Journal of Optimization The latest articles from Hindawi © 2017 , Hindawi Limited . All rights reserved. Metaheuristic Optimization: Algorithmic Design and Applications Tue, 05 Sep 2017 06:37:17 +0000 Gexiang Zhang, Linqiang Pan, Ferrante Neri, Maoguo Gong, and Alberto Leporati Copyright © 2017 Gexiang Zhang et al. All rights reserved. Robust Circle Detection Using Harmony Search Wed, 02 Aug 2017 00:00:00 +0000 Automatic circle detection is an important element of many image processing algorithms. Traditionally the Hough transform has been used to find circular objects in images but more modern approaches that make use of heuristic optimisation techniques have been developed. These are often used in large complex images where the presence of noise or limited computational resources make the Hough transform impractical. Previous research on the use of the Harmony Search (HS) in circle detection showed that HS is an attractive alternative to many of the modern circle detectors based on heuristic optimisers like genetic algorithms and simulated annealing. We propose improvements to this work that enables our algorithm to robustly find multiple circles in larger data sets and still work on realistic images that are heavily corrupted by noisy edges. Jaco Fourie Copyright © 2017 Jaco Fourie. All rights reserved. Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms Wed, 07 Jun 2017 06:57:40 +0000 Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters looking for candidate configurations close to a promising alternative. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach. Eduardo Batista de Moraes Barbosa and Edson Luiz França Senne Copyright © 2017 Eduardo Batista de Moraes Barbosa and Edson Luiz França Senne. All rights reserved. A Genetic Algorithm Based Approach for Solving the Minimum Dominating Set of Queens Problem Sun, 04 Jun 2017 09:21:26 +0000 In the field of computing, combinatorics, and related areas, researchers have formulated several techniques for the Minimum Dominating Set of Queens Problem (MDSQP) pertaining to the typical chessboard based puzzles. However, literature shows that limited research has been carried out to solve the MDSQP using bioinspired algorithms. To fill this gap, this paper proposes a simple and effective solution based on genetic algorithms to solve this classical problem. We report results which demonstrate that near optimal solutions have been determined by the GA for different board sizes ranging from 8 × 8 to 11 × 11. Saad Alharbi and Ibrahim Venkat Copyright © 2017 Saad Alharbi and Ibrahim Venkat. All rights reserved. A NNIA Scheme for Timetabling Problems Tue, 30 May 2017 00:00:00 +0000 This paper presents a memetic multiobjective optimization algorithm based on NNIA for examination timetabling problems. In this paper, the examination timetabling problem is considered as a two-objective optimization problem while it is modeled as a single-objective optimization problem generally. Within the NNIA framework, the special crossover operator is utilized to search in the solution space; two local search techniques are employed to optimize these two objectives and a diversity-keeping strategy which consists of an elitism group operator and an extension optimization operator to ensure a sufficient number of solutions in the pareto front. The proposed algorithm was tested on the most widely used uncapacitated Carter benchmarks. Experimental results prove that the proposed algorithm is a competitive algorithm. Yu Lei and Jiao Shi Copyright © 2017 Yu Lei and Jiao Shi. All rights reserved. A Novel Distributed Quantum-Behaved Particle Swarm Optimization Wed, 03 May 2017 06:44:26 +0000 Quantum-behaved particle swarm optimization (QPSO) is an improved version of particle swarm optimization (PSO) and has shown superior performance on many optimization problems. But for now, it may not always satisfy the situations. Nowadays, problems become larger and more complex, and most serial optimization algorithms cannot deal with the problem or need plenty of computing cost. Fortunately, as an effective model in dealing with problems with big data which need huge computation, MapReduce has been widely used in many areas. In this paper, we implement QPSO on MapReduce model and propose MapReduce quantum-behaved particle swarm optimization (MRQPSO) which achieves parallel and distributed QPSO. Comparisons are made between MRQPSO and QPSO on some test problems and nonlinear equation systems. The results show that MRQPSO could complete computing task with less time. Meanwhile, from the view of optimization performance, MRQPSO outperforms QPSO in many cases. Yangyang Li, Zhenghan Chen, Yang Wang, Licheng Jiao, and Yu Xue Copyright © 2017 Yangyang Li et al. All rights reserved. The Research of Disease Spots Extraction Based on Evolutionary Algorithm Wed, 03 May 2017 00:00:00 +0000 According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search. Kangshun Li, Lu Xiong, Dongbo Zhang, Zhengping Liang, and Yu Xue Copyright © 2017 Kangshun Li et al. All rights reserved. A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework Wed, 19 Apr 2017 06:33:33 +0000 Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism. Naeimeh Elkhani and Ravie Chandren Muniyandi Copyright © 2017 Naeimeh Elkhani and Ravie Chandren Muniyandi. All rights reserved. A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for Cooperative Air Combat DWTA Tue, 11 Apr 2017 00:00:00 +0000 A hybrid multiobjective discrete particle swarm optimization (HMODPSO) algorithm is proposed to solve cooperative air combat dynamic weapon target assignment (DWTA). First, based on the threshold of damage probability and time window constraints, a new cooperative air combat DWTA multiobjective optimization model is presented, which employs the maximum of the target damage efficiency and minimum of ammunition consumption as two competitive objective functions. Second, in order to tackle the DWTA problem, a mixed MODPSO and neighborhood search algorithm is proposed. Furthermore, the repairing operator is introduced into the mixed algorithm, which not only can repair infeasible solutions but also can improve the quality of feasible solutions. Besides, the Cauchy mutation is adopted to keep the diversity of the Pareto optimal solutions. Finally, a typical two-stage DWTA scenario is performed by HMODPSO and compared with three other state-of-the-art algorithms. Simulation results verify the effectiveness of the new model and the superiority of the proposed algorithm. Guang Peng, Yangwang Fang, Shaohua Chen, Weishi Peng, and Dandan Yang Copyright © 2017 Guang Peng et al. All rights reserved. An Improved Heuristic Algorithm for UCAV Path Planning Mon, 10 Apr 2017 00:00:00 +0000 The study of unmanned combat aerial vehicle (UCAV) path planning is increasingly important in military and civil field. This paper presents a new mathematical model and an improved heuristic algorithm based on Sparse Search (SAS) for UCAV path planning problem. In this paper, flight constrained conditions will be considered to meet the flight restrictions and task demands. With three simulations, the impacts of the model on the algorithms will be investigated, and the effectiveness and the advantages of the model and algorithm will be validated. Kun Zhang, Peipei Liu, Weiren Kong, Jie Zou, and Min Liu Copyright © 2017 Kun Zhang et al. All rights reserved. Maximum Lateness Scheduling on Two-Person Cooperative Games with Variable Processing Times and Common Due Date Thu, 06 Apr 2017 00:00:00 +0000 A new maximum lateness scheduling model in which both cooperative games and variable processing times exist simultaneously is considered in this paper. The job variable processing time is described by an increasing or a decreasing function dependent on the position of a job in the sequence. Two persons have to cooperate in order to process a set of jobs. Each of them has a single machine and their processing cost is defined as the minimum value of maximum lateness. All jobs have a common due date. The objective is to maximize the multiplication of their rational positive cooperative profits. A division of those jobs should be negotiated to yield a reasonable cooperative profit allocation scheme acceptable to them. We propose the sufficient and necessary conditions for the problems to have positive integer solution. Peng Liu and Xiaoli Wang Copyright © 2017 Peng Liu and Xiaoli Wang. All rights reserved. A Metaheuristic Algorithm Based on Chemotherapy Science: CSA Thu, 23 Feb 2017 12:27:35 +0000 Among scientific fields of study, mathematical programming has high status and its importance has led researchers to develop accurate models and effective solving approaches to addressing optimization problems. In particular, metaheuristic algorithms are approximate methods for solving optimization problems whereby good (not necessarily optimum) solutions can be generated via their implementation. In this study, we propose a population-based metaheuristic algorithm according to chemotherapy method to cure cancers that mainly search the infeasible region. As in chemotherapy, Chemotherapy Science Algorithm (CSA) tries to kill inappropriate solutions (cancers and bad cells of the human body); however, this would inevitably risk incidentally destroying some acceptable solutions (healthy cells). In addition, as the cycle of cancer treatment repeats over and over, the algorithm is iterated. To align chemotherapy process with the proposed algorithm, different basic terms and definitions including Infeasibility Function (IF), objective function (OF), Cell Area (CA), and Random Cells (RCs) are presented in this study. In the terminology of algorithms and optimization, IF and OF are mainly applicable as criteria to compare every pair of generated solutions. Finally, we test CSA and its structure using the benchmark Traveling Salesman Problem (TSP). Mohammad Hassan Salmani and Kourosh Eshghi Copyright © 2017 Mohammad Hassan Salmani and Kourosh Eshghi. All rights reserved. Power and Execution Time Optimization through Hardware Software Partitioning Algorithm for Core Based Embedded System Sun, 19 Feb 2017 00:00:00 +0000 Shortening the marketing cycle of the product and accelerating its development efficiency have become a vital concern in the field of embedded system design. Therefore, hardware/software partitioning has become one of the mainstream technologies of embedded system development since it affects the overall system performance. Given today’s largest requirement for great efficiency necessarily accompanied by high speed, our new algorithm presents the best version that can meet such unpreceded levels. In fact, we describe in this paper an algorithm that is based on HW/SW partitioning which aims to find the best tradeoff between power and latency of a system taking into consideration the dark silicon problem. Moreover, it has been tested and has shown its efficiency compared to other existing heuristic well-known algorithms which are Simulated Annealing, Tabu search, and Genetic algorithms. Siwar Ben Haj Hassine, Mehdi Jemai, and Bouraoui Ouni Copyright © 2017 Siwar Ben Haj Hassine et al. All rights reserved. Water Network Design Using a Multiobjective Real Options Framework Tue, 31 Jan 2017 08:50:20 +0000 Water distribution networks (WDNs) are an essential element of urban infrastructure. To achieve a good level of performance, the traditional design of WDNs based on expected future conditions should be replaced by a flexible design, using real options (ROs), that accounts for uncertainty by taking a broader view of possible future options. This work proposes a multiobjective ROs framework that sets out to reduce costs, minimize hydraulic pressure deficiency, and a third objective for minimizing carbon emissions. A multiobjective simulated annealing algorithm is used to identify the Pareto-optimal solutions, thus enabling a trade-off analysis between solutions. These trade-offs show that a low pressure deficit solution is achieved by increasing investment at a much faster rate after a certain pressure deficit threshold (60 m). Also, the pressure deficits can only be reduced by increasing carbon emissions. Finally, this work also emphasizes the importance of including carbon emissions as a specific objective by comparing the results of the proposed model and another one that did not cover the environmental objective. The results show that it is possible to reduce CO2 for the same level of capital expenditure or the same level of network pressure deficits if carbon emissions are minimized in the optimization process. João Marques, Maria Cunha, Dragan Savić, and Orazio Giustolisi Copyright © 2017 João Marques et al. All rights reserved. Modelling of Hydrothermal Unit Commitment Coordination Using Efficient Metaheuristic Algorithm: A Hybridized Approach Wed, 28 Dec 2016 08:33:20 +0000 In this paper, a novel approach of hybridization of two efficient metaheuristic algorithms is proposed for energy system analysis and modelling based on a hydro and thermal based power system in both single and multiobjective environment. The scheduling of hydro and thermal power is modelled descriptively including the handling method of various practical nonlinear constraints. The main goal for the proposed modelling is to minimize the total production cost (which is highly nonlinear and nonconvex problem) and emission while satisfying involved hydro and thermal unit commitment limitations. The cascaded hydro reservoirs of hydro subsystem and intertemporal constraints regarding thermal units along with nonlinear nonconvex, mixed-integer mixed-binary objective function make the search space highly complex. To solve such a complicated system, a hybridization of Gray Wolf Optimization and Artificial Bee Colony algorithm, that is, h-ABC/GWO, is used for better exploration and exploitation in the multidimensional search space. Two different test systems are used for modelling and analysis. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions. Suman Sutradhar, Nalin B. Dev Choudhury, and Nidul Sinha Copyright © 2016 Suman Sutradhar et al. All rights reserved. Bidirectional Nonnegative Deep Model and Its Optimization in Learning Wed, 16 Nov 2016 13:17:32 +0000 Nonnegative matrix factorization (NMF) has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF) with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL) model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms. Xianhua Zeng, Zhengyi He, Hong Yu, and Shengwei Qu Copyright © 2016 Xianhua Zeng et al. All rights reserved. An Analysis of Robustness Approaches for the Airport Baggage Sorting Station Assignment Problem Thu, 08 Sep 2016 13:41:19 +0000 In the Airport Baggage Sorting Station Assignment Problem (ABSSAP), the Baggage Sorting Stations (BSSs) are assigned to flights for the period of time necessary to perform their service for a given flights’ schedule. But the flights schedule may change on the day of operation which may deem the original assignment of some flights to BSSs infeasible. These changes may create conflicts between those flights whose schedules have changed and may not be restricted to those flights but propagating to the other flights for different reasons. Conflicts depend on the original assignments for the real arrival and departure flight times on the day of operation. It is therefore desirable to consider potential delays on the day of operation when generating the original flight assignments to BSSs, such that the final flight assignments differ little or do not differ at all from the original assignments on the day of operation. The term robustness is here used to give an indication of the degree to which this has been achieved. Some existing approaches originally presented in the Airport Gate Assignment Problem (AGAP) are adapted to the ABSSAP, other approaches are suggested for generating assignments which take account of potential perturbations on the day of operation for the ABSSAP, and all of them are then compared. It is shown that the suggested approaches by themselves do not perform better than the other considered approaches but when combined they enhance the result further compared to when each approach is used alone. Amadeo Ascó Copyright © 2016 Amadeo Ascó. All rights reserved. Finding the Efficiency Status and Efficient Projection in Multiobjective Linear Fractional Programming: A Linear Programming Technique Tue, 06 Sep 2016 12:43:51 +0000 Multiobjective linear fractional programming (MOLFP) problems are the important problems with special structures in multiobjective optimization. In the MOLFP problems, the objective functions are linear fractional functions and the constraints are linear; that is, the feasible set is a polyhedron. In this paper, we suggest a method to identify the efficiency status of the feasible solutions of an MOLFP problem. By the proposed method, an efficient projection on the efficient space for an inefficient solution is obtained. The proposed problems are constructed in linear programming structures. S. Morteza Mirdehghan and Hassan Rostamzadeh Copyright © 2016 S. Morteza Mirdehghan and Hassan Rostamzadeh. All rights reserved. Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem Tue, 23 Aug 2016 14:25:57 +0000 In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities from to with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity of . It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics. Ziauddin Ursani and David W. Corne Copyright © 2016 Ziauddin Ursani and David W. Corne. All rights reserved. Location of Farmers Warehouse at Adaklu Traditional Area, Volta Region, Ghana Sun, 31 Jul 2016 09:17:29 +0000 Postharvest loss is one major problem farmers in Adaklu Traditional Area that most Ghanaian farmers face. As a result, many farmers wallow in abject poverty. Warehouses are important facilities that help to reduce postharvest loss. In this research, Beresnev pseudo-Boolean Simple Plant Location Problem (SPLP) model is used to locate a warehouse at Adaklu Traditional Area, Volta Region, Ghana. This model was used because it gives a straightforward computation and produces no iteration as compared with other models. The SPLP is a problem of selecting a site from candidate sites to locate a plant so that customers can be supplied from the plant at a minimum cost. The model is made up of fixed cost and transportation cost. Location index ordering matrix was developed from the transportation cost matrix and we used it with the fixed cost and differences between variable costs to formulate the Beresnev function. Linear term developed from the function which was partial is pegged to obtain a complete solution. Of the 14 notable communities considered, Adaklu Waya is found most suitable for the setting of the warehouse. The total cost involved is Gh 78,180.00. Vincent Tulasi, Isaac Kwasi Adu, and Elikem Kofi Krampa Copyright © 2016 Vincent Tulasi et al. All rights reserved. Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method Sun, 31 Jul 2016 05:52:35 +0000 Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances. Rashida Adeeb Khanum, Muhammad Asif Jan, Nasser Mansoor Tairan, and Wali Khan Mashwani Copyright © 2016 Rashida Adeeb Khanum et al. All rights reserved. An Optimal SVM with Feature Selection Using Multiobjective PSO Mon, 18 Jul 2016 09:34:45 +0000 Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, , and the kernel factor, . Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM). The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks. Iman Behravan, Oveis Dehghantanha, Seyed Hamid Zahiri, and Nasser Mehrshad Copyright © 2016 Iman Behravan et al. All rights reserved. Evidence Maximization Technique for Training of Elastic Nets Thu, 30 Jun 2016 12:32:08 +0000 This paper presents a technique of evidence maximization for automatic tuning of regularization parameters of elastic nets, which allows tuning many parameters simultaneously. This technique was applied to handwritten digit recognition. Experiments showed its ability to train either models with high accuracy of recognition or highly sparse models with reasonable accuracy. Igor Dubnov, Alexander Merkov, Vladimir Arlazarov, and Ilia Nikolaev Copyright © 2016 Igor Dubnov et al. All rights reserved. The Vulnerability of Some Networks including Cycles via Domination Parameters Mon, 13 Jun 2016 10:50:02 +0000 Let be an undirected simple connected graph. A network is usually represented by an undirected simple graph where vertices represent processors and edges represent links between processors. Finding the vulnerability values of communication networks modeled by graphs is important for network designers. The vulnerability value of a communication network shows the resistance of the network after the disruption of some centers or connection lines until a communication breakdown. The domination number and its variations are the most important vulnerability parameters for network vulnerability. Some variations of domination numbers are the 2-domination number, the bondage number, the reinforcement number, the average lower domination number, the average lower 2-domination number, and so forth. In this paper, we study the vulnerability of cycles and related graphs, namely, fans, -pyramids, and -gon books, via domination parameters. Then, exact solutions of the domination parameters are obtained for the above-mentioned graphs. Tufan Turaci and Hüseyin Aksan Copyright © 2016 Tufan Turaci and Hüseyin Aksan. All rights reserved. Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase Wed, 08 Jun 2016 07:54:00 +0000 Essentially, strategic fleet planning is vital for airlines to yield a higher profit margin while providing a desired service frequency to meet stochastic demand. In contrast to most studies that did not consider slot purchase which would affect the service frequency determination of airlines, this paper proposes a novel approach to solve the fleet planning problem subject to various operational constraints. A two-stage fleet planning model is formulated in which the first stage selects the individual operating route that requires slot purchase for network expansions while the second stage, in the form of probabilistic dynamic programming model, determines the quantity and type of aircraft (with the corresponding service frequency) to meet the demand profitably. By analyzing an illustrative case study (with 38 international routes), the results show that the incorporation of slot purchase in fleet planning is beneficial to airlines in achieving economic and social sustainability. The developed model is practically viable for airlines not only to provide a better service quality (via a higher service frequency) to meet more demand but also to obtain a higher revenue and profit margin, by making an optimal slot purchase and fleet planning decision throughout the long-term planning horizon. Lay Eng Teoh and Hooi Ling Khoo Copyright © 2016 Lay Eng Teoh and Hooi Ling Khoo. 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.