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Risk assessment of urban network using traffic indicators determines vulnerable links with high danger of traffic incidents. Thus Determination of an appropriate methodology remains a big challenge to achieve this objective. This paper proposed a methodology based on data fusion concept using Fuzzy-AHP and TOPSIS to achieve this aim. The proposed methodology tries to overcome two main problems, first of all using Fuzzy AHP for weight estimation of risk indicator, overcomes the problem of some famous weighting method such as AHP that uses limited scale of Saaty (1-9) for weight estimation. Because in risk assessment decision maker prefer to compare criteria with a range instead of using exact number such as Saaty scale As a result fuzzy triangular number was proposed in our methodology. What’s more using TOPSIS method is proposed for risk score estimation respecting estimated weight, because all input risk data are numeric furthermore risk evaluation would be done using distance from ideal solution.To test the proposed methodology an urban network in North of Washington was selected as pilot area. In the next step input criteria such as annual average daily traffic (AADT index), accident severity (IR index), average slope and closeness to critical place (that need traffic controlling such as school) were determined as risk indicators using Iranian traffic organization expert’s idea then nonlinear-Fuzzy-AHP was used to estimate weight of input criteria. Estimated weight entered to TOPSIS method to determine vulnerable links that are in high danger of traffic incidents.

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Option valuation has been a challenging issue of financial engineering and optimization for a long
time. The increasing complexity of market conditions requires utilization of advanced models that,
commonly, do not lead to closed-form solutions. Development of novel numerical procedures, which prove to be efficient within various option valuation problems, is therefore worthwhile. Notwithstanding, such novel approaches should be tested as well, the most natural way being to assume simple plain vanilla options under the Black and Scholes model first; because of its simplicity the analytical solution is available and the convergence of novel numerical approaches can be analyzed easily. Here, we present the methodological concepts of two relatively modern numerical techniques, i.e., discontinuous Galerkin and fuzzy transform approaches, and compare their performance with the standard finite difference scheme in the case of sensitivity calculation
(a so-called Greeks) of plain vanilla option price under Black and Scholes model conditions. The results show some interesting properties of the proposed methods.

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In this work, we consider a ‎multi-objective‎ ‎minimal ‎cost ‎flow (MMCF) ‎problem where there are several commodities to transport from‎ sources ‎to ‎destinations and there is more than one conveyance for those transporting. We also assume that in each conveyance, there are distinct capacities for each commodity. The obtained model is not necessary balanced, and we introduced a method to solve this model without converting it to a balanced model. Some advantages of the proposed method is discussed.