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Showing 18 results for Network

Thomas L. Saaty,
Volume 1, Issue 1 (5-2008)
Abstract

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. 


Thomas Saaty,
Volume 1, Issue 2 (6-2009)
Abstract


Habibi Badrabadi, Tarokh,
Volume 2, Issue 1 (4-2010)
Abstract

  Network and processing overhead associated with web services is a significant challenge to its performance. As a result, web service providers often announce a service level agreement. This ensures that consumers, who pay for the service, can get the service at a given quality level. In this paper, we study the competition between two providers offering functionally the same web services, where there is a monopoly service provider who offers a service that is complementary to their services. Each provider needs to decide a service level (L or H) he/she would offer and a corresponding price for the selected service level to meet the QoS guarantee. We combine modeling constructs from game theory and queuing theory to propose a model that can provide useful insights to service providers about pricing and general competitive strategies.

  


Etebari, Aaghaie, Khoshalhan,
Volume 3, Issue 1 (4-2012)
Abstract

In recent years, enriching traditional revenue management models by considering the customer choice behavior has been a main challenge for researchers. The terminology for the airline application is used as representative of the problem. A popular and an efficient model considering these behaviors is choice-based deterministic linear programming (CDLP). This model assumes that each customer belongs to a segment, which is characterized by a consideration set, which is a subset of the products provided by the firm that a customer views as options. Initial models consider a market segmentation, in which each customer belongs to one specific segment. In this case, the segments are defined by disjoint consideration sets of products. Recent models consider the extension of the CDLP to the general case of overlapping segments. The main difficulty, from a computational standpoint, in this approach is solving the CDLP efficiently by column generation. Indeed, it turns out that the column generation subproblem is difficult on its own. It has been shown that for the case of nonoverlapping segments, this can be done in polynomial time. For the more general case of overlapping segments, the column generation sub-problem is NP-hard for which greedy heuristics are proposed for computing approximate solutions. Here, we present a new approach to solve this problem by using a genetic algorithm and compare it with the column generation method. We comparatively investigate the effect of using the new approach for firm’s revenue
Karamali, Memariani, Jahanshahloo,
Volume 4, Issue 1 (5-2013)
Abstract

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.
Morovatdar, Aghaie, Roghanian, Asl Haddad,
Volume 4, Issue 1 (5-2013)
Abstract

  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.


Izadi, Ranjbarian, Ketabi, Nassiri-Mofakham,
Volume 4, Issue 1 (5-2013)
Abstract

  Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis has recently attracted the researchers’ interest. This study evaluates multi-group discriminant linear programming (MDLP) for classification problems against well-known methods such as neural networks and support vector machine. MDLP is less complicated as compared to other methods and does not suffer from having local optima. This study also proposes a fuzzy Delphi method to select and gather the required data, when databases suffer from deficient data. In addition, to absorb the uncertainty infused to collecting data, interval MDLP (IMDLP) is developed. The results show that the performance of MDLP and specially IMDLP is better than conventional classification methods with respect to correct classification, at least for small and medium-size datasets.


M. Forghani-Elahabad, N. Mahdavi-Amiri,
Volume 4, Issue 2 (10-2013)
Abstract

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.
A Fakharzadeh, S Mahmoodi,
Volume 5, Issue 2 (10-2014)
Abstract

The traffic assignment problem is one of the most important problems for analyzing and optimizing the transportation network to find optimal flows. This study presented a new formulation based on a generalized Benders' decomposition approach to solve its important part, i.e. user equilibrium problems, in deterministic and stochastic cases. The new approach decomposed the problem into a master problem and a sub problem. The first one was a nonlinear and the latter a linear programming problem. Iteratively, the master problem was solved and its outputs were used to solve the sub-problem in which to form appropriate cuts and add them to the master problem for solving it in the next iteration. Based on the convergence of Benders' decomposition, the iterative process was terminated in a finite number of steps. In this manner, some numerical examples were explained and compared with other methods.


M Aman, J Tayyebi,
Volume 5, Issue 2 (10-2014)
Abstract

Given an instance of the minimum cost flow problem, a version of the corresponding inverse problem, called the capacity inverse problem, is to modify the upper and lower bounds on arc flows as little as possible so that a given feasible flow becomes optimal to the modified minimum cost flow problem. The modifications can be measured by different distances. In this article, we consider the capacity inverse problem under the bottleneck-type and the sum-type weighted Hamming distances. In the bottleneck-type case, the binary search technique is applied to present an algorithm for solving the problem in O(nm log n) time. In the sum-type case, it is shown that the inverse problem is strongly NP-hard even on bipartite networks


Somaiieh Rokhsari, Abolghasem Sadeghi-Niaraki,
Volume 6, Issue 2 (9-2015)
Abstract

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.
Dr Davood Shishebori,
Volume 7, Issue 1 (4-2016)
Abstract

We consider the reliable multi configuration capacitated logistics network design problem (RMCLNDP) with system disruptions, concerned with facilities locating, transportation links constructing, and also allocating their limited capacities to the customers in order to satisfy their demands with a minimum expected total cost (including locating costs, link constructing costs, as well as expected transshipment costs in usual and disruption conditions). The motivating application of this class of problem is in capacitated logistics network design with multi configuration (including multi-product, multi-vehicle, and multi-type link) regarding system disruptions simultaneously. The problem is modelled as a mixed integer program. Also, a hybrid heuristic algorithm is proposed. The algorithm, as an efficient approach, is a hybridization of sample average approximation, the LP relaxation, and a two stage decomposing heuristic. The results of a detailed comprehensive computational analysis are also reported. Computational experiments illustrate that the provided algorithm is able to substantially outperform the integer programming approach in terms of both finding and verifying the efficient optimal (or near optimal) solutions at reasonable processing times.


Mr. Mirmohammad Musavi, Dr. Reza Tavakkoli-Moghaddam, Ms. Farnaz Rayat,
Volume 8, Issue 1 (4-2017)
Abstract

We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.


Mr. Hassan Heidari-Fathian, Dr. Seyyed Hamid Reza Pasandideh,
Volume 8, Issue 1 (4-2017)
Abstract

A multi-periodic, multi-echelon green supply chain network consisting of manufacturing plants, potential distribution centers, and customers is developed. The manufacturing plants can provide the products in three modes including production in regular time, production in over time, or by subcontracting. The problem has three objectives including minimization of the total costs of the green supply chain network, maximization of the average safe inventory levels of the manufacturing plants and the distribution centers and minimization of the environmental impacts of the manufacturing plants in producing, holding and dispatching the products and also the environmental impacts of the distribution centers in holding and dispatching the products. The problem is first formulated as a mixed-integer mathematical model. Then, in order to solve the model, the augmented weighted Tchebycheff method is employed and its performance in producing the Pareto optimal solutions is compared with the goal attainment method.
Dr. Nader Ghaffarinasab, Dr. Y. Jabarzadeh, Mr. A. Motallebzadeh,
Volume 8, Issue 1 (4-2017)
Abstract

The hub location problems (HLP) constitute an important class of facility location problems that have been addressed by numerous operations researchers in recent years. HLP is a strategic problem frequently encountered in designing logistics and transportation networks. Here, we address the competitive multiple allocation HLP in a duopoly market. It is assumed that an incumbent firm (the leader) is operating an existing hub network in a market and an entrant firm (the follower) tries to enter the market by locating its own hubs aiming at capturing as much flow as possible from the leader. The customers choose one firm based on the service level (cost, time, distance, etc.) provided by the firm. We formulate the problem from the entrant firm’s point of view and propose an efficient tabu search based solution algorithm to solve it. Computational experiments show the capability of the proposed solution algorithm to obtain the optimal solutions in short computing times.
Dr. M. Fallah, Dr. Amir Mohajeri, Mr. Mahdi Jamshidi,
Volume 8, Issue 1 (4-2017)
Abstract

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.
Dr. Hamed Fazlollahtabar, Prof. Nezam Mahdavi-Amiri,
Volume 8, Issue 1 (4-2017)
Abstract

     This special issue is a collection of refereed articles selected from the 13th International Industrial Engineering Conference (IIEC 2017). The initial selection was made by Dr. Hamed Fazlollahtabar who also wrote the following description. The accepted articles were reviewed going through the usual reviewing process of IJOR.

Nezam Mahdavi-Amiri
Editor-in-Chief


     The 13th International Industrial Engineering Conference (IIEC 2017) hosted by Mazandaran University of Science and Technology, Babol, Iran, was held on 22nd and 23rd of February 2017 at Mizban Complex, Babolsar, Mazandaran, Iran. The total number of papers received was 805, among which 378 papers were accepted in two categories of oral presentations (185 papers) and poster presentations (193 papers). The scientific committee of the conference selected a number of papers to be extended and considered for a special issue of the Iranian Journal of Operations Research (IJOR). For this, 25 selected papers were considered and 17 papers were chosen for the second round of review. Having strict review criteria, 11 papers were then selected and refereed for a final decision. After the review process, 6 papers were finally accepted for the special issue. The contents of the accepted papers follow here. Vehicle routing and scheduling in an environmentally friendly manner attracted researchers to develop both mathematical programming models and heuristics as solution approaches. Green supply network design under uncertainty was considered for a multi-mode production system. Facility planning and hub location problem in competitive conditions and multiple allocations were investigated. Robust optimization philosophy as an uncertainty treatment approach was studied using event-driven and attribute-driven concepts. An interesting application of operations research in forestry was studied developing stochastic dynamic programming with Markov chains.

     My special gratitude goes to Professor Nezam Mahdavi-Amiri, Editor-in-chief, and the editorial board members of IJOR for their cooperation and support during the past 10 months of preparing this special issue.

Hamed Fazlollahtabar
Coordinator for Special Issues IIEC2017
Department of Industrial Engineering
School of Engineering, Damghan University
Damghan, Iran

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