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Abstract

  The Analytic Network Process (ANP) is a generalization of the Analytic Hierarchy Process (AHP). The basic structure is an influence network of clusters and nodes contained within the clusters. Priorities are established in the same way they are in the AHP using pairwise comparisons and judgment. Many decision problems cannot be structured hierarchically because they involve the interaction and dependence of higher-level elements in a hierarchy on lower-level elements. Not only does the importance of the criteria determine the importance of the alternatives as in a hierarchy, but also the importance of the alternatives themselves determines the importance of the criteria. Feedback enables us to factor the future into the present to determine what we have to do to attain a desired future. To illustrate ANP, one example is also presented. 

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Here, we examine the capability of artificial neural networks (ANNs) in sensitivity analysis of the parameters of efficiency analysis model, namely data envelopment analysis (DEA). We are mainly interested to observe the required change of a group of parameters when another group goes under a managerial change, maintaining the score of the efficiency. In other words, this methodology provides a platform for simulating the level of some parameters against the remaining parameters for generating different scenarios, as being in demand for managers.

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  We consider criticality in project networks having imprecise activity duration times. It is well known that finding all possibly critical paths of an imprecise project network is an NP-hard problem. Here, based on a method for finding critical paths of crisp networks by using only the forward recursion of critical path method, for the first time an algorithm is proposed which can find all possibly critical paths of interval-valued project networks. The proposed algorithm considers interactivity among paths which has not been yet considered in the fuzzy project scheduling literature. The extension of the proposed algorithm to the fuzzy network calculates criticality degrees of activities and paths of projects without any need to enumerate all project paths. Although algorithms for calculating criticality degrees in fuzzy networks have been previously proposed, despite the fact that they mostly consider a specific type of fuzzy numbers as activity duration times, the exiting algorithms do not discriminate possibly critical paths before calculating the criticality degrees. The computational experience on a series of well-known project samples confirms the algorithm to be remarkably more efficient than similar algorithms for fuzzy networks.

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A number of problems in several areas such as power transmission and distribution, communication and transportation can be formulated as a stochastic-flow network (SFN). The system reliability of an SFN can be computed in terms of all the upper boundary points, called d-MinCuts (d-MCs). Several algorithms have been proposed to find all the d-MCs in an SFN. Here, some recent studies in the literature on search for all d-MCs are investigated. We show that some existing results and the corresponding algorithms are incorrect. Then, correct versions of the results are established. By modifying an incorrect algorithm, we also propose an improved algorithm. In addition, complexity results on a number of studies are shown to be erroneous and correct counts are provided. Finally, we present comparative numerical results in the sense of performance profile of Dolan and Moré showing the proposed algorithm to be more efficient than some existing algorithms.

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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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A genetic algorithm is proposed to optimize a tree-structured power distribution network considering optimal cable sizing. For minimizing the total cost of the network, a mixed-integer programming model is presented determining the optimal sizes of cables with minimized location-allocation cost. For designing the distribution lines in a power network, the primary factors must be considered as maximum allowable electrical flow in cables, permitted length of cables, maximum permitted voltage drops, and balance of load. The relationship between rates of electric current and cable sizes with consideration of constraints such as voltage drops and length are our essential data. To create a network with a minimum number of arcs and no closed loop such that all the nodes are covered, a minimum spanning tree technique is utilized. Here, we solve the problem using a genetic optimization algorithm and apply the offered approach to a real problem. By comparing the two extracted results from the proposed approach and an exact method, effectiveness of the genetic algorithm for optimization of power distribution network is shown. To demonstrate the validity of the offered model, a case study in Tehran power distribution company in Iran is made.

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In this paper bus scheduling problem under the constraints that the total number of buses needed to perform all trips is known in advance and the energy level of buses is limited, is considered. Each depot has a different time processing cost. The goal of this problem is to find a minimum cost feasible schedule for buses. A mathematical formulation of the problem is developed. When there are two depots, a polynomial time algorithm is developed for the problem and theoretical results about the complexity and correctness of the algorithm is presented. Also, several examples are introduced for illustrating validity of the algorithm.

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Nowadays, airline industries should overcome different barriers regarding the fierce competition and changing consumer behavior. Thus, they attempt to focus on joint decision making which enables them to set pricing and capacity allocation to maximize their profits. In this research, we develop a model to optimize pricing and capacity allocation in a duopoly of single-flight leg for two competitive airlines. The problem considers actual assumptions about flexible partitions in flight’s cabins and additionally demand uncertainty. There is a flexible partitioning of business and economy cabins and demand is assumed price-dependent with additive uncertainty. The capacity and pricing decisions are simultaneously determined through indirect channels. Moreover, a numerical study is developed to investigate how market components and competition conditions change pricing, capacity, and profit levels. The results show that increasing market volume like decreasing price sensitivity provides higher levels of price and profits. Moreover, intensified competition never leads to higher prices. Thus, a competitive network of airlines provides better impact on market mechanism to achieve competitive prices for both economy and business classes.

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Nowadays, manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of supply chain planning. On the other hand, increasing worldwide environmental, lack of food resources and social concerns are motivating manufacturers and consumers to implement recycling strategies such as product recovery, waste management, or usage of recycled materials. In this study, the closed-loop supply chain network has been proposed which consists of four echelons (suppliers, plants, distribution centers, and customers) in the forward chain and three echelons (collection centers, inspection centers, and disposal centers) in the backward chain. We present a multi-product and multi-period mixed-integer linear programming problem in this paper. The objective of this study is to maximize the profit in the closed-loop supply chain network. The proposed model is applied to an illustrative example based on inspiration from the dairy industry in Iran. The solution of the proposed model is achieved by using Gams software. The results give important insight for fostering the decision making process.

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Nowadays, due to population increase and expansion of the transportation networks, the monitoring of this network, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This network includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilon-constraint method is used which simultaneously determines the location of the patrols, allocation of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

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In this paper, two non-linear technologies are proposed based on weak disposability definitions: weak disposability with non-uniform abatement factors and new weak disposability. Both technologies are applied to Spanish airport systems and the existing technologies are modified. To remove the computational complexity of non-linear approaches, the linearization methods are proposed. Then, in order to evaluate the efficiency measure of decision making units (DMUs), a directional distance function (DDF) is applied to the linear technologies and the analysis of the results is presented.

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In this study, an alternative method is proposed based on recursive deep learning with limited steps and prepossessing, in which the data is divided into A unit classes in order to change a long short term memory and solve the existing challenges. The goal is to obtain predictive results that are closer to real world in COVID-19 patients. To achieve this goal, four existing challenges including the heterogeneous data, the imbalanced data distribution in predicted classes, the low allocation rate of data to a class and the existence of many features in a process have been resolved. The proposed method is simulated using the real data of COVID-19 patients hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020, which has led to recovery or death. The obtained results are compared against three valid advanced methods, and are showed that the amount of memory resources usage and CPU usage time are slightly increased compared to similar methods  and the accuracy is increased by an average of 12%.

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This research uses Network Data EnvelopmentAanalysis (NDEA) by  undesirable factors to analyze and evaluate the performance of automotive industry. The modeling used is applied to five production lines of an automobile company by 16 indicators. The data used are for the year 2019. The main purpose is to provide a model to improve the quality of the product by evaluating the performance of quality health in production lines able  to rank by providing appropriate quality indicators to identify, formulate and achieve corrective measures. Accompanied with accurate problem solving and operational scheduling according to the most efficient organization/production line and so investigating the source of the problem and preventing the occurrence of the problem. Because determining the direction of performance and key performance indicators (KPI) of the organization and measuring them to increase its health efficiency requires an efficient and integrated system. On the other hand, creating a homogeneous and orderly development process between the elements of the organization as a common language to solve the quality problems by aiming the improvement of the performance, customer satisfaction, sustainable production and cost management has been proposed.

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Data Envelopment Analysis (DEA) is a nonparametric approach for evaluating the relative efficiency of a homogenous set of Decision Making Units (DMUs). To evaluate the relative efficiency of all DMUs, DEA model should be solved once for each DMU. Therefore, by increasing the number of DMUs, computational requirements are increased. The Cerebellar Model Articulation Controller (CMAC) is a neural network that resembles a part of the brain known as cerebellum. The CMAC network with a simple structure is capable of estimating nonlinear functions, system modelling and pattern recognition. Meanwhile, the CMAC approach has fast learning convergence and local generalization in comparison to other networks. The present paper is concerned with assessing the efficiency of DMUs by the CMAC neural network for the first time. The proposed approach is applied to a large set of 600 Iranian bank branches. The efficiency results are analyzed and compared with the Multi-layer Perceptrons (MLP) network outcomes. Based on the results, it can be seen that the DEA-CMAC results tend to be similar to those of DEA-MLP in terms of accuracy. In addition, the Mean Squared Error (MSE) in DEA-CMAC decreases much faster than that in DEA-MLP. The DEA-CMAC model takes 1008 and 1107 iterations to reach MSE errors of 2.03×〖10〗^(-4) and of 6.01×〖10〗^(-4), respectively, while the DEA-MLP model takes 1190 iterations keeping the MSE error stable at 2.07×〖10〗^(-1). Moreover, DEA-CMAC requirements for CPU time are far less than those needed by DEA-MLP.

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This paper develops slacks-based measure (SBM) and additive SBM (ASBM) to evaluate efficiency of decision making units (DMUs) in a two-stage structure with undesirable outputs and feedback variables from the internal perspective. The SBM model is linearized  for a specific weight and the ASBM model is reformulated as a second order cone program. The target values for all inputs, outputs (both desirable and undesirable) and intermediate products are  provided. This study shows that unlike the SBM model, ASBM can be adapted to the preference of the decision maker by selecting the weights to aggregate stages in the network.
 

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Abstract: This study developed a mathematical programming model in order to consider an SCND problem. In this model, the operational and financial decisions are simultaneously considered to design a supply chain network. It also paves the way for opening or closing facilities in order to adapt to fluctuations at market. Furthermore, an accounts payable policy is provided for the company managers because bank loans, liability repayment and new capital from shareholders are considered as decision variables in this model. The economic value added (EVA) index was also used besides the common operational objectives and constraints in order that the financial performance of supply chain and lower and / or upper limit value for financial rations to be measured. To demonstrate the efficiency of the proposed model, a test problem from the recent literature is used. And also, sensitivity analyses to evaluate the results are provided to obtain better insight and access to different aspects of the problem. The results illustrate that with appropriate financial decisions, creating more value for the company and its shareholders is achievable since the total created value by the proposed model with a new financial approach is able to improve the total created shareholder value as much as 21.05% and convince the decision-makers to apply it as an effective decision tool.
 

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Data Envelopment Analysis (DEA) is a nonparametric approach for evaluating the relative efficiency of a homogenous set of Decision Making Units (DMUs). To evaluate the relative efficiency of all DMUs, DEA model should be solved once for each DMU. Therefore, by increasing the number of DMUs, computational requirements are increased. The Cerebellar Model Articulation Controller (CMAC) is a neural network that resembles a part of the brain known as cerebellum. The CMAC network with a simple structure is capable of estimating nonlinear functions, system modelling and pattern recognition. Meanwhile, the CMAC approach has fast learning convergence and local generalization in comparison to other networks. The present paper is concerned with assessing the efficiency of DMUs by the CMAC neural network for the first time. The proposed approach is applied to a large set of 600 Iranian bank branches. The efficiency results are analyzed and compared with the Multi-layer Perceptrons (MLP) network outcomes. Based on the results, it can be seen that the DEA-CMAC results tend to be similar to those of DEA-MLP in terms of accuracy. In addition, the Mean Squared Error (MSE) in DEA-CMAC decreases much faster than that in DEA-MLP. The DEA-CMAC model takes 1008 and 1107 iterations to reach MSE errors of 2.03×10-4  and of 6.01×10-4 , respectively, while the DEA-MLP model takes 1190 iterations keeping the MSE error stable at 2.07×10-1 . Moreover, DEA-CMAC requirements for CPU time are far less than those needed by DEA-MLP.
 

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This study aims to develop a capacitated hub location-routing model to design a rapid transit network under uncertainty. The mathematical model is formulated by making decisions about the location of the hub and spoke (non-hub) nodes, the selection of the hub and spoke edges, the allocation of the spoke nodes to the hub nodes, the determination of the hub and spoke lines, the determination of the percentage of satisfied origin-destination demands, and the routing of satisfied demand flows through the lines. Capacity constraints are considered in the hub and spoke nodes and also the hub and spoke edges. Uncertainty is assumed for the demands and transportation costs, represented by a finite set of scenarios. The aim is to maximize the total expected profit, where transfers between the lines are penalized by including their costs in the objective function. The performance of the proposed model is evaluated by computational tests and some managerial insights are also provided through the analysis of the resulting networks under various parameter settings.
 

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Today, a high volume of multimedia information is transmitted in computer networks, such as the internet, that the requirement for achieving high security level against unauthorized access. the visual secret sharing scheme is a cryptographic system without requiring a secret key that is able to encrypt information in a multi - user computer network. in this research, a visual secret sharing scheme including two strategies for binary communications and multiple relationships were considered. in binary communications, an entity plays a role in the role of the server and an entity in the role of the client 's service in this strategy using a network - based visual secret sharing scheme ( 2 , 2 ) a secure approach to establish double relations where the provider entity is sending confidential information after authentication of the client 's service. then, two main phases of registration and verification are performed on the basis of the visual secret sharing scheme based on random networks. in tuple communication ( n , n ) an entity plays a role in the role of the server and several entities in the role of the service, and then two main phases of registration and verification are executed.
 

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One of the most useful tools in Operations Research (OR) which is essentially applied to evaluate the performance of treated Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). Because of in the current decades, DEA models have been used and extended in many disciplines and hence attracted much interests. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. Since, in the real situations, input data are included some uncertainties, hence in this study we consider a DEA with fuzzy stochastic data and suggest a three-stage DEA model by taking into account undesirable output. To achieve this aim, an extended probability approach is applied to the reform of three-stage DEA models. Finally, we give an illustrative example by considering a case study on the banking industry.