This article presents a broad review of Hub location problem (HLP), and its applications. The goal of this article is to provide a review on the newest and most recent publications on Hub location and its application in real world practices since 2013. While, there are some articles which reviewed hub location problem literature but some models were not covered in those articles. In this paper, we try to include capacitated models. We survey advances in analysis and modeling of the hub location problem including its variants and solution algorithm of HLPs. We emphasize the most applicable areas for Marine Transpiration. We show that first, majority of the articles consider network domain, while there are some articles which execute HLP in discrete and continuous domain. Also, the capacitated case attracts more attention in recent years. Finally, the contribution of any type of HLP in the literature is shown schematically.

Nowadays, selecting the most appropriate location for hub is one of the most significant issues not only in road, rail and air transportations, but also in maritime. Transshipment is the fastest growing segment of the marine container market; it increases traffic flow of marine container and scope of this type of marine carriage, accordingly. In this way, determining a movement loop for the voyages of a shipping company, probes identification of container hub ports by considering different operational factors including distance to the destinations. The focus of this paper is to locate the best location for container transshipment hub in southern seas of Iran. In this paper, an MCDM model is proposed for evaluating and selecting the marine container transshipment hub port. Finally, the utilization of the proposed model is demonstrated with a real case study of Iranian main ports. The results show that the MCDM model can be used to explain the evaluation and decision-making procedures of a proper marine container hub location selection.

Providing insurance contract with “deductible” is beneficial for both insurer and insured. In this paper, we provide a utility modeling approach to handle insurance pricing and evaluate the tradeoff between discount benefit and deductible level. We analyze four different pricing problems of no insurance, full insurance coverage, insurance with β% deductible and insurance with D-dollar deductible based on a given utility function. A numerical example is also used to illustrate some interesting results.

At the computational point of view, a fuzzy system has a layered structure, similar to an
artificial neural network (ANN) of the radial basis function type. ANN learning algorithms can
be employed for optimization of parameters in a fuzzy system. This neuro-fuzzy modeling
approach has preference to explain solutions over completely black-box models, such as ANN.
In this paper, we implement the design of experiment (DOE) technique to identify the
significant parameters in the design of adaptive neuro-fuzzy inference systems (ANFIS) for
stock price prediction.

In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their corresponding?-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.

One of the most important issues on educational systems is to measure the relative efficiency of similar units based on non-financial factors. Data envelopment analysis (DEA) has become popular among many who wish to rank educational systems using different factors such as the rate of educational success or the number of employees, etc. However, one of the main concerns on implementing DEA methods is the uncertainty involved in input/output parameters. In this paper, a robust data envelopment analysis (RDEA) is developed to measure the efficiency of high schools considering uncertainty on output parameters. We present an empirical study on a set of high schools located in Tehran, which is the capital city of Iran. The study uses uncertain data for input/output information and the results are compared with an existing parametric stochastic frontier analysis (SFA). The preliminary results indicate that the robust DEA approach is relatively a reliable method for efficiency estimating.

During the past two decades, there have been tremendous efforts on developing efficient
business excellence models to improve the quality of organizations. A typical business
excellence model proposes several alternatives with different budget and the primary objective
is to use the best ones. In this paper, we present a robust LINMAP method for measuring the
relative importance of various alternatives in an EFQM self assessment. The presented robust
model is capable of handling uncertainty as part of the problem formulation. The proposed
model of this paper is implemented for a case-study of energy sector in Iran with various
alternatives. The orderings of the alternatives are measured using data from multiple experts
through applying the proposed model of this paper.

When a production facility is designed, there are various parameters affecting the number machines such as production capacity and reliability. It is often a tedious task to optimize different objectives, simultaneously. The other issue is the uncertainty in many design parameters which makes it difficult to reach a desirable solution. In this paper, we present a new mathematical model with two objectives. The primary objective function is considered to be the production capacity and the secondary objective function is total reliability. The proposed model is formulated on different units of production which are connected together in serial form and for each unit, we may have various machines. The resulted model is formulated using recent advances of robust optimization and solution procedure is analyzed with some numerical examples.