Journal of Optimization The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Prioritization of the Factors Affecting Bank Efficiency Using Combined Data Envelopment Analysis and Analytical Hierarchy Process Methods Tue, 10 May 2016 13:36:36 +0000 Bank branches have a vital role in the economy of all countries. They collect assets from various sources and put them in the hand of those sectors that need liquidity. Due to the limited financial and human resources and capitals and also because of the unlimited and new customers’ needs and strong competition between banks and financial and credit institutions, the purpose of this study is to provide an answer to the question of which of the factors affecting performance, creating value, and increasing shareholder dividends are superior to others and consequently managers should pay more attention to them. Therefore, in this study, the factors affecting performance (efficiency) in the areas of management, personnel, finance, and customers were segmented and obtained results were ranked using both methods of Data Envelopment Analysis and hierarchical analysis. In both of these methods, the leadership style in the area of management; the recruitment and resource allocation in the area of financing; the employees’ satisfaction, dignity, and self-actualization in the area of employees; and meeting the new needs of customers got more weights. Mehdi Fallah Jelodar Copyright © 2016 Mehdi Fallah Jelodar. All rights reserved. A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems Tue, 12 Apr 2016 12:50:03 +0000 Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex and -hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness. Hamed Piroozfard, Kuan Yew Wong, and Adnan Hassan Copyright © 2016 Hamed Piroozfard et al. All rights reserved. Optimization of Conductive Thin Film Epoxy Composites Properties Using Desirability Optimization Methodology Thu, 25 Feb 2016 16:47:12 +0000 Multiwalled carbon nanotubes (MWCNTs)/epoxy thin film nanocomposites were prepared using spin coating technique. The effects of process parameters such as sonication duration (5–35 min) and filler loadings (1-2 vol%) were studied using the design of experiment (DOE). Full factorial design was used to create the design matrix for the two factors with three-level experimentation, resulting in a total of 9 runs () of experimentation. Response surface methodology (RSM) combined with E.C. Harrington’s desirability function called desirability optimization methodology (DOM) was used to optimize the multiple properties (tensile strength, elastic modulus, elongation at break, thermal conductivity, and electrical conductivity) of MWCNTs/epoxy thin film composites. Based on response surface analysis, quadratic model was developed. Analysis of variance (ANOVA), -squared (-Sq), and normal plot of residuals were applied to determine the accuracy of the models. The range of lower and upper limits was determined in an overlaid contour plot. Desirability function was used to optimize the multiple responses of MWCNTs/epoxy thin film composites. A global solution of 12.88 min sonication and 1.67 vol% filler loadings was obtained to have maximum desired responses with composite desirability of 1. C. P. Khor, Mariatti bt Jaafar, and Sivakumar Ramakrishnan Copyright © 2016 C. P. Khor et al. All rights reserved. A Hybrid Dynamic Programming for Solving Fixed Cost Transportation with Discounted Mechanism Mon, 22 Feb 2016 16:46:36 +0000 The problem of allocating different types of vehicles for transporting a set of products from a manufacturer to its depots/cross docks, in an existing transportation network, to minimize the total transportation costs, is considered. The distribution network involves a heterogeneous fleet of vehicles, with a variable transportation cost and a fixed cost in which a discount mechanism is applied on the fixed part of the transportation costs. It is assumed that the number of available vehicles is limited for some types. A mathematical programming model in the form of the discrete nonlinear optimization model is proposed. A hybrid dynamic programming algorithm is developed for finding the optimal solution. To increase the computational efficiency of the solution algorithm, several concepts and routines, such as the imbedded state routine, surrogate constraint concept, and bounding schemes, are incorporated in the dynamic programming algorithm. A real world case problem is selected and solved by the proposed solution algorithm, and the optimal solution is obtained. Farhad Ghassemi Tari Copyright © 2016 Farhad Ghassemi Tari. All rights reserved. On Fuzzy Multiobjective Multi-Item Solid Transportation Problem Wed, 25 Mar 2015 08:57:30 +0000 A transportation problem involving multiple objectives, multiple products, and three constraints, namely, source, destination, and conveyance, is called the multiobjective multi-item solid transportation problem (MOMISTP). Recently, Kundu et al. (2013) proposed a method to solve an unbalanced MOMISTP. In this paper, we suggest a method, which first converts an unbalanced problem to a balanced one. In one case of an example, while the method proposed by Kundu et al. concludes infeasibility, our method gives a feasible solution. Deepika Rani, T. R. Gulati, and Amit Kumar Copyright © 2015 Deepika Rani et al. All rights reserved. A Structural Optimization Framework for Multidisciplinary Design Thu, 05 Feb 2015 06:53:07 +0000 This work describes the development of a structural optimization framework adept at accommodating diverse customer requirements. The purpose is to provide a framework accessible to the optimization research analyst. The framework integrates the method of moving asymptotes into the finite element analysis program (FEAP) by exploiting the direct interface capability in FEAP. Analytic sensitivities are incorporated to provide a robust and efficient optimization search. User macros are developed to interface the design algorithm and analytic sensitivity with the finite element analysis program. To test the optimization tool and sensitivity calculations, three sizing and one topology optimization problems are considered. In addition, flutter analysis of a heated panel is analyzed as an example of coupling to nonstructural discipline. In sizing optimization, the calculated semianalytic sensitivities match analytic and finite difference calculations. Differences between analytic designs and numerical ones are less than 2.0% and are attributed to discrete nature of finite elements. In the topology problem, quadratic elements are found robust at resolving checkerboard patterns. Mohammad Kurdi Copyright © 2015 Mohammad Kurdi. All rights reserved. Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation Sat, 31 Jan 2015 11:41:18 +0000 Tool vibration and surface roughness are two important parameters which affect the quality of the component and tool life which indirectly affect the component cost. In this paper, the effect of cutting parameters on tool vibration, and surface roughness has been investigated during end milling of EN-31 tool steel. Response surface methodology (RSM) has been used to develop mathematical model for predicting surface finish, tool vibration and tool wear with different combinations of cutting parameters. The experimental results show that feed rate is the most dominating parameter affecting surface finish, whereas cutting speed is the major factor effecting tool vibration. The results of mathematical model are in agreement with experimental investigations done to validate the mathematical model. Sukhdev S. Bhogal, Charanjeet Sindhu, Sukhdeep S. Dhami, and B. S. Pabla Copyright © 2015 Sukhdev S. Bhogal et al. All rights reserved. Constraint Consensus Methods for Finding Strictly Feasible Points of Linear Matrix Inequalities Thu, 08 Jan 2015 12:54:25 +0000 We give algorithms for solving the strict feasibility problem for linear matrix inequalities. These algorithms are based on John Chinneck’s constraint consensus methods, in particular, the method of his original paper and the modified DBmax constraint consensus method from his paper with Ibrahim. Our algorithms start with one of these methods as “Phase 1.” Constraint consensus methods work for any differentiable constraints, but we take advantage of the structure of linear matrix inequalities. In particular, for linear matrix inequalities, the crossing points of each constraint boundary with the consensus ray can be calculated. In this way we check for strictly feasible points in “Phase 2” of our algorithms. We present four different algorithms, depending on whether the original (basic) or DBmax constraint consensus vector is used in Phase 1 and, independently, in Phase 2. We present results of numerical experiments that compare the four algorithms. The evidence suggests that one of our algorithms is the best, although none of them are guaranteed to find a strictly feasible point after a given number of iterations. We also give results of numerical experiments indicating that our best method compares favorably to a new variant of the method of alternating projections. Shafiu Jibrin and James W. Swift Copyright © 2015 Shafiu Jibrin and James W. Swift. All rights reserved. Ranking All DEA-Efficient DMUs Based on Cross Efficiency and Analytic Hierarchy Process Methods Mon, 05 Jan 2015 06:47:43 +0000 The aim of this paper is to present an original approach for ranking of DEA-efficient DMUs based on the cross efficiency and analytic hierarchy process (AHP) methods. The approach includes two basic stages. In the first stage using DEA models the cross efficiency value of each DEA-efficiency DMU is specified. In the second stage, the pairwise comparison matrix generated in the first stage is utilized to rank scale of the units via the one-step process of AHP. The advantage of this proposed method is its capability of ranking extreme and nonextreme DEA-efficient DMUs. The numerical examples are presented in this paper and we compare our approach with some other approaches. Dariush Akbarian Copyright © 2015 Dariush Akbarian. All rights reserved. Multi-Objective Optimization of Two-Stage Helical Gear Train Using NSGA-II Sun, 30 Nov 2014 00:10:10 +0000 Gears not only transmit the motion and power satisfactorily but also can do so with uniform motion. The design of gears requires an iterative approach to optimize the design parameters that take care of kinematics aspects as well as strength aspects. Moreover, the choice of materials available for gears is limited. Owing to the complex combinations of the above facts, manual design of gears is complicated and time consuming. In this paper, the volume and load carrying capacity are optimized. Three different methodologies (i) MATLAB optimization toolbox, (ii) genetic algorithm (GA), and (iii) multiobjective optimization (NSGA-II) technique are used to solve the problem. In the first two methods, volume is minimized in the first step and then the load carrying capacities of both shafts are calculated. In the third method, the problem is treated as a multiobjective problem. For the optimization purpose, face width, module, and number of teeth are taken as design variables. Constraints are imposed on bending strength, surface fatigue strength, and interference. It is apparent from the comparison of results that the result obtained by NSGA-II is more superior than the results obtained by other methods in terms of both objectives. R. C. Sanghvi, A. S. Vashi, H. P. Patolia, and R. G. Jivani Copyright © 2014 R. C. Sanghvi et al. All rights reserved. Ordering Cost Reduction in Inventory Model with Defective Items and Backorder Price Discount Wed, 12 Nov 2014 12:02:03 +0000 In the real market, as unsatisfied demands occur, the longer the length of lead time is, the smaller the proportion of backorder would be. In order to make up for the inconvenience and even the losses of royal and patient customers, the supplier may offer a backorder price discount to secure orders during the shortage period. Also, ordering policies determined by conventional inventory models may be inappropriate for the situation in which an arrival lot contains some defective items. To compensate for the inconvenience of backordering and to secure orders, the supplier may offer a price discount on the stockout item. The purpose of this study is to explore a coordinated inventory model including defective arrivals by allowing the backorder price discount and ordering cost as decision variables. There are two inventory models proposed in this paper, one with normally distributed demand and another with distribution free demand. A computer code using the software Matlab 7.0 is developed to find the optimal solution and present numerical examples to illustrate the models. The results in the numerical examples indicate that the savings of the total cost are realized through ordering cost reduction and backorder price discount. Karuppuchamy Annadurai and Ramasamy Uthayakumar Copyright © 2014 Karuppuchamy Annadurai and Ramasamy Uthayakumar. All rights reserved. A New Measure for Detecting Influential DMUs in DEA Thu, 16 Oct 2014 14:13:16 +0000 We discuss about influential DMUs and also we review some related studies in the literature. Then we propose a new method to detect influential DMUs, which is based on Euclidean distance and omitting the efficient DMUs by using single case deletion. Our method will be explained with an empirical example. Irmak Acarlar, Harun Kınacı, and Vadoud Najjari Copyright © 2014 Irmak Acarlar et al. All rights reserved. Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine Mon, 22 Sep 2014 07:30:16 +0000 This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, , and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO) was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller. Bambang Wahono and Harutoshi Ogai Copyright © 2014 Bambang Wahono and Harutoshi Ogai. All rights reserved. Multiobjective Optimization Involving Quadratic Functions Mon, 15 Sep 2014 08:56:47 +0000 Multiobjective optimization is nowadays a word of order in engineering projects. Although the idea involved is simple, the implementation of any procedure to solve a general problem is not an easy task. Evolutionary algorithms are widespread as a satisfactory technique to find a candidate set for the solution. Usually they supply a discrete picture of the Pareto front even if this front is continuous. In this paper we propose three methods for solving unconstrained multiobjective optimization problems involving quadratic functions. In the first, for biobjective optimization defined in the bidimensional space, a continuous Pareto set is found analytically. In the second, applicable to multiobjective optimization, a condition test is proposed to check if a point in the decision space is Pareto optimum or not and, in the third, with functions defined in n-dimensional space, a direct noniterative algorithm is proposed to find the Pareto set. Simple problems highlight the suitability of the proposed methods. Oscar Brito Augusto, Fouad Bennis, and Stephane Caro Copyright © 2014 Oscar Brito Augusto et al. All rights reserved. Two-Dimensional IIR Filter Design Using Simulated Annealing Based Particle Swarm Optimization Tue, 09 Sep 2014 11:35:11 +0000 We present a novel hybrid algorithm based on particle swarm optimization (PSO) and simulated annealing (SA) for the design of two-dimensional recursive digital filters. The proposed method, known as SA-PSO, integrates the global search ability of PSO with the local search ability of SA and offsets the weakness of each other. The acceptance criterion of Metropolis is included in the basic algorithm of PSO to increase the swarm’s diversity by accepting sometimes weaker solutions also. The experimental results reveal that the performance of the optimal filter designed by the proposed SA-PSO method is improved. Further, the convergence behavior as well as optimization accuracy of proposed method has been improved significantly and computational time is also reduced. In addition, the proposed SA-PSO method also produces the best optimal solution with lower mean and variance which indicates that the algorithm can be used more efficiently in realizing two-dimensional digital filters. Supriya Dhabal and Palaniandavar Venkateswaran Copyright © 2014 Supriya Dhabal and Palaniandavar Venkateswaran. All rights reserved. Optimal and Suboptimal Resource Allocation in MIMO Cooperative Cognitive Radio Networks Sun, 31 Aug 2014 08:41:54 +0000 The core aim of this work is the maximization of the achievable data rate of the secondary user pairs (SU pairs), while ensuring the QoS of primary users (PUs). All users are assumed to be equipped with multiple antennas. It is assumed that when PUs are present, the direct communication between SU pairs introduces intolerable interference to PUs and thereby SUs transmit signal using the cooperation of one of the SUs and avoid transmission in the direct channel. In brief, an adaptive cooperative strategy for MIMO cognitive radio networks is proposed. At the presence of PUs, the issue of joint relay selection and power allocation in underlay MIMO cooperative cognitive radio networks (U-MIMO-CCRN) is addressed. The optimal approach for determining the power allocation and the cooperating SU is proposed. Besides, the outage probability of the proposed system is further derived. Due to high complexity of the optimal approach, a low complexity approach is further proposed and its performance is evaluated using simulations. The simulation results reveal that the performance loss due to the low complexity approach is only about 14%, while the complexity is greatly reduced. Mehdi Ghamari Adian and Mahin Ghamari Adyan Copyright © 2014 Mehdi Ghamari Adian and Mahin Ghamari Adyan. All rights reserved. An Improved Laguerre-Samuelson Inequality of Chebyshev-Markov Type Tue, 12 Aug 2014 11:37:54 +0000 The Chebyshev-Markov extremal distributions by known moments to order four are used to improve the Laguerre-Samuelson inequality for finite real sequences. In general, the refined bound depends not only on the sample size but also on the sample skewness and kurtosis. Numerical illustrations suggest that the refined inequality can almost be attained for randomly distributed completely symmetric sequences from a Cauchy distribution. Werner Hürlimann Copyright © 2014 Werner Hürlimann. All rights reserved. Equal Angle Distribution of Polling Directions in Direct-Search Methods Sun, 20 Jul 2014 11:18:47 +0000 The purpose of this paper is twofold: first, to introduce deterministic strategies for directional direct-search methods, including new instances of the mesh adaptive direct-search (MADS) and the generating set search (GSS) class of algorithms, which utilize a nice distribution of PoLL directions when compared to other strategies, and second, to introduce variants of each algorithm which utilize a minimal positive basis at each step. The strategies base their PoLL directions on the use of the QR decomposition to obtain an orthogonal set of directions or on using the equal angular directions from a regular simplex centered at the origin with vertices on the unit sphere. Test results are presented on a set of smooth, nonsmooth, unconstrained, and constrained problems that give comparisons between the various implementations of these directional direct-search methods. Benjamin Van Dyke Copyright © 2014 Benjamin Van Dyke. All rights reserved. An Optimization-Based Approach to Calculate Confidence Interval on Mean Value with Interval Data Sun, 13 Jul 2014 07:23:26 +0000 In this paper, we propose a methodology for construction of confidence interval on mean values with interval data for input variable in uncertainty analysis and design optimization problems. The construction of confidence interval with interval data is known as a combinatorial optimization problem. Finding confidence bounds on the mean with interval data has been generally considered an NP hard problem, because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimization to find the confidence interval on mean values with interval data. With numerical experimentation, we show that the proposed confidence bound algorithms are scalable in polynomial time with respect to increasing number of intervals. Several sets of interval data with different numbers of intervals and type of overlap are presented to demonstrate the proposed methods. As against the current practice for the design optimization with interval data that typically implements the constraints on interval variables through the computation of bounds on mean values from the sampled data, the proposed approach of construction of confidence interval enables more complete implementation of design optimization under interval uncertainty. Kais Zaman and Saraf Anika Kritee Copyright © 2014 Kais Zaman and Saraf Anika Kritee. All rights reserved. Beamforming with Reduced Complexity in MIMO Cooperative Cognitive Radio Networks Tue, 06 May 2014 12:11:27 +0000 An approach for beamforming with reduced complexity in MIMO cooperative cognitive radio networks (MIMO-CCRN) is presented. Specifically, a suboptimal approach with reduced complexity is proposed to jointly determine the transmit beamforming (TB) and cooperative beamforming (CB) weight vectors along with antenna subset selection in MIMO-CCRN. Two multiantenna secondary users (SU) constitute the desired link, one acting as transmitter (SU TX) and the other as receiver (SU RX) and they coexist with single-antenna primary and secondary users. Some of single antenna secondary users are recruited by desired link as cooperative relay. The maximization of the achievable rates in the desired link is the objective of this work, provided to interference constraints on the primary users are not violated. The objective is achieved by exploiting transmit beamforming at SU TX, cooperation of some secondary users, and cooperative beamforming. Meanwhile, the costs associated with RF chains at the radio front end at SU RX are reduced. Through simulations, it is shown that better performance in the desired link is attained, as a result of cooperation of SUs. Mehdi Ghamari Adian Copyright © 2014 Mehdi Ghamari Adian. All rights reserved. Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems Wed, 19 Feb 2014 14:20:15 +0000 A polymorphic uncertain linear programming (PULP) model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems. Xinbo Zhang, Feng Zhang, Xiaohong Chen, and Zhong Wan Copyright © 2014 Xinbo Zhang et al. All rights reserved. A Nonmonotone Adaptive Trust Region Method Based on Conic Model for Unconstrained Optimization Mon, 27 Jan 2014 07:57:11 +0000 We propose a nonmonotone adaptive trust region method for unconstrained optimization problems which combines a conic model and a new update rule for adjusting the trust region radius. Unlike the traditional adaptive trust region methods, the subproblem of the new method is the conic minimization subproblem. Moreover, at each iteration, we use the last and the current iterative information to define a suitable initial trust region radius. The global and superlinear convergence properties of the proposed method are established under reasonable conditions. Numerical results show that the new method is efficient and attractive for unconstrained optimization problems. Zhaocheng Cui Copyright © 2014 Zhaocheng Cui. All rights reserved. Determining Bounds on Assumption Errors in Operational Analysis Sun, 12 Jan 2014 00:00:00 +0000 The technique of operational analysis (OA) is used in the study of systems performance, mainly for estimating mean values of various measures of interest, such as, number of jobs at a device and response times. The basic principles of operational analysis allow errors in assumptions to be quantified over a time period. The assumptions which are used to derive the operational analysis relationships are studied. Using Karush-Kuhn-Tucker (KKT) conditions bounds on error measures of these OA relationships are found. Examples of these bounds are used for representative performance measures to show limits on the difference between true performance values and those estimated by operational analysis relationships. A technique for finding tolerance limits on the bounds is demonstrated with a simulation example. Neal M. Bengtson Copyright © 2014 Neal M. Bengtson. All rights reserved. Memetic Algorithm with Local Search as Modified Swine Influenza Model-Based Optimization and Its Use in ECG Filtering Thu, 02 Jan 2014 11:16:46 +0000 The Swine Influenza Model Based Optimization (SIMBO) family is a newly introduced speedy optimization technique having the adaptive features in its mechanism. In this paper, the authors modified the SIMBO to make the algorithm further quicker. As the SIMBO family is faster, it is a better option for searching the basin. Thus, it is utilized in local searches in developing the proposed memetic algorithms (MAs). The MA has a faster speed compared to SIMBO with the balance in exploration and exploitation. So, MAs have small tradeoffs in convergence velocity for comprehensively optimizing the numerical standard benchmark test bed having functions with different properties. The utilization of SIMBO in the local searching is inherently the exploitation of better characteristics of the algorithms employed for the hybridization. The developed MA is applied to eliminate the power line interference (PLI) from the biomedical signal ECG with the use of adaptive filter whose weights are optimized by the MA. The inference signal required for adaptive filter is obtained using the selective reconstruction of ECG from the intrinsic mode functions (IMFs) of empirical mode decomposition (EMD). Devidas G. Jadhav, Shyam S. Pattnaik, and Sanjoy Das Copyright © 2014 Devidas G. Jadhav et al. All rights reserved. Nondifferentiable Minimax Programming Problems in Complex Spaces Involving Generalized Convex Functions Sun, 22 Dec 2013 10:14:20 +0000 We start our discussion with a class of nondifferentiable minimax programming problems in complex space and establish sufficient optimality conditions under generalized convexity assumptions. Furthermore, we derive weak, strong, and strict converse duality theorems for the two types of dual models in order to prove that the primal and dual problems will have no duality gap under the framework of generalized convexity for complex functions. Anurag Jayswal, Ashish Kumar Prasad, and Krishna Kummari Copyright © 2013 Anurag Jayswal et al. All rights reserved. Application of Fuzzy DEMATEL in Risks Evaluation of Knowledge-Based Networks Mon, 16 Dec 2013 15:41:07 +0000 Developing new products has received much attention within the last decades. This issue can be highlighted for strategic innovations, in particular. Recently, knowledge-based networks have been introduced in order to facilitate the affair of transforming knowledge into commercial products which can be regarded as a set of research centers, universities, knowledge intermediaries, customers, and so forth. However, there is a wide range of risk factors that are liable to affect the chain performance. Hence, this paper aims to consider the most influencing criteria that can play a more significant role in achievements of such networks. To do so, DEMATEL has been applied to take the relationships between the risk factors into account. Moreover, fuzzy set theory has been utilized in order to deal with the linguistic variables. Finally, the most important factors are identified and their relations are determined. M. Abbasi, R. Hosnavi, and B. Tabrizi Copyright © 2013 M. Abbasi et al. All rights reserved. Double Flight-Modes Particle Swarm Optimization Mon, 16 Dec 2013 08:49:06 +0000 Getting inspiration from the real birds in flight, we propose a new particle swarm optimization algorithm that we call the double flight modes particle swarm optimization (DMPSO) in this paper. In the DMPSO, each bird (particle) can use both rotational flight mode and nonrotational flight mode to fly, while it is searching for food in its search space. There is a King in the swarm of birds, and the King controls each bird’s flight behavior in accordance with certain rules all the time. Experiments were conducted on benchmark functions such as Schwefel, Rastrigin, Ackley, Step, Griewank, and Sphere. The experimental results show that the DMPSO not only has marked advantage of global convergence property but also can effectively avoid the premature convergence problem and has good performance in solving the complex and high-dimensional optimization problems. Wang Yong, Li Jing-yang, and Li Chun-lei Copyright © 2013 Wang Yong et al. All rights reserved. Dynamic Optimization Technique for Distribution of Goods with Stochastic Shortages Sun, 15 Dec 2013 14:52:15 +0000 This work considers the distribution of goods with stochastic shortages from factories to stores. It is assumed that in the process of shipping the goods to various stores, some proportion of the goods will be damaged (which will lead to shortage of goods in transit). The cost of the damaged goods is added to the cost of the shipment. A proportion of the total expected cost of the shortage goods is assumed to be recovered and should be deducted from the total cost of the shipment. In order to determine the minimum transportation costs for the operation, we adopt dynamic optimization principles. The optimal transportation cost and optimal control policies of shipping the goods from factories to stores were obtained. We find that the optimal costs of the goods recovered could be determined. It was further found that the optimum costs of distributing the goods with minimum and maximum error bounds coincide only at infinity. Charles I. Nkeki Copyright © 2013 Charles I. Nkeki. All rights reserved. Quasiconvex Semidefinite Minimization Problem Thu, 12 Dec 2013 15:39:03 +0000 We introduce so-called semidefinite quasiconvex minimization problem. We derive new global optimality conditions for the above problem. Based on the global optimality conditions, we construct an algorithm which generates a sequence of local minimizers which converge to a global solution. R. Enkhbat and T. Bayartugs Copyright © 2013 R. Enkhbat and T. Bayartugs. All rights reserved. Multiobjective Optimization Using Cross-Entropy Approach Tue, 10 Dec 2013 12:25:02 +0000 A new approach for multiobjective optimization is proposed in this paper. The method based on the cross-entropy method for single objective optimization (SO) is adapted to MO optimization by defining an adequate sorting criterion for selecting the best candidates samples. The selection is made by the nondominated sorting concept and crowding distance operator. The effectiveness of the approach is tested on several academic problems (e.g., Schaffer, Fonseca, Fleming, etc.). Its performances are compared with those of other multiobjective algorithms. Simulation results and comparisons based on several performance metrics demonstrate the effectiveness of the proposed method. Karim Sebaa, Abdelhalim Tlemçani, Mounir Bouhedda, and Noureddine Henini Copyright © 2013 Karim Sebaa et al. All rights reserved.