The present study aims to clarify the necessity and effectiveness of considering fuzziness in modelling fish habitat preference, and the advantages which would be achieved by considering it. For this purpose, genetic algorithm (GA) optimized habitat preference models under three different levels of fuzzification were compared with regard to prediction ability of the habitat use of Japanese medaka (Oryzias latipes) dwelling in agricultural canals in Japan. Field surveys were conducted in agricultural canals in Japan to establish a relationship between fish habitat preference and physical environments of water depth, current velocity, lateral cover ratio and percent vegetation coverage. The habitat preference models employed for testing the fuzzy-based approach were category model, fuzzy habitat preference model, and fuzzy habitat preference model with fuzzy inputs. All the models were developed at 50 different initial conditions. The effectiveness of the fuzzification in fish habitat modelling was assessed by comparing mean square error and standard deviation of the models, and fluctuation in habitat preference curves evaluated by each model. As a result, the effect of fuzzification appeared as smoother curves and was found to reduce fluctuation in habitat preference curves in proportion to the level of fuzzification. The smooth curves would be appropriate for expressing uncertainty in habitat preference of the fish, by which fuzzy habitat preference model with fuzzy input achieve the best prediction ability among the models. In conclusion, the present study revealed that there are two advantages of fuzzification: reducing fluctuations in habitat preference evaluation and improving prediction ability of the model. Therefore, the consideration of fuzziness would be appropriate for representing fish habitat preference under natural conditions.
In this paper, we address the problem of planning the universal mobile telecommunication system base stations location for uplink direction. The objective is to maximize the total traffic covered and minimize the total installation cost based on data involving fuzziness. To define the cost, researchers used the current period market prices as constants. However prices may change over time. Our aim here is to deal with the imprecise and uncertain information of prices. For this we introduce a model of problem where each cost is a fuzzy variable, and then we present a decision-making model based on possibility theory. To solve the problem we propose a search algorithm based on the hybridization of genetic algorithm and local search method. To validate the proposed method some numerical examples are given.
This paper presents two mathematical models representing imprecise capacitated fixed-charge transportation problems for a two-stage supply chain network in Gaussian fuzzy type-2 environment. It is a two-stage transportation process from a manufacturing center to m potential distribution centers (DCs) and then from DCs to business centers of n retailers with particular demands. Retailers are situated at some distances apart. Here unit transportation costs, fixed charges, availabilities, and demands are imprecise and represented by Gaussian type-2 fuzzy numbers. The proposed models are formulated as profit maximization problems in such a way that some DCs are selected in order to satisfy the demands at all retailers. The type-2 fuzziness has been removed by using generalized credibility measure developed with the help of CV-based reduction method and hence the models are reduced to chance constrained programming problems with different credibility labels. The deterministic models are then solved using both genetic algorithm (GA) based on Roulette wheel selection, arithmetic crossover with uniform mutation and modified particle swarm optimization (PSO), where the position of each particle is adjusted according to its own experience and that of its neighbors. Finally models are illustrated with some numerical data. Some sensitivity analyses on the proposed models are presented. (C) 2015 Elsevier Inc. All rights reserved.
This paper is based on two mathematical models for multi-item multi-stage solid transportation problem with budget on total transportation cost in Gaussian type-2 fuzzy environment considering the fixed opening charge and operating cost in distribution center. The first model is about transportation of breakable/damageable items, and the second one considers non breakable/damageable items. The main aspect here is to develop the mathematical formulation of multi stage related solid transportation problem where several items are available for transportation. In order to deal with the Gaussian type-2 fuzziness, two chance-constrained programming models are developed based on generalized credibility measures for the objective function as well as the constraints sets with the help of the CV-based reductions method. Finally the reduced model is turned into its equivalent parametric programming problem. The problem is of high complexity and is difficult to find the optimal solution by any classical method and hence a time and space based meta-heuristic Genetic Algorithm has been proposed. Also the equivalent crisp models are solved using GA and LINGO 13.0 and after comparison, GA results are better. The proposed models and techniques are finally illustrated by providing numerical examples. Some sensitivity analysis and particular cases are presented and discussed. Degrees of efficiency is also evaluated for both the techniques.