The dynamic transfer batch-size decision for thin film transistor–liquid crystal display array manufacturing by artificial neural-network

In the thin film transistor–liquid crystal display (TFT–LCD) manufacturing process, array manufacturing is an important process. Transporting activities in array manufacturing are an important factor because of the frequent tasks. The transporting activity in array manufacturing is performed by an automated material handling system (AMHS). Automated guided vehicle (AGV) is the transporter used to carry glass substrates that are stored in a cassette. The capacity of a cassette is known as the transfer batch-size. Prior research of decisions in transfer batch-size, has addressed an optimal methodology, where one optimal transfer batch-size is assumed to have known conditions. However, in the volatile production environment, there may be multiple kinds of transfer batch-sizes. Therefore, we present an application of using a dynamic transfer batch-size strategy within a volatile production environment. In order to obtain the appropriate transfer batch-size for the current production environment, we adopt a neural-network based methodology as the core of the decision-making mechanism. This mechanism has the capability to identify the suitable transfer batch-size to allow an effective and efficient transportation under numerous conditions within the current production environment. This methodology is compared with the fixed transfer batch-size strategy in a real practical case. The results show that the dynamic transfer batch-size is superior to the fixed batch-size transportation.     

A multi-domain vertical alignment liquid crystal display to improve the V–T property

The voltage–transmittance (V–T) property is important for the liquid crystal displays (LCDs). In this work, we propose a sub-pixel structure with two common electrodes of a multi-domain vertical alignment (MVA) mode. The sub-pixel is divided into two sub-areas and different common electrode voltages are applied to it. The optimal voltage difference of the common electrodes between sub-area 1 and sub-area 2 is proposed. The simulated results on the plotted displays and the voltage–transmittance property of the LCD, which has 1:1 sub-area ratio, have been carried out. The results show that the structure can form MVA liquid crystal display mode, such as 8-domain VA mode. It can improve the V–T property at large oblique viewing angle and make the transmittance difference between the normal direction and the oblique direction viewing angle less than that of conventional 4-domain MVA mode.     

A novel hybrid BPSO–SCA approach for feature selection

Natural Computing - Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem....     

A hybrid genetic algorithm for the discrete time–cost trade-off problem

In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete time–cost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The HA and a sole GA have been coded in Visual C++ on a personal computer. Ten benchmark test problems with a range of 18 to 630 activities are used to evaluate performance of the HA. The benchmark problems are solved to optimality using mixed integer programming technique. The results of the performance analysis indicate that the hybrid strategy improves convergence of GA significantly and HA provides a powerful alternative for the DTCTP.     

A novel hybrid PSO–GWO approach for unit commitment problem

Particle swarm optimization algorithm is a inhabitant-based stochastic search procedure, which provides a populace-based search practice for getting the best solution from the problem by taking particles and moving them around in the search space and efficient for global search. Grey Wolf Optimizer is a recently developed meta-heuristic search algorithm inspired by Canis-lupus. This research paper presents solution to single-area unit commitment problem for 14-bus system, 30-bus system and 10-generating unit model using swarm-intelligence-based particle swarm optimization algorithm and a hybrid PSO–GWO algorithm. The effectiveness of proposed algorithms is compared with classical PSO, PSOLR, HPSO, hybrid PSOSQP, MPSO, IBPSO, LCA–PSO and various other evolutionary algorithms, and it is found that performance of NPSO is faster than classical PSO. However, generation cost of hybrid PSO–GWO is better than classical and novel PSO, but convergence of hybrid PSO–GWO is much slower than NPSO due to sequential computation of PSO and GWO.     

A coupled Ericksen/Allen–Cahn model for liquid crystal droplets

We present a model and discretization that couples the Ericksen model of liquid crystals with variable degree of orientation to the Allen–Cahn equations with a mass constraint. The coupled system models liquid crystal droplets with anisotropic surface tension effects due to the liquid crystal molecular alignment. The total energy consists of the Ericksen energy, phase-field (Allen–Cahn) energy, and a weak anchoring energy that couples the liquid crystal to the diffuse interface. We describe our discretization of the total energy along with a method to compute minimizers via a discrete gradient flow algorithm which has a strictly monotone energy decreasing property. Numerical experiments are given in three dimensions that illustrate a wide variety of droplet shapes that result from their interaction with defects.     

A novel hybrid DE–random search approach for unit commitment problem

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

     

A hybrid metaheuristic approach for the rollon–rolloff vehicle routing problem

This paper discusses the rollon–rolloff vehicle routing problem, a sanitation routing problem in which large containers are left at customer locations such as construction sites and shopping centers. Customers dump their garbage into large waste containers and request for waste treatment services. Tractors then transport a container at a time between customer locations, disposal facility, and depot. The objective of the problem is to determine routes that minimize the number of required tractors and their deadhead time to serve all given customer demands. We propose a hybrid metaheuristic approach that consists of a large neighborhood search and various improvement methods to solve the problem. The effectiveness of the proposed approach is demonstrated by computational experiments using benchmark data. New best-known solutions are found for 17 problems out of 20 benchmark instances.     

A hybrid multicast–unicast assignment approach for data distribution management in HLA

The main focus of data distribution management (DDM) in HLA is to reduce the amount of data received by federates in large-scale distributed simulations. The use of limited multicast resources plays a key role in the performance of DDM. In order to improve the performance of DDM by using communication protocol effectively, a hybrid multicast–unicast data transmission problem and its formal definition are presented, and then a hybrid multicast–unicast assignment approach is proposed. The approach uses a new adaptive communication protocol selection (ACPS) strategy to utilize the advantages of multicast and unicast, avoid their disadvantages, and consider the inter-relationship between connections. It includes the ACPS static assignment algorithm and the ACPS dynamic assignment algorithm, according to the difference between the static connections and the dynamic connections. In our approach, a concept of distance is presented to measure the inter-relationship between connections for multicast and the message redundancy for unicast, which is the core of the two algorithms in order to gather the connections to a multicast group or to balance the use of unicast and multicast for best performance. As a result, our algorithms can more effectively decide whether a new connection should use unicast or multicast communication, and whether adjusting previous assignment result can further improve the performance. In addition, a control mechanism is introduced to deal with connection changes during the dynamic assignment. The experiment results indicate that our algorithms can utilize the multicast and unicast communication resources effectively, as well as can achieve better performance than existing methods in the real running environment.     

A new iterative mutually coupled hybrid GA–PSO approach for curve fitting in manufacturing

Fitting data points to curves (usually referred to as curve reconstruction) is a major issue in computer-aided design/manufacturing (CAD/CAM). This problem appears recurrently in reverse engineering, where a set of (possibly massive and noisy) data points obtained by 3D laser scanning have to be fitted to a free-form parametric curve (typically a B-spline). Despite the large number of methods available to tackle this issue, the problem is still challenging and elusive. In fact, no satisfactory solution to the general problem has been achieved so far. In this paper we present a novel hybrid evolutionary approach (called IMCH-GAPSO) for B-spline curve reconstruction comprised of two classical bio-inspired techniques: genetic algorithms (GA) and particle swarm optimization (PSO), accounting for data parameterization and knot placement, respectively. In our setting, GA and PSO are mutually coupled in the sense that the output of one system is used as the input of the other and vice versa. This coupling is then repeated iteratively until a termination criterion (such as a prescribed error threshold or a fixed number of iterations) is attained. To evaluate the performance of our approach, it has been applied to several illustrative examples of data points from real-world applications in manufacturing. Our experimental results show that our approach performs very well, being able to reconstruct with very high accuracy extremely complicated shapes, unfeasible for reconstruction with current methods.     

A hybrid genetic algorithm–fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals

Incomplete data are often encountered in data sets used in clustering problems, and inappropriate treatment of incomplete data can significantly degrade the clustering performance. In view of the uncertainty of missing attributes, we put forward an interval representation of missing attributes based on nearest-neighbor information, named nearest-neighbor interval, and a hybrid approach utilizing genetic algorithm and fuzzy c-means is presented for incomplete data clustering. The overall algorithm is within the genetic algorithm framework, which searches for appropriate imputations of missing attributes in corresponding nearest-neighbor intervals to recover the incomplete data set, and hybridizes fuzzy c-means to perform clustering analysis and provide fitness metric for genetic optimization simultaneously. Several experimental results on a set of real-life data sets are presented to demonstrate the better clustering performance of our hybrid approach over the compared methods.     

A new hybrid method for time series forecasting: AR–ANFIS

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR–ANFIS). AR–ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR–ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR–ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.

     

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.     

A synthetical development approach for rehabilitation assistive smart product–service systems: A case study

Digital transformation (DT), the combination of information, computing, communication and connectivity technologies, which has triggered an effective upgrade of different aspects of market strategy, customer experience etc. Nowadays, rehabilitation assistive devices (RADs) are evolving to be more digital, intelligent and personalized. Digitalization and servitization have fostered to an emerging business model—the smart product–service system (Smart PSS). Therefore, DT of the RADs’ industry advocates not only the design of products and functions, the more important is the management of service processes and resource integration. With the increase in the elderly and disabled population, the requirement for RADs is becoming more urgent. However, research on Smart PSS for RAD is still limited. The rehabilitation assistive smart product–service systems (RASPSS) was introduced into the development of RADs based on the “Design and Management of DT” strategy through the service design of assistive devices and user requirements analysis. Further, an integrated design of RAD and Smart PSS has been created, a development method of RASPSS proposed, the theoretical model of the Smart PSS based on RADs built. To specify the service framework, this case study discusses the development of a home rehabilitation assistive system for femoral stem fracture patients. This paper evaluates the usability of the system, the results of which prove usability and effectiveness of the RASPSS development method. The RASPSS development model is designed to meet needs of stakeholders, improve the user rehabilitation experience, promote the service innovation of Smart PSS, bring certain market benefits of rehabilitation aids and create social value.     

A hybrid method of semi-Lagrangian and additive semi-implicit Runge–Kutta schemes for gyrokinetic Vlasov simulations

A hybrid method of semi-Lagrangian and additive semi-implicit Runge–Kutta schemes is developed for gyrokinetic Vlasov simulations in a flux tube geometry. The time-integration scheme is free from the Courant–Friedrichs–Lewy condition for the linear advection terms in the gyrokinetic equation. The new method is applied to simulations of the ion-temperature-gradient instability in fusion plasmas confined by helical magnetic fields, where the parallel advection term severely restricts the time step size for explicit Eulerian schemes. Linear and nonlinear results show good agreements with those obtained by using the explicit Runge–Kutta–Gill scheme, while the new method substantially reduces the computational cost.     

A hybrid teaching–learning slime mould algorithm for global optimization and reliability-based design optimization problems

Neural Computing and Applications - Slime mould algorithm (SMA) is a novel metaheuristic algorithm with good performance for optimization problems, but it may encounter premature or low accuracy in...