triangular parabolic, Trapezoidal parabolic, Hexagonal and octagonal fuzzy numbers are developed in such a way that their membership function attains the highest value only between an interval. If that fuzzy numbers are parabolic in shape when they attains the highest value at midpoint of an interval and called as Triangular parabolic fuzzy number. This paper deals with developing ?-cut from Triangular parabolic membership function and using Triangle shaped values with an Economic Order Quantity(EOQ)model with shortage,here the setup cost ,holding cost, shortage cost are defined as fuzzy numbers. The purpose of this research is to analyse in which point attains it�s maximum value also using midpoint of an interval. Finally numerical examples along with graphical representation of the results are presented

Fuzzy power aggregation operators and their application to multiple attribute group decision making

The article investigates the multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of triangular fuzzy information. Motivated by the ideal of power aggregation, in this paper some power aggregation operators for aggregating triangular fuzzy information are developed and then applied in order to develop some models for multiple attribute group decision making with triangular fuzzy information. Finally, some illustrative examples are given to verify the developed approach and to demonstrate its practicality and effectiveness

Optimization of traffic light control system of an intersection using fuzzy inference system

This paper considers an automated static road traffic control system of an intersection for the purpose of minimizing the effects of traffic jam and hence its attendant consequences such as prolonged waiting time, emission of toxic hydrocarbons from automobiles, etc. Using real-time road traffic data, a dynamic round-robin allocation of right-of-way to road users based on fuzzy inference system (FIS) was implemented as a decision support tool. The static phase scheduling algorithm for traffic light systems was used as a benchmark to measure the performance of our technique which is based on dynamic phase scheduling algorithm. The performance comparison records a significant improvement of about 65.35% in average waiting time. This clearly demonstrates the efficacy and potential of our solution strategy to address the traffic scheduling problem.Keywords: Fuzzy Logic; Traffic Control Systems; Dynamic Phase Scheduling; Static Phase Scheduling, Fuzzy Set