A Fuzzy Approach for Maintenance Management of Urban Roadway BridgesAmini, Amin; Nikraz, Navid
2019 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1886810
A successful bridge management system needs to utilise an efficient decision-making model for prioritising the bridges for repair and maintenance operations to deal with the limited allocated funds. Models based on certain mathematics and divalent logic that need accurate data are not flexible with the uncertainty space of project management procedure and lead to imprecise outputs. Unlike classical logic, fuzzy logic represents the propositions with degrees of truthfulness and falsehood. In this paper, a fuzzy decision-making model was developed to prioritise a large number of urban roadway bridges and put them on the agenda for repair and maintenance operations. The proposed model considers experts' feelings, knowledge, and judgment expressed by linguistic variables, vague data or uncertain values in the modelling. The introduced model uses a fuzzy multi-attribute decision-making matrix to evaluate a large number of bridges to a large number of effective factors in the bridge maintenance area and determines the fuzzy desirability priority of each bridge as well as the preference value of every bridge to another one. This capability makes the model adaptable for a particular region or condition and helps managers make quick and more accurate strategic decisions.
A Fuzzy-Weighted Approach to the Problem of Selecting the Right Strategy Using the Robustness Analysis (Case Study: Iran Automotive Industry)Sorourkhah, Ali; Babaie-Kafaki, Saman; Azar, Adel; Shafiei Nikabadi, Mohsen
2019 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1886811
Purpose: We focus on a fuzzy-weighted approach to the robustness analysis based on experts' opinions for selecting a reasonable strategy in a special decision-making problem. Design/methodology/approach: We deal with environmental uncertainty by reviewing performance of the strategies among the alternative futures, to answer some complexities of the strategy selection problem by considering the desired number of scenarios, indicators and options as well as collecting experts' judgments in an appropriate time. Weights of the indicators determining the situations of future scenarios are considered as triangular fuzzy numbers. Also, we use the well-known Dolan-Moré performance profile in order to more precisely investigate the strategies situations, especially when their robustness levels are close to each other. Findings: According to the results, concerning environmental situations as well as the possible future of the automotive industry of Iran, the defensive strategy is a robust decision. Among the sub-strategies related to the main strategy defensive, retrenchment and divestiture strategies have a higher robustness level. Originality/value: Using matrix modeling, it is possible to overcome some of the weaknesses of the classical robustness analysis related to reviewing few scenarios, indicators and options as well as the time-consuming process of collecting the experts' opinions.
Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural NetworkAnitha, A.; Acharjya, D. P.
2019 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1886813
Agriculture plays a vital role in Indian economy. On considering the overall geographical space verses population in India, 7% of population is chronicled in Tamilnadu, with 3% of water and 4% of land resources. Thus an automated prediction system becomes essential for predicting the crop based on the nutritional security of the country. In this paper, effort has been made to process the uncertainties by hybridizing rough set on intuitionistic fuzzy approximation space (RSIFAS) [Acharjya DP, Tripathy BK. Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation. Int J Artif Int Comput Res. 2009;1 (1):29–36.] and neural network [Hecht NR. Theory of the backpropagation neural network. Proceedings of the international Joint Conference on neural networks, 1 (1989), 593–605.]. RSIFAS identifies the almost indiscernibility among the natural resources, and helps in reducing the computational procedure on employing data reduction techniques whereas neural network helps in prediction process. It helps to find the crops that may be cultivated based on the available natural resources. The proposed model is analyzed on data accumulated from Vellore district of Tamilnadu, India and achieved 93.7% of average classification accuracy. The model is compared with earlier models and found 6.9% better accuracy while prediction.
Multi-echelon Supply Chain Model for Deteriorating Products in a Fuzzy Deterioration EnvironmentPriyan, S.; Mala, P.
2019 Fuzzy Information and Engineering
doi: 10.1080/16168658.2021.1886814
Purpose: This research analyses the optimal inventory strategy for a deteriorating product with imprecise deterioration rate in a single supplier-buyer supply chain system with realistic factors. Design/methodology/approach: The integration of production and distribution inventory system is crucial in practice for minimizing a firm's cost while the effect of deterioration cannot be ignored as it affects the entire inventory system. Nowadays managers have begun to recognize that effectively managing deterioration risks in their business operations plays an important role in successfully managing their inventories. Here, the deterioration rate is treated as the triangular fuzzy number. Also we use the extension principle method to define the membership function of the fuzzy total cost and defuzzify by the centroid to find the estimate of the total cost in the fuzzy sense. Findings: A methodology has been proposed through mathematical model which involves designing an iterative algorithm to achieve optimal decisions such as order lot-size and total number of deliveries from the supplier to buyer. Originality value: The effects of varying the central parameter values on the optimal solution are performed by numerical and sensitivity analysis so as to highlight the differences between crisp and the fuzzy cases.