In this paper, the problem of winner determination in a combinatorial Reverse Auction mechanism is considered for solving. Because of NP-completeness of finding a feasible solution for the formulated winner determination problem as well as its solving, the popular exact methods are failed in solving its large-scale instances. So, an exact problem-specific two-stage method is proposed to reduce the time required for its solving. The proposed method involves a well-known population-based meta-heuristic called genetic algorithm and an advanced exact method called Dantzig-Wolfe decomposition in first and second stages, respectively. The genetic algorithm is used for finding a near-optimal feasible solution for the formulated winner determination problem as an initial solution for the second stage. The proposed genetic algorithm generates feasible solutions in initial population and repairs infeasible child solutions after reproduction using problem-specific operators. Also, Dantzig-Wolfe decomposition is used for decomposing the formulated winner determination problem with block-diagonal structure in its constraints matrix to a master problem and multiple sub-problems for finding its optimal solution within a reasonable time starting from the nearoptimal feasible solution found by genetic algorithm in first stage. We conducted a computational experiment using randomly generated instances of winner determination problem with different sizes to evaluate the performance of our proposed two-stage method in solving the formulated winner determination problem. Computational results demonstrate that the genetic algorithm performs well in finding near-optimal solutions of the formulated problem. Also, the computational results show that the Dantzig-Wolfe decomposition based method in second stage can improve the near-optimal solution found by genetic algorithm in first stage and find the optimal solution of formulated winner determination problem as well as confirming the optimality or non-optimality of solution found by genetic algorithm. The other result is that the proposed two-stage method spends less time compared with LINGO software in finding optimal solution of formulated winner determination problem.

In this paper, a combinatorial Reverse Auction mechanism is proposed for selecting the most efficient service-providers for resolving sustained power interruptions in multiple regions of an electric power distribution company’ s responsibility area. Through this mechanism, supplying the required service in each region is assigned to only one potential service-provider considering two criteria including cost and service time. So, the corresponding winner determination problem of the proposed Auction mechanism is formulated as a bi-objective combinatorial optimization problem. However, finding a feasible solution for the formulated problem as well as its solving is NP-complete. Since exact optimization algorithms are failed in solving this kind of problems in a reasonable time, a problem specific metaheuristic called NSGA-II that is an evolutionary algorithm for solving multi-objective optimization problems is developed to estimate the set of Pareto optimal solutions of the formulated bi-objective winner determination problem. In our developed NSGA-II, two problem-specific operators are proposed for creating initial feasible solutions and converting infeasible solutions to feasible ones. Furthermore, a new method for determining the population size based on the size of problem instance is proposed. We conduct a computational experiment in which several randomly generated problem instances are solved using the proposed NSGA-II in different settings. Computational results of proposed algorithm in different settings are compared using a quality measure and statistical hypothesis tests. The results of performance comparison show that the proposed NSGA-II with a population size determined by proposed method and a different form of binary tournament method performs better in finding non-dominated solutions for different instances of formulated bi-objective optimization problem.

One of the main activities of humanitarian logistics is to provide relief items for survivors in case of a disaster. To facilitate the procurement operation, this paper proposes a bidding framework for supplier selection and optimal allocation of relief items. The proposed Auction process is divided into the announcement construction, bid construction and bid evaluation phases. In the announcement phase, the bidder (purchaser or relief organization) invites certain suppliers to the Auction. Next, the construction phase is formulated as a bi-objective fuzzy model from the perspective of suppliers. This phase provides the bidder with several suggestions, each of which containing the amount, price, and lead time of the delivery of relief items. Then, in the evaluation phase, the bidder determines the winners and optimally assigns orders by a multi-objective fuzzy model. Each of the fuzzy mathematical models in the paper is formulated under the uncertainty of parameters and is then solved by a two-stage fuzzy approach. Finally, to illustrate the validity and applicability of the proposed model, a numerical example is provided and its result is analyzed.

Providing required relief items in the condition of post-disaster is a time-consuming and complex problem because of demands uncertainty and the severity of the disaster. This paper presents a model based on Auction with the aim to facilitate purchase activities and to make coordination between suppliers and relief organizations. The structure of the proposed Auction includes construction and evaluation phases which are managed by the suppliers (sellers) and relief organizations (customer) respectively. In the bid construction phase, a fuzzy multi-objective non-linear mathematical programming model is presented that addresses disruption risk in both distribution centers and suppliers and uncertainty among parameters. The proposed model is solved using augmented ε-constraint method, and optimal packages of the suppliers including price, delivery time and quantity of relief items is determined. In the bid evaluation phase, in order to rank the suppliers fuzzy promethee technique was used. In this stage of the bid, each supplier will be assessed by customers regarding proposed packages and other qualitative criteria and the winner of the bid will be determined. To illustrate the validity and applicability of the proposed model, a numerical example is provided, and it has been analyzed from various aspects.

In this study, an attempt is made to predict effective porosity in one of the oil fields in the Persian Gulf by designing a probablistic neural network (PNN) and simultanusely making use of seismic attributes and effective porosity logs in the reservoir window. This was done by deriving a multiattribute transformation between an optimum subset of seismic attributes and the effective porosity logs.The geophysical data used in this study consist of 3D seismic pre-stack time migrated (PSTM) data with 12.5*12.5 m grid size and a 4 ms sampling rate. The length of the seismic traces are two seconds. Well logs of five vertical wells in the study area, including Sonic (DT), Density (RHOB), Effective Porosity (PHIE) and Seismic Well Velocity Surveys (Check Shots), were used. The reservoir layer is a Mishrif member of the Sarvak formation with Cretaceous age, which is common in oil reservoirs in the Persian Gulf. The top of the Mishrif is adjusted with the Middle Turonian Unconformity and covered with shaley Laffan formation. The Mishrif Reservoir in study area contains two reservoir zones. The lower zone with higher clay content is separate from the upper zone. The upper zone consists of clean limesone with better reservoir properties. Seismic traces close to the well locations were used to generate seismic attributes. Effective porosity logs at the reservoir area were the target logs in this study.The designed neural network consists of one input layer, one hidden layer with four processing units (neuron), and one output layer with one neuron. In order to prepare training samples for the neural network, PHIE logs were converted to time domain using a time-depth relationship calculated from the DT logs and check shot curves for each well location. Subsequently, these logs were filtered (using a Hanning filter with 4 ms length) and resampled with seismic sampling rate (4 ms). Finally, a set of seismic attributes, including sixteen sample-based seismic attributes, were generated using HRS software. Training samples in this study consisted of 57 samples (selected seismic attributes and their related effective porosity from PHIE logs in the time domain). For training the network, the samples were divided into three data sets: the training samples, cross validation samples and testing samples. The training data were used for adjusting the weights of the network; the cross validation data were used to prevent overtraining theneural network; and the testing data were used to ensure generalizabillity of the network output.A forward stepwise regression process was used to determine an optimum subset of attributes for use in the training of the neural networks. The optimum subset of attributes in this study consists of the Dominant Frequency, Amplitude Weighted Frequency, Integrated Absolute Amplitude and Filter 45-60 Hz.After the network was trained using training and cross validation data sets, it was used to predict the testing data. The results show a good correlation between real and predicted data, with 92% correlation. Finally, in order to attain a better generalization of the network, testing data sets were inserted to trained data and the network was trained again. This network was then used to predict effective porosity in well locations which increased the correlation coefficient to 95%. This study shows the ability of the PNN networks to predict effective porosity even with a paucity of training examplares.

The present article discusses the impacts of selecting between different common Auctions in electricity markets (system marginal price Auction and pay as bid Auction) on production efficiency, total efficiency and average expected price in an asymmetric information situation, where each player’s information about the marginal cost of the competitor is incomplete. A simple model is designed to conduct the comparative study of alternative Auction mechanisms. The model is based on two profit maximizing players, with full information about their own marginal cost and incomplete information about their competitor. Assumptions which have been used to construct the model are based on Iran’s electricity market structure. The outcome indicates that although players bid functions in alternative Auction mechanism differ from each other, the production efficiency, total efficiency and the average expected price are equivalent.

Today, the expansion of social relations between individuals and the emergence of social phenomena resulting from it, creates a law in relations between individuals that it is necessary for the law to determine the event that causes it, according to which the conditions for creation change and eliminate Go check it out. Undoubtedly, knowing its legal nature has the greatest role in recognizing its effects and rulings. In this research, an attempt has been made to explain the legal nature and guarantee of tender and Auction procedures by descriptive-analytical method and by examining the law of tenders, Auctions, regulations and related clauses and explaining the expressed opinions, and by examining gaps in the country's legal system In the field of prevention and fight against corruption and collusion in their implementation. In the following, the role that these conditions play in the nature of government contracts will be discussed and the ambiguities and problems of holding Auctions and tenders in Iranian law and economic name will be examined.

Reverse redistribution pattern is defined as decreased activity in the myocardium in the rest phase of the myocardial perfusion scan in comparison with the stress images. There are many studies concerning the etiology and clinical significant of this phenomenon in nuclear medicine literature. The dominant idea about etiology of Reverse redistribution is early wash out of the radiotracer from the myocardium. There is rather unanimous agreement among researchers about viability of the areas of Reverse redistribution and the majority of the studies point to existence of viable tissue in these regions. However from prognostic point of view, this issue is much more controversial. In this review, we tried to summarize the current literature and reach a guideline for practical significance of Reverse redistribution in every day work of nuclear medicine specialists.