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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.
 

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The backup 2-median location problem on a tree T is to deploy two servers at the vertices such that the expected sum of distances from all vertices to the set of functioning servers is minimum. In this paper, we investigate the backup 2-median location problem on tree networks with trapezoidal interval type-2 fuzzy weights. We first, present  a new  method for comparing generalized trapezoidal fuzzy numbers and then develop it for trapezoidal interval type-2 fuzzy numbers. Then numerical examples are given to compare the proposed methods with other existing  methods. Finally, we apply our ranking method to  solve the the backup 2-median location problem on a tree network with trapezoidal interval type-2 fuzzy weights.

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Supplier selection is one of the main discussions in the Supply Chain. The issue of assigning purchase orders to suppliers that act differently in terms of quality, cast, services, etc. criteria is one of the significant concerns of purchase managers in the supply chain. To adopt an optimal decision in this regard is related to a multi-objective problem that the objectives are contradicting each other and have different importance and priority depending on the location. In practice, the existence of kind of ambiguity in explaining the information related to the problem constraints and complicated. In this regard, the emergence of Fuzzy set theory as a tool to describe such conditions besides presenting question model realistically can help to solve such problems well. Despite the importance of the model with the mentioned structure, unfortunately, few original works have been done in this field. As a result, in this paper, in addition to presenting a new multi-objective Fuzzy model being modelled based on assigning purchase order to suppliers in a supply chain a solution method is introduced based on using Fuzzy linear programming. To clarify solution process modelling and description, a case study is included related to selecting flour supplier for providing industrial bread of Khoshkar factory. The proposed model includes four objective functions:

1. Aggregate costs of minimizing type,
2. Services of maximizing type (such as packing, being faithful to promise, factory heath, discount, correct transportation, good relationships, honestly, etc.),
3. Flour useful survival of maximizing type (regarding monthly flour buying by the factory),
4. The purchased flour quality of maximizing type (concerning product type).

 Especially in the solution process, a method is determined based on setting weight for each of the objectives concerning the major factory stockholders.