fuzzy multiple objective linear programming (FMOLP) approaches have been used to be solved many applications of multi-objective decision-making (MODM) problems. Several methods have been proposed including max-min app...
详细信息
fuzzy multiple objective linear programming (FMOLP) approaches have been used to be solved many applications of multi-objective decision-making (MODM) problems. Several methods have been proposed including max-min approaches, preemptive approaches, and weighted approaches. However, they have some limitations in use;some may not be able to obtain efficient solutions or can give only a few solutions. Some methods need to be solved in two steps or specify the target level, which may be difficult for decision-makers (DMs). In this research, a new single-phase interactive fuzzy programming approach with priority control that can find several efficient solutions is proposed. It is not necessary to specify the target value for each objective and it can solve with only one step for each solution. The DM can easily select the appropriate solutions from a set of efficient solutions. This method is different from existing single-phase approaches by controlling the satisfaction level of the last priority instead of using weight additive. Simple examples and a practical example of a perishable product supply chain network were tested to show the effectiveness of the proposed model. The performance of the proposed method was also compared with existing methods to verify and validate the model.
Yang ct al., in their paper "fuzzy programming with nonlinear membership functions...", published in fuzzy Sets and Systems 41 (1991), declared that their model can solve a fuzzy program with an S-shaped mem...
详细信息
Yang ct al., in their paper "fuzzy programming with nonlinear membership functions...", published in fuzzy Sets and Systems 41 (1991), declared that their model can solve a fuzzy program with an S-shaped membership function by adding only one 0-1 variable. This paper indicates that their declaration is correct only for a specific type of S-shape membership functions. We propose another model to treat the fuzzy programs which cannot be solved effectively by Yang et al. (C) 1999 Elsevier Science B.V. All rights reserved.
In real life situations, it is difficult to handle multi-objective linear fractional stochastic transportation problem. It can't be solved directly using mathematical programming approaches. In this paper, a solut...
详细信息
In real life situations, it is difficult to handle multi-objective linear fractional stochastic transportation problem. It can't be solved directly using mathematical programming approaches. In this paper, a solution procedure is proposed for the above problem using a genetic algorithm based fuzzy programming method. The supply and demand parameters of the said problem follow four-parameters Burr distribution. The proposed approach omits the derivation of deterministic equivalent form as required in case of classical approach. In the given methodology, initially the probabilistic constraints present in the problem are tackled using stochastic programming combining the strategy adopted in genetic algorithm. Throughout the problem, feasibility criteria is maintained. Then after, the non-dominated solution are obtained using genetic algorithm based fuzzy programming approach. In the proposed approach, the concept of fuzzy programming approach is inserted in the genetic algorithm cycle. The proposed algorithm has been compared with fuzzy programming approach and implemented on two examples. The result shows the efficacy of the proposed algorithm over fuzzy programming approach.
To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port di...
详细信息
To solve the optimization problem of tugboat scheduling for assisting ships in entering and exiting ports in uncertain environments, this study investigates the impact of the decisions of tugboat operators and port dispatchers on tugboat scheduling under the scenario of dynamic task arrival and fuzzy tugboat operation time. Considering the features of the shortest distance tugboat principle, the first available tugboat principle, and the principle of fairness in the task volume of each tugboat, the tugboat company aims to minimize the total daily fuel consumption of tugboat operations, maximize the total buffer time of dynamic tasks, and minimize the total completion time as the objective functions. Due to the limitations of port vessel berthing and departure, as well as the allocation standards for piloting or relocating tugboats, the present study proposes a Stackelberg game-based fuzzy model for port tugboat scheduling with the tugboat operator and port dispatcher acting as decision makers at the upper and lower levels, respectively. A seagull optimization algorithm based on priority encoding and genetic operators is designed as a solution approach. CPLEX, genetic algorithm, standard seagull optimization algorithm, and simulated annealing algorithm are used to compare and analyze the solution results for the 45 problem cases generated from the actual data obtained from the Guangzhou Port. The results verify the efficiency of the proposed seagull optimization algorithm based on priority encoding and genetic operators. Furthermore, additional experiments are conducted to evaluate the changes in fairness coefficient, uncertain parameter correlation coefficients, and objective function correlation coefficients to demonstrate the practicality of the fuzzy programming model. This analysis involves adjusting the confidence level incrementally from 0 to 100% with respect to the model's uncertain parameters.
This paper presents a fuzzy programming method to design supply chain network, in which the customer demands and transportation costs are assumed to be fuzzy parameters. Existing researches on supply chain network des...
详细信息
This paper presents a fuzzy programming method to design supply chain network, in which the customer demands and transportation costs are assumed to be fuzzy parameters. Existing researches on supply chain network design problem are either restricted on deterministic environment or only address stochastic parameters. In this paper, we consider this problem in fuzzy environment. Under different criteria, we format three types of models for the decision makers: expected cost optimization model, chance-constrained model and chance maximization model. A genetic algorithm based on fuzzy simulation is developed to solve the proposed fuzzy models. Moreover, some numerical examples are presented to illustrate the effectiveness of models and solution algorithm.
All existing methods generate a set of nondominated solutions or construct a single compromise solution for the linear multiobjective transportation problem in which objectives are equally important. In this paper, an...
详细信息
All existing methods generate a set of nondominated solutions or construct a single compromise solution for the linear multiobjective transportation problem in which objectives are equally important. In this paper, an additive fuzzy programming model for the multiobjective transportation problem is presented. The method aggregates the membership functions of the objectives to construct the relevant decision function. Weights and priorities for nonequivalent objectives are also incorporated in the method. This model gives a nondominated solution which is nearer to the best compromise solution. A Fortran program has been developed based on simple and weighted additive algorithms. The method is illustrated with a numerical example.
In this paper we propose an interactive fuzzy programming method for obtaining a satisfactory solution to a "bi-level quadratic fractional programming problem" with two decision makers (DMs) interacting with...
详细信息
In this paper we propose an interactive fuzzy programming method for obtaining a satisfactory solution to a "bi-level quadratic fractional programming problem" with two decision makers (DMs) interacting with their optimal solutions. After determining the fuzzy goals of the DMs at both levels, a satisfactory solution is efficiently derived by updating the satisfactory level of the DM at the upper level with consideration of overall satisfactory balance between both levels. Optimal solutions to the formulated programming problems are obtained by combined use of some of the proper methods. Theoretical results are illustrated with the help of a numerical example.
One of the generalizations of fuzzy programming (FP) is intuitionistic fuzzy programming (IFP). IFP and goal programming (GP) are two important techniques for determining the solution (optimal) of multi-objective opti...
详细信息
ISBN:
(纸本)9789811033223;9789811033216
One of the generalizations of fuzzy programming (FP) is intuitionistic fuzzy programming (IFP). IFP and goal programming (GP) are two important techniques for determining the solution (optimal) of multi-objective optimization problem by transforming it to a single objective one. The main purpose of this article is to introduce the similarities between IFP and GP. In this work, the max and min-operator are considered to transform the IFP to a deterministic program. One example is given to show the applicability of the proposed theory.
暂无评论