CHAPTER 2
LITERATURE SURVEY
2.1 INTRODUCTION
This chapter describes the cell formation in the cellular manufacturing system. That can be done to evaluate the different algorithm. They also give an account of the brief history of earlier works with a detailed description of the core algorithms, highlighting the significant contributions of the different algorithms put forth by various authors. This research work aims at giving an overview of the previous researches done in this field, evaluating the current status of the job done and envisioning gaps in the current knowledge. The concept of cellular manufacturing system has also been explained. It also describes the principles of operation of cell formation their characteristics considerations, and the techniques that are used to manufacturing for the maximum grouping efficiency and minimum computational time.
2.3 A HYBRID SWARM BASED OPTIMIZATION APPROACH FOR SOLVING CELL FORMATION PROBLEM
(Mehdizadeh and Rahimi 2016) to present an integrated mathematical model to solve the dynamic cell formation problem considering operator assignment and inter/intra cell layouts problems with machine duplication, simultaneously. The proposed model includes three objectives which the first objective seeks to minimize inter/intra cell part movements and machine relocation; the second objective minimizes machine and operator related costs and the third objective maximizes consecutive forward flows ratio. To validate the proposed model, a numerical example is presented and solved by the sum weighted method. Due to the NP-hardness of the model, two meta-heuristics, namely multi-objective simulated annealing (MOSA) and multi-objective vibration-damping optimization (MOVDO) present to solve the proposed model. Finally, two algorithms have been compared using multi-objective criteria.
(Rajesh, Krishna et al. 2017) has focuses on Cellular Manufacturing Systems (CMS) that is based on Group Technology (GT) concepts. Cell Formation (CF) is one of the essential steps in the design of CMS. The main objective of CF is to group machines and parts into cells. This paper proposes a new approach to obtain CF based on the similarity coefficient considering similarities and dissimilarities in CMS. This approach overcomes many disadvantages inherent in some traditional methods like Rank order clustering (ROC).
Moreover, a comparison between the proposed method and one of the well known traditional approaches ROC has been conducted. Percentage of Exceptional Elements (PEE), Machine Utilization (MU), Grouping Efficacy (GE) and Cell Efficiency (CE) are considered as performance measures. The results obtained are better than the results of ROC. A Java Programmed is developed for the proposed method which will solve any size of CF problem within 5 seconds.
(Laha and Hazarika 2017) Cellular manufacturing system (CMS) is an example of an application of group technology in manufacturing systems. The cell formation problem (CFP) in CMS aims to identify part families and machine cells to minimize the intercellular movement and to maximize the machine utilization within a cell. The previous study in CFP generally motivated on maximizing grouping efficacy (GC) by minimizing exceptional elements as well as void elements. In this paper, a heuristic approach based on Euclidean Distance matrix is proposed. Computational experiments were performed with 20 benchmark problem sets taken from the literature. Computational results demonstrate that the performances of the proposed heuristic in terms of GC are either better than or competitive with the well-known existing algorithms.
(Li, Baki et al. 2010) has proposed an ant colony optimization metaheuristic (ACO-CF) to solve the machine–part cell formation problem. ACO-CF is a MAX–MIN ant system, which is implemented in the hyper-cube framework to automatically scale the objective functions of machine–part cell formation problems. As an intensification strategy, we integrate an iteratively local search into ACO-CF. Based on the assignment of the machines or parts, the local search can optimally reassign parts or machines to cells. We carry out a series of experiments to investigate the performance of ACO-CF on some standard benchmark problems. The comparison study between ACO-CF and other methods proposed in the literature indicates that ACO-CF is one of the best approaches for solving the machine–part cell formation problem.
(Pailla, Trindade et al. 2010) The cell formation problem is a crucial component of a cell production design in a manufacturing system. This problem consists of a set of product parts to be manufactured in a group of machines. The objective is to build manufacturing clusters by associating part families with machine cells, with the aim of minimizing the inter-cellular movements of parts by grouping efficacy measures. We present two approaches to solve the cell formation problem. First, we offer an evolutionary algorithm that improves the efficiency of the standard genetic algorithm by considering cooperation with a local search around some of the solutions it visits. Second, we present an approach based on simulated annealing that uses the same representation scheme of a feasible solution. To evaluate the performance of both algorithms, we used a known set of CFP instances. We compared the results of both algorithms with the results of five other algorithms from the literature. In eight out of 36 cases we considered, the evolutionary method outperformed the previous results of different evolutionary algorithms, and in 26 cases it found the same best solutions. On the other hand, simulated annealing not only found the best previously known solutions, but they also found better solutions than existing ones for various problems.
(Mahdavi, Teymourian et al. 2013) Cellular manufacturing system (CMS) is one of the group technology (GT) usages. Among the necessary decisions for a successful CMS implementation, cell formation problem (CFP) and cell layout problem (CLP) are the two most popular ones. The majority of past studies in CMS discussed on CFPs and some of those focused on CLP ones. A few researchers solve the CPF and CLP simultaneously. In this paper, we present a new integrated mathematical model considering cell formation and cell layout simultaneously. The goal of our model is to group similar parts and corresponding different machines in the same cells. Machines sequence in each cell and cell positions is also specified in the system.
Moreover, our proposed model considers forward and backtracking movements as well as new assumptions for distances between cells using sequence data and production volume. One appropriate adjusted measure from the literature and two new measures of performance for evaluating solutions are defined. To validate the model, two well-known critical benchmark examples are employed. Computational experiments demonstrate that our proposal is a proficient model and show the effectiveness of our implementation.
(Noktehdan, Karimi et al. 2010) Cellular manufacturing (CM) is an essential application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differential evolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored to a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature.
(Ulutas 2015) Cellular manufacturing design is concerned with the creation and operation of manufacturing cells to take advantage of flexibility, efficient flow and high production rate. Cell formation problem (CFP) is the assignment of part types and machines to specific cells based on their similarity. Several exact and heuristic methods are provided in the literature to solve the problem, and test problems in research are commonly used for Comparison. This study presents a Clonal Selection Algorithm (CSA) to solve a classical CFP that outperforms currently available heuristics in the literature. The number of cells may be critical in the environments where cell formation costs are high, and singletons occur in design. It is concluded that the CFP results should be assessed not only based on efficacy values but also the number of cells.
(Feng, Da et al. 2017) Both cell design and social issues are essential factors for successful implementation of cellular manufacturing. To better implement cellular production, we investigate the integrated cell formation and worker assignment problem (ICFWAP). A comprehensive linear model is developed for the ICFWAP to determine the optimal allocation of machines, parts and workers. Specific characteristics of this model include the simultaneous consideration of production planning, the coexistence of alternative process routings, lot splitting, workload balancing between cells and worker over-assignment to multiple cells. Motivated by the inefficiency of exact approaches, this paper proposes a hybrid approach combining combinatorial particle swarm optimization and linear programming (CPSO-LP) to solve real-sized problems efficiently. In CPSO-LP, decision variables corresponding to part routing selection and part operation assignment are fixed, and other variables are allowed to be changed. CPLEX is then used to solve the reduced LP problem. Numerical experiments validate the proposed model. Results reveal that worker over-assignment can reduce the number of workers hired and improve labour utilization rate. The better efficiency and effectiveness of CPSO-LP are proved by comparisons with CPLEX, a genetic algorithm (GA), CPSO, and a hybrid approach combining GA and LP.
(Bychkov and Batsyn 2018) The Cell Formation Problem has been studied as an optimization problem in manufacturing for more than 90 years. It consists of grouping machines and parts into manufacturing cells to maximize the loading of cells and minimize movement of parts from one cell to another. Many heuristic algorithms have been proposed, which are doing well even for large-sized instances. However, only a few authors have aimed to develop exact methods, and most of these methods have some significant restrictions such as a fixed number of production cells, for example. In this paper, we suggest a new mixed-integer linear programming model for solving the cell formation problem with a variable number of manufacturing cells. The popular grouping efficacy measure is used as an objective function. To deal with its fractional nature, we apply the Dinkelbach approach. Our computational experiments are performed on two test sets: the first consists of 35 well-known instances from the literature and the second contains 32 cases less popular. We solve these instances using CPLEX software. Optimal solutions have been found for 63 of the 67 considered problem instances, and several new solutions unknown before have been obtained. The computational times are significantly decreased compared to the state-of-art approaches.
(Wu, Chung et al. 2010) The available research on the manufacturing cell formation problem shows that most solution approaches are either single- or multiple-solution-agent-based, with a fixed size of solution agents. Frequent problems encountered during the process of solving the cell formation problem include solutions being easily trapped in local optima and bad solution efficiency. Yang and Wang [Yang, F.-C., Wang, Y.-P., 2007. Water flow-like algorithm for object grouping problems. Journal of the Chinese Institute of Industrial Engineers, 24 (6), 475–488] proposed the water flow-like algorithm (WFA) to overcome the shortcomings of single- and multiple-solution -agent-based algorithms. WFA has the features of multiple and dynamic numbers of solution agents, and its mimicking of the natural behaviour of water flowing from higher to lower levels coincides precisely with the process of searching for optimal solutions. This paper, therefore, adopts the WFA logic and designs a heuristic algorithm for solving the cell formation problem. Computational results obtained from running a set of 37 test instances from the literature and newly created have shown that the proposed algorithm has performed better than other benchmarking approaches both in solution effectiveness and efficiency, especially in large-sized problems. The superiority of the proposed WFACF over other methods from the literature should be attributed to the collaboration of the WFA logic, the proposed prior estimation of the cell size, and the insertion-move. The WFA is a novel heuristic approach that deserves more attention. More attempts on adopting the WFA logic to solve many other combinatorial optimization problems are highly recommended.
(Imran, Kang et al. 2017) Work-in-process (WIP) is an important performance measure of contemporary manufacturing systems such as cellular manufacturing system (CMS). The term value-added WIP (VAWIP) is used because; the amount of WIP increased at each stage of production due to the application of resources in the form of machines, time and energy. This research is an attempt of cell formation (CF) in CMS that would minimize the value-added work in process. To achieve this objective, a mathematical model is formulated and solved using discrete event simulation (DES) integrated hybrid genetic algorithm (SHGA) in which simulation and the genetic algorithm have been integrated to form an approach called SHGA. It has the advantages of using both. The proposed method has been applied to the local automobile part supply industry for cell formation. While solving the problem with SHGA, each population has been evaluated using the discrete event simulation (DES). The solution was found in the form of assigning machines to cells in a way that resulted in minimum value-added work in process. An 8.55% reduction of value-added work in the process occurred using SHGA. The reduction of value-added work in process VAWIP in the system resulted in the reduced waiting and throughput times, whereas increased throughput rate and machine utilization.
(Boulif and Atif 2006) has proposed the manufacturing cell formation MCF problem, which is based on group technology principles, using a graph partitioning formulation. An attempt has been made to take into account the natural constraints of real-life production systems, such as operation sequences, minimum and maximum numbers of cells, and maximum cell sizes. Cohabitation constraints were added to the proposed model to deal with the necessity of grouping certain machines in the same cell for technical reasons, and non-cohabitation restrictions were included to prevent placing certain machines in close vicinity.
First, the problem is solved with a genetic algorithm GA, using a binary coding system that has proved superior to the classic integer coding systems. A new Branch-and-Bound B&B enhancement is then proposed to improve the GA’s performance. The results obtained for medium-sized instances using this enhancement are better than those obtained using the GA alone. Given these results, it is reasonable to assume that the B&B improvement will provide excellent results for significant real-life problems.
(Zohrevand, Rafiei et al. 2016) addresses dynamic cell formation problem DCFP, which has been explored vastly for several years. Although a considerable body of literature in this filed, two remarkable aspects have been significantly ignored so far, as uncertainty and human-related issues. To compensate such a shortage, this paper develops a bi-objective stochastic model. The first objective function of the developed model seeks to minimize the total cost of machine procurement, machine relocation, inter-cell moves, overtime utilization, worker hiring/laying-off, and worker moves between cells; In contrast, the second objective function maximizes labour utilization of the cellular manufacturing system. In the developed model, labour utilization, worker overtime cost, worker hiring/laying off, and worker cell assignment are considered to tackle some of the essential human-related issues in DCFP. Considering the complexity of the proposed model, a hybrid Tabu Search–Genetic Algorithm TS–GA is offered whose strength is validated to obtain optimal and near-optimal solutions through conducted experimental results.
(Jolai, Tavakkoli-Moghaddam et al. 2012) Implementation of cellular manufacturing systems CMS is thriving among manufacturing companies due to many advantages that are attained by applying this system. In this study, CMS formation and layout problems are considered. An Electromagnetism like EM-like algorithm is developed to solve the mentioned problems. Also, the required modifications to make the EM-like algorithm applicable in these problems are mentioned. A heuristic approach is designed as a local search method to improve the quality of the solution of EM-like. Beside to examine its performance, it is compared with two other ways. The performance of the EM-like algorithm with proposed heuristic and GA are compared, and it is demonstrated that implementing EM-like algorithm in this problem can improve the results significantly in Comparison with GA. Also, some statistical tests are conducted to find the best performance of the EM-like algorithm and GA due to their parameters. The convergence diagrams are plotted for two problems to compare the convergence process of the algorithms. For small size problems, the performances of the algorithms are compared with an exact algorithm.
(Li and Mehrabadi 2014) cell formation CF problems have been studied for a few decades; the purpose of this paper is to advance the solution technique using one classical approach, hierarchical cluster analysis HCA. In the application of HCA, one technical challenge is to cluster both machines and parts simultaneously. In this paper, this challenge is addressed by quantifying the coupling between machines and components in the clustering process. One feature of the proposed method is to generate block diagonal forms that show some intermediate sorting of tools and parts without specifying the structural criteria, e.g., the number of cells. Consequently, engineers can specify the fundamental principles after inspecting the block diagonal forms instead of determining them at the beginning. Some numerical examples from the literature are used to examine and verify the proposed method.
(Nouri and Hong 2013) The cellular manufacturing system CMS is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families based on appropriate similarity measures. The bacteria foraging optimization BFO algorithm is a modern evolutionary computation technique derived from the social foraging behaviour of Escherichia coli bacteria. Ever since Kevin M. Passion invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation CF problem. Also, for this purpose, the matrix-based bacteria foraging optimization algorithm traced constraints handling MBATCH is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and several exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with several algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and several exceptional elements. The results lie in favour of the better performance of the proposed algorithm.
(Paydar and Saidi-Mehrabad 2013) Cell formation problem attempts to group machines and part families in dedicated manufacturing cells such that the number of voids and exceptional elements in cells is minimized. In this paper, we presented a linear fractional programming model with the objective of maximizing the grouping efficacy while the number of cells is unknown. To show the effectiveness of the proposed model, two test problems were applied. Then, to solve the model for real-sized applications, a hybrid meta-heuristic algorithm in which a genetic algorithm and variable neighbourhood search are combined. Using the grouping efficacy measure, we have also compared the performance of the proposed algorithm on a set of 35 test problems from the literature. The results show that the proposed GA-VNS method outperforms state-of-the-art algorithms.
(Thanh, Ferland et al. 2016) to solve the 0–1 cell formation problem where the number of cells is fixed a priori and where the objective is to maximize the overall efficiency of a production system by grouping machines providing service to similar parts into a subsystem denoted cell. Three different methods are introduced and compared numerically. The first local search method is an implementation of simulated annealing SA where the definition of the neighbourhood is specific to the application and requires using diversification and intensification strategies. The second local search method is an adaptive simulated annealing method where the neighbourhood is selected randomly at each iteration. The procedure is adaptive in the sense that the probability of choosing a neighbourhood is updated during the process. The third method is a hybrid method HM of a population-based method and a local search method. To improve the solution obtained with HM, we apply a SA method afterwards. The best variants are very efficient to solve the 35 benchmark problems commonly used in the literature.
(Safaei, Saidi-Mehrabad et al. 2008) has presented an integration of explicit uncertainty for a cell formation problem CFP with a dynamic condition in cellular manufacturing systems CMS. The progressive condition indicates a multi-period planning horizon, in which product mix and demand in each period are different. As a result, the best cells designed for one period may not be the most efficient for subsequent periods and thus require reconfigurations. Moreover, in real manufacturing systems, some input parameters are fuzzy. In such cases, the fluctuation in part demand and the availability of manufacturing facilities in each period can also be regarded as fuzzy. In this paper, a fuzzy programming-based approach is developed to solve an extended mixed-integer programming model of the dynamic CFP, in which there are piecewise fuzzy numbers as coefficients in the objective function and the technological matrix. The primary purpose of this paper is to determine the optimal cell configuration in each period with the maximum degree of satisfying the fuzzy objective under the given constraints. To illustrate the behaviour of the proposed model and verify the performance of the developed fuzzy programming-based approach, we introduce several numerical examples to demonstrate the use of the foregoing approach. Finally, the detailed computational results are reported and discussed.
(Abhiraj and Aravindhababu 2017) Dragonfly Optimization DO is a nature-inspired optimization technique that imitates the static and dynamic swarming activities of dragonflies. The static swarm possessing a smaller number of dragonflies hunts for preys in a small area. In Comparison, the dynamic swarm with more number of dragonflies travels over long distances, and they represent the exploration and exploitation phases of the DO. This paper applies to DO in solving the reconfiguration problem of distribution systems to enhance the voltage profile. It presents the results of distribution systems for illustrating the superiority of the proposed method.
(KS and Murugan 2017) Dragonfly algorithm DA is a recently proposed optimization algorithm based on the static and dynamic swarming behaviour of dragonflies. Due to its simplicity and efficiency, DA has received interest of researchers from different fields. However, it lacks internal memory which may lead to its premature convergence to local optima. To overcome this drawback, we propose a novel Memory based Hybrid Dragonfly Algorithm MHDA for solving numerical optimization problems. The best and best concept of Particle Swarm Optimization PSO is added to current DA to guide the search process for potential candidate solutions, and PSO is then initialized with best of DA to exploit the search space further. The proposed method combines the exploration capability of DA and exploitation capability of PSO to achieve optimal global solutions. The efficiency of the MHDA is validated by testing on essential unconstrained benchmark functions and CEC 2014 test functions. A comparative performance analysis between MHDA and other powerful optimization algorithms have been carried out, and statistical methods prove the significance of the results. The results show that MHDA gives better performance than current DA and PSO. Moreover, it offers competitive results in terms of convergence, accuracy and search-ability when compared with the state-of-the-art algorithms. The efficacy of MHDA in solving real-world problems is also explained with three engineering design problems.
(Mafarja, Eleyan et al. 2017) Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improves the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behaviour of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization PSO, Genetic Algorithms GAs in terms of classification accuracy and several selected attributes. The results show the ability of Binary Dragonfly Algorithm BDA in searching the feature space and selecting the most informative features for classification tasks.
(Vikram, Ratnam et al. 2018) Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on the optimal multi-response evaluation of process parameters in generating responses like surface roughness Ra, surface hardness H and tool vibration displacement amplitude Vib while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling centre. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under the dry condition with high-speed steel end milling cutters using Taguchi design of experiments DOE. Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objective like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm MODA are compared with another multi-response optimization technique—Grey relational analysis GRA.
(Babayigit 2018) Due to the strong non-linear relationship between the array factor and the array elements, concentric circular antenna array CCAA synthesis problem is challenging. Nature-inspired optimization techniques have been playing an essential role in solving array synthesis problems. Dragonfly algorithm DA is a novel nature-inspired optimization technique which is based on the static and dynamic swarming behaviours of dragonflies in nature. This paper presents the design of CCAAs to get low side lobes using DA. The effectiveness of the proposed DA is investigated in two different with and without centre element cases of two three-ring CCAA design. The radiation pattern of each design cases is obtained by finding optimal excitation weights of the array elements using DA. Simulation results show that the proposed algorithm outperforms the other state-of-the-art techniques symbiotic organisms search, biogeography-based optimization, sequential quadratic programming, opposition-based gravitational search algorithm, cat swarm optimization, firefly algorithm, evolutionary programming for all design cases. DA can be a promising technique for electromagnetic problems.
(Shefu, Ali Safdar et al. 2017) Service-Oriented Computing SOC provides a framework for the realization of loosely coupled service-oriented applications. Web services are central to the concept of SOC. Currently, research into how web services can be composed to yield QoS optimal composite service has gathered significant attention. However, the number and spread of web services across the cloud data centres has increased, thereby increasing the impact of the network on composite service performance experienced by the user. Recently, QoS-based web service composition techniques focus on optimizing web service QoS attributes such as cost, response time, execution time, etc. In doing so, existing approaches do not separate QoS of the network from web service QoS during service composition. In this paper, we propose a network-aware service composition approach which separates QoS of the system from QoS of web services in the Cloud.
Consequently, our approach searches for composite services that are not only QoS-optimal but also have optimal QoS of the network. Our approach consists of a network model which estimates the QoS of the system in the form of network latency between services on the Cloud. It also includes a service composition technique based on fruit fly optimization algorithm which leverages the network model to search for low latency compositions without compromising service QoS levels. The approach is discussed, and the results of the evaluation are presented. The results indicate that the proposed method is competitive in finding QoS optimal and low latency solutions when compared to recent techniques.
(Pan 2012) The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed the Fruit Fly Optimization Algorithm. In this article, throughout the process of finding the maximal value and minimum value of a function, the function of this algorithm is tested repeatedly, in the meantime, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwan’s enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression is adopted to construct a financial distress model. It is found in this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression Neural Network model has a perfect convergence, and the model also has a complete classification and prediction capability.
(Polo-López, Córcoles et al. 2018) to propose a heuristic optimization algorithm known as the Fruit fly Optimization Algorithm is applied to antenna design problems. The original formulation of the algorithm is presented, and it is adapted to array factor and horn antenna optimization problems. Correctly, it is applied to the array factor synthesis of uniformly-fed, non-equispaced arrays and the profile optimization of multimode horn antennas. Several numerical examples are presented, and the obtained results are compared with those provided by a deterministic optimization based on a simple method and another well-known heuristic approach, the Genetic Algorithm.
(Wang, Ran et al. 2019) to propose the novel filtering antenna with the high selectivity when the antenna can be mainly composed of the three various kinds of size of patches, a pair of multiple feeding based lines, a slot, the line based resonators. When the air gap can be adapted to, they improve the impedance-based bandwidth, and they can be considered the filtering based performance. When the line resonator used to generate the highly based standard mode based rejection, then they owing to parasitic concave as well as convex and concave patches, they can be filtering based performance and the bandwidth of the antenna that can be enhanced. When the parasitic based hollow piece that can be affected, the lower based band edges selectivity. When the addition, the frequencies of the two radiation nulls that can be controlled independently can achieve the two high based sharp roll-off based rates that can be adjusting the length based parasitic based patches. They consider the impedance matching based bandwidth can be achieved through an aperture based coupling. When they finally the prototype of the various filtering antenna that can be fabricated as well as tested. When they measure the result can be considered as a better agreement that can be simulated. The simulated result can be showing the impedance-based bandwidth of about 9.9 percentage they also realize the average gain of about 7.5dBi. Besides the two filters based on zero transmission that can be located at 3.40 by using the frequency of about 3.84GHz that can produce the two radiation nulls.
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(Kashkoush and ElMaraghy 2014) proposed a proper formation of product families for Reconfigurable Manufacturing Systems RMS is of great importance for cost-effective and productive manufacturing. One key aspect that differentiates assembly systems from other manufacturing systems is that they often require parallel operations, which is not standard for different types of manufacturing systems such as dedicated manufacturing systems. This paper introduces the first product family formation method that mainly addresses Reconfigurable Assembly Systems RAS. Product assembly sequences are used, along with product demand and commonality, as similarity coefficients. Product assembly sequences are represented in the form of binary rooted trees and, based on well-established tree matching techniques used in Biology and Phylogenetics, and a new sequence-based similarity coefficient is introduced to measure the distance between any given pair of assembly sequence trees. Hierarchical clustering is then applied to generate various groups of product families that may be formed based on each similarity coefficient. A novel consensus tree-based method is applied to find the best aggregation for the three different hierarchical clustering trees. The proposed method is applied to an example of eight products. Using the proposed approach to a Reconfigurable Assembly System should significantly improve system efficiency and productivity and hence supporting cost-effective production.
(Manimaran, Nagaraj et al. 2013) Cellular Manufacturing System CMS is an application of Group Technology GT in which functionally different machines are grouped to form a family of parts. This work gives an overview of the Back Propagation Network BPN based approaches to create the machine cells and component grouping for minimizing the exceptional elements and bottleneck machines. This method is applied to the known benchmark problems found in literature, and it is found to be equal or best when compared to in terms of minimizing the number of exceptional elements.
(Karthikeyan, Saravanan et al. 2012) Manufacturing industries are under intense pressure from the increasingly competitive market. Shorter products life cycles, time-to-market and diverse customer needs have challenged manufacturers to improve the efficiency and productivity of their production activities. Proper scheduling of jobs is indispensable for the successful operation of a shop. Group technology has become an increasingly popular concept in manufacturing, which is designed to take advantage of mass production layout and techniques in the smaller batch production system. Since the conventional scheduling methods need more computation time. An attempt has been made to optimize the scheduling for cellular manufacturing system by comparing Meta-heuristic methods named as simulated annealing and Tabu Search. In the first part of this work, different types of products in the job-shop environment are identified, and the grouping of cells is performed using the Rank Order Clustering Method. In the second part, optimization procedure has been developed for the scheduling problem for processing in the machine cells. The objectives are minimization of total penalty cost, Comparison of the solutions are obtained by the proposed algorithm with the benchmark problems has been reported.
(Paramasamy, Manimaran et al.) Modern competitive environment induces every organization to be on top of its competitors with better quality products at lower prices. To meet the demands of globalization various products with different design is essential for a stable market. Group technology GT can be used as an option to meet the needs of the market. GT is a manufacturing philosophy based on the principle that similar operations should be done similarly. The Cellular Manufacturing System CMS is one of the most critical applications of GT in production. CMS is used to split the manufacturing facility into small cells in which the similar parts and its associated machines are grouped to form a manufacturing cell. The identification of part family and its associated machine cells are called Cell Formation CF. Many metaheuristic techniques are used to develop cellular manufacturing systems. In this work, cell formation is done using genetic algorithm GA is a metaheuristic technique. Initially, GA is prepared for the concurrent formation of part families and machine cells for CMS. GA is designed to handle the objective of minimization of exceptional elements in the sheet metal industry, where outstanding features are the machines and parts that are excluded from the suggested two or three cells. In this study, the machine parts incidence matrix is given as input for GA to minimize the total number of exceptional elements to evaluate the effectiveness of the CF. The proposed GA is coded in C++ language on a personal computer with core Duo, the 2GHZ processor.
(Mhudtongon, Phongcharoenpanich et al. 2016) Linear antenna array synthesis with maximum directivity using an improved fruit fly optimization algorithm with adaptive fruit fly swarm population size, namely IMFOA, is presented in this paper. The IMFOA is a recently explored, high-performance algorithm, and suitable for solving the optimization problems. To show the versatility of the presented method, the objective of antenna design is to achieve maximum directivity for linear antenna array by controlling amplitude and spacing parameters of the array antenna. A design example is presented that illustrates the use of the IMFOA, and the optimization goal in case is accurately provided and easily achieved. The result of the IMFOA is validated by comparing with results obtained using the MFOA method, and GA/F min search method. Finally, the IMFOA method is efficient and accurate for electromagnetic problems of the linear antenna array in free space.
(Smith and Baginski 2019) This paper presents a novel optimization method used to design thin-wire antennas to approximate any arbitrary antenna gain pattern. These types of antenna designs may be useful for specific tracking-search radars or telecommunication systems trying to maximize the antenna footprint without significant side lobe power loss. A genetic algorithm GA is used to optimize the design of a thin-wire antenna to match a predefined antenna pattern. Two different 3-D antenna topologies are allowed. The first is the conventional bent long wire or crooked wire antenna and the second branching tree antenna. The branching tree antenna resembles the structure of a natural tree. The branching tree topology allows antennas to branch in several directions at each segment’s end. This paper also describes a systematic approach to developing an objective function for any desired power pattern.
(Liu, Leung et al. 2019) This article presents a compact absorptive filtering patch antenna. It consists of a filtering patch antenna and a band stop filter BSF, with their transfer functions being complementary to each other. A slot is fabricated in each of the patch and ground, giving a total of two radiation nulls for the lower band edge. By using a dual-stub feed, two radiation nulls are also obtained for the upper band edge. For the BSF, a λ g /2 defected ground structures DGS and a λ g /4 defected microstrip structure DMS is used in the design. A chip resistor terminates it. Since the filtering patch antenna and BSF have complimentary transfer functions, the incident energy can be radiated effectively in the passband but primarily absorbed by the resistor in the stopbands. As a result, only little energy will be reflected over a wide frequency range, giving a reflectionless characteristic. To demonstrate this idea, an absorptive filtering antenna operating at 5.8 GHz was designed, fabricated, and tested. Its impedance is matched from 5 to 6.5 GHz, with the measured out-of-band suppression being higher than 17 and 20 dB for the lower and upper stopbands, respectively. The measured peak realized gain is 7.28 dB.
(Sang, Wang et al. 2017) A new configuration is proposed to generate an orbital angular momentum OAM beam of an arbitrary mode. The system is constituted of a ring antenna array and a digital DDS Direct Digital Synthesis amplitude and phase up-convertor control network. The experiment results show that the system has an excellent performance in generating OAM beams of different modes.
(Kawdungta and Phongcharoenpanich 2016) proposed a modified fruit fly optimization algorithm MFOA‐integrated adaptive array antenna AAA for the 2.4–2.5 GHz WLAN system. The principal components of the array antenna system encompass four array elements, four bandpass filters BPF, four digital phase shifters, a four‐way power combiner/splitter, a directional coupler, a radio frequency RF detector, and a microcontroller unit MCU. In the realization of the adaptive antenna system, the modified inverted F antenna with a finite ground plane was first innovated and subsequently deployed as the element of the four‐element array antenna. In the study, simulations and experiments were carried out with the four‐element AAAs of two configurations, i.e. the linear and planar array configurations. The simulation and experimental results revealed that the MFOA algorithmic scheme could determine the direction of the maximum arrival signal efficiently and accurately and also was capable of manipulating the radiation pattern in the desired direction. Also, the MFOA‐integrated four‐element AAA is a compact 20 mm × 35 mm × 1.8 mm and operable in the 2.31–2.55 GHz frequency band with omnidirectional radiation pattern and a gain of 1.6 dBi.
(Jiang, Chen et al. 2019) The southbound protocol of Software Defined Networking SDN enables direct access into SDN switches which accelerates the innovation and deployment of network function in the data plane. Correspondingly, SDN switches that support the new southbound protocol and provide high performance are developed continuously. Therefore, there is an increasing need for testing tools to test such equipment in terms of protocol correctness and performance. However, existing tools have deficiencies in flexibility for verifying the novel southbound protocol, time synchronization between the two planes, and supporting more testing functions with less resource consumption. In this paper, we present the concept of CPU & FPGA co-design Tester CFT for SDN switches, which provides flexible APIs for test cases of the control plane and high performance for testing functions in the data plane. We put forward an efficient scheduling algorithm to integrate the control plane and the data plane into a single pipeline which fundamentally solves the time synchronization between these two planes. Due to the reconfigurable feature of our proposed pipeline, it becomes possible to perform different testing functions in one pipeline. Through a prototype implementation and evaluation, we reveal that the proposed CFT can verify the protocol correctness of SDN switches on the control plane while providing no-worse performance for tests on the data plane compared with commercial testers.
(Czibula, Gu et al. 2016) present the planning of training sessions in large organizations requiring periodic retraining of their staff. The allocation of students must take into account student preferences as well as the desired composition of study groups. The paper presents a bicriteria Quadratic Multiple Knapsack formulation of the considered practical problem, and a novel solution procedure based on Lagrangian relaxation. The paper presents the results of computational experiments aimed at testing the optimization procedure on real-world data originating from Australia’s largest electricity distributor. Results are compared and validated against a Genetic Algorithm based metaheuristic.
(Hassan, Wadbro et al. 2015) present expressions for the derivatives of the outgoing signal in coaxial cables concerning the conductivity distribution in a specific domain. The derived expressions can be used with gradient-based optimization methods to design metallic electromagnetic devices, such as antennas and waveguides. We use the ad joint-field method to derive the emotions and the derivation is based on the 3D time-domain Maxwell’s equations. We present two derivative expressions; one expression is calculated in the continuous case and the second is calculated based on the FDTD discretization of Maxwell’s equations, including the uniaxial perfectly match layer UPML to simulate the radiation boundary condition. The derivatives are validated through a numerical example, where derivatives computed by the ad joint-field method are compared against derivatives computed with finite differences. Up to 7 digits precision matching is obtained.
(Moctar, Stojilović et al. 2018) describes a deterministic and parallel implementation of the VPR rout ability-driven router for FPGAs. We considered two parallelization strategies 1 routing multiple nets in parallel and 2 routing one fillet at a time while parallelizing the Maze Expansion step. Using eight threads running on eight cores, the two methods achieved speedups of 1.84× and 3.67×, respectively, compared to VPR’s single-threaded rout ability-driven router. Removing the determinism requirement increased these respective speedups to 2.67× and 5.46× while sacrificing the guarantee of reproducible results.
(Hassan, Berggren et al. 2019) presents a topology optimization approach to design planar transitions between a microstrip line MSL and a rectangular waveguide RWG in the K-band. The development comprises two sub-transitions: one from the MSL to a substrate integrated waveguide SIW and the second from the SIW to the RWG. Both are on the same substrate and can be manufactured with a standard printed circuit board process. This leads to a very cost-effective solution compared with other approaches. A WR-42 waveguide can easily be surface mounted to the transitions using a standard flange. The transformations have been fabricated, and their measured performance shows good agreement with the simulations. The MSL-SIW change has a broadband behaviour, and SIW-RWG development still reaches a relative bandwidth of 10%.
(Wang 2015) Circuit clustering is one of the most crucial steps in a post-synthesis FPGA CAD flow. It attempts to efficiently fit synthesized logic functions into FPGA logic clusters. On an FPGA, different clustering’s result in different circuit mappings, which affect FPGA utilization, rout ability and timing, and therefore impact the circuit performance. This research proposes the use of a Multi-Objective Genetic Algorithm (MOGA) as a methodology to solve the cluster-based FPGA circuit clustering problem. Four alternative approaches based on MOGA methods are proposed in this research: RV Pack is inspired by the stochastic feature that exists in Evolutionary Algorithms EAs. GGAPack, GGAPack2, DBPack and HY Pack, T-HY Pack Timing-driven HY Pack are then proposed and developed, which are fully customized MOGA-based circuit clustering methods. GGAPack clusters a circuit using a top-down perspective, and DBPack uses a new bottom-up perspective. HY Pack combines GGAPack and HY Pack a – hybrid method. According to experimental results, a few conclusions are drawn: It is possible to improve the performance of the greedy algorithm based circuit clustering methods by incorporating randomness. The production of MOGA based top-down clustering is sparse however, using MOGA to cluster a circuit from a bottom-up perspective can produce better solutions. T-HY Pack clustered circuit has the best timing performance compared with state-of-the-art methods. The experimental results also reflect a full potential for using GAs to solve FPGA circuit mapping problems.
(Zhao, Jin et al. 2018) propose a High-resolution three-dimensional 3D images can be acquired by the planar Multiple-Input Multiple-Output MIMO array radar making future work like detection and tracking easier. However, regarding portability and to save the costs of a radar system, MIMO radar array adopts rare type with a limited number of antennas, so the imaging performance of a MIMO radar system is inadequate. In this paper, the 3D back-projection imaging algorithm is verified by the experimental results of planar MIMO array for the human body, and an enhanced radar imaging method is proposed. The Lucy-Richardson (LR) algorithm based on deconvolution that is usually used for optical images is applied in radar images. Since the LR algorithm can amplify the noise level in a noise-contaminated system, a regularization method based on the Total Variation constraint is further incorporated in the LR algorithm to suppress the ill-posed characteristics. The proposed method shows a higher image Signal-to-Noise Ratio, a faster rate of convergence, a taller structure similarity and a smaller relative error compared to some similar techniques. In the meantime, it also reduces the loss of image information after image enhancement and improves the radar image quality get less grating lobe and clearer human limbs. The proposed method overcomes the disadvantages mentioned above and is verified by a simulation experiment and real data measurement.
(Dey, Chakrabarty et al. 2016) presents the design and analysis of linear, as well as cubic spline, interpolated profiled smooth-wall multimode horn using mode matching MM technique along with genetic algorithm GA and particle swarm optimization PSO. To apply the MM technique, the smooth profile conical geometry is assumed to consist of cascaded sections of large number of small symmetrical radial steps. Scattering matrix for each part has been calculated using MM technique, and overall scattering matrix of the cascaded junctions has been calculated using the generalized scattering matrix technique. The radiation pattern has been calculated using a hybrid boundary contour mode-matching technique. Optimization of the linear/spline profile horn has been carried out using GA as well as PSO. Detailed modal analysis, along with the convergence of the present technique for the smooth profile structure, has also been carried out. A spline profile multimode horn working at 89 GHz has been designed and developed. The current analysis has been verified with the measured results. There is an excellent agreement between theoretical and measured results.
(Qin, Liu et al. 2016) the electromagnetic EM vortex imaging purposes, the sidelobe suppression and the beam collimation method in the generation of orbital angular momentum OAM beams is proposed. Based on the concentric ring array, the objective function for the generic algorithm is defined to calculate the signal amplitude for each ring. Comprehensive simulations are conducted to validate the effectiveness of the proposed method. Results show that the main lobes of the radiation pattern of different OAM modes are collimated in the same direction, and the side lobes are all lower than -20 dB. Furthermore, the imaging model of the concentric-ring array is established, and the target image is obtained through numerical simulations. The work can advance the development of the EM vortex imaging technique and novel radar detection technology as well.
(Moradi and Mohajeri 2016) Metamaterial surfaces with a low refractive index have been recently proposed in the design of the low sidelobe level horn antennas based on engineering dispersion method. This paper explores a linearly polarized square horn antenna in the Ku-band for communication satellite reflector antennas. We study the designs of broadband met surfaces and their use as coating layers on the interior E-plane walls of the horn. The design process includes combining the Particle Swarm Optimization technique with a full-wave electromagnetic solver to model dispersion-engineered Metasurfaces with surface impedance characteristics that support the desired hybrid modes. The obtained optimized met surface is a low refractive index metamaterial with negligible losses that improves the conventional horn properties over the frequency band. The symmetric far-field radiation patterns, the small side low levels and cross polarization levels and the aperture field distributions verify the hybrid mode operation of the horn. Moreover, this technique promises for a lighter horn with a more straightforward manufacturer method compared with the conventional dielectric core loaded and corrugated horns.
(Pinchera, Migliore et al. 2018) propose an effective antenna array synthesis algorithm which is aimed at the generation of sparse arrays radiating patterns with arbitrary upper bounds. The approach based on a convex optimization exploits an iterative polygonal expansion and contraction of the sources and is suitable for the synthesis of linear and planar arrays. Numerical simulations show that the proposed algorithm allows a degree of sparsification equal or better than the one achievable by concurrent algorithms, with a significantly lower computational burden concerning the best ones, allowing the useful synthesis of sparse arrays of hundreds of wavelengths with simple office computers.
(Pinchera, Migliore et al. 2018) to introduces essential components for programming Xilinx FPGAs Xilinx, San Jose, CA, the USA, such as Xilinx Design Language XDL, XDL Report XDLRC, and bitstream. Then, reverse engineering tools Debit, BIL, and Bit2ncd, which extract the bitstream from the external memory to the FPGA and utilize it to recover the netlist, are reviewed, and their limitations are discussed. This paper also covers additional tools Rapidsmith that can adjust the FPGA design flow to support reverse engineering. Finally, reverse engineering projects for non-Xilinx products, such as Lattice FPGAs Icestorm and Altera FPGAs QUIP, are introduced to compare the reverse engineering capabilities by various commercial FPGA products.