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Honda A.,Kyushu Institute of Technology | Okazaki Y.,Fuzzy Logic Systems Institute
Information Sciences | Year: 2017

We propose an integral with respect to a nonadditive monotone measure. This integral is a generalization of the Lebesgue integral and also the Choquet integral. It has appropriate properties, and can be extensively applied to real data analysis. © 2016 Elsevier Inc.


Gen M.,Fuzzy Logic Systems Institute | Gen M.,National Tsing Hua University | Lin L.,Fuzzy Logic Systems Institute | Lin L.,Dalian University of Technology
Journal of Intelligent Manufacturing | Year: 2014

Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems. © 2013 Springer Science+Business Media New York.


Wu J.-Z.,Soochow University of Taiwan | Hao X.-C.,Waseda University | Chien C.-F.,National Tsing Hua University | Gen M.,Fuzzy Logic Systems Institute
Journal of Intelligent Manufacturing | Year: 2012

To improve capital effectiveness in light of demand fluctuation, it is increasingly important for hightech companies to develop effective solutions for managing multiple resources involved in the production. To model and solve the simultaneous multiple resources scheduling problem in general, this study aims to develop a genetic algorithm (bvGA) incorporating with a novel bi-vector encoding method representing the chromosomes of operation sequence and seizing rules for resource assignment in tandem. The proposed model captured the crucial characteristics that the machines were dynamic configuration among multiple resources with limited availability and sequence-dependent setup times of machine configurations between operations would eventually affect performance of a scheduling plan. With the flexibility and computational intelligence that GA empowers, schedule planners can make advanced decisions on integrated machine configuration and job scheduling. According to a number of experiments with simulated data on the basis of a real semiconductor final testing facility, the proposed bvGA has shown practical viability in terms of solution quality as well as computation time. © Springer Science+Business Media, LLC 2011.


Sangsawang C.,Khon Kaen University | Sethanan K.,Khon Kaen University | Fujimoto T.,University of Tokyo | Gen M.,Fuzzy Logic Systems Institute
Expert Systems with Applications | Year: 2014

This paper addresses a problem of the two-stage reentrant flexible flow shop (RFFS) with blocking constraint (FFS|2-stage,rcrc,block|Cmax). The objective is to find the optimal sequences in order to minimize the makespan. In this study, the hybridization of GA (HGA: hybrid genetic algorithm) with adaptive auto-tuning based on fuzzy logic controller and the hybridization of PSO (HPSO: hybrid particle swarm optimization) with Cauchy distribution were developed to solve the problem. The encoding and decoding routines that appropriate for blocking constraint and Relax-Blocking algorithm for improving chromosome and particle were suggested. Experimental results reveal that the HPSO and HGA algorithms give better solutions than the classical metaheuristics, GA and PSO, for all test problems respectively. Additionally, the relative improvement (RI) of the makespan solutions obtained by the proposed algorithms with respect to those of the current practice is performed in order to measure the quality of the makespan solutions generated by the proposed algorithms. The RI results show that the HGA and HPSO algorithms can improve the makespan solution by averages of 15.51% and 15.60%, respectively. We found that the performance of the HGA is not significantly competitive as compared to the HPSO but its computational times are significantly higher than those of the HPSO. © 2014 Elsevier Ltd.


Wu J.-Z.,Soochow University of Taiwan | Chien C.-F.,National Tsing Hua University | Gen M.,Fuzzy Logic Systems Institute
International Journal of Production Research | Year: 2012

Increasing global competition has forced high-tech companies to focus on their core competences and outsource other activities to maintain their competitive advantages in the supply chains. While most companies rely on domain experts to coordinate strategic outsourcing decisions among a number of qualified vendors with different capabilities, the present problem can be formulated into a complex nonlinear, multi-dimensional, multi-objective combinatorial optimisation problem. Focused on real settings, this study aims to fill the gap via developing a bi-objective genetic algorithm (boGA) for determining the outsourcing order allocation with nonlinear cost structure, while minimising both the total alignment gap and the total allocation cost. The proposed boGA incorporates specific random key representation to facilitate encoding and decoding. This study also develops a bi-objective Pareto solution generation algorithm to enable efficient searching of Pareto solutions in multiple ranks and designs a composite Pareto ranking selection with uniform sum rank weighting for effective selection. To estimate its validity, the proposed boGA was validated with realistic cases from a leading semiconductor company in Hsinchu Science Park in Taiwan. The optimal boGA parameters were tested using a set of experiments. Scenario analyses were conducted to evaluate the performance of the proposed algorithm under different demand conditions using the metrics in the literature. The results have shown the practical viability of the proposed algorithm to solve the present problem of monthly outsourcing decisions for the case company in practicable computation time. This algorithm can determine the near-optimal Pareto front for decision makers to further incorporate with their preferences. This study concludes with discussion of future research directions. © 2012 Copyright Taylor and Francis Group, LLC.


Fukushima K.,Fuzzy Logic Systems Institute
Neural Networks | Year: 2011

The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. To find out the causes of vulnerability to noise, this paper analyzes the behavior of feature-extracting S-cells. It then proposes the use of subtractive inhibition to S-cells from V-cells, which calculate the average of input signals to the S-cells with a root-mean-square. Together with this, several modifications have also been applied to the neocognitron. Computer simulation shows that the new neocognitron is much more robust against background noise than the conventional ones. © 2011 Elsevier Ltd.


Fukushima K.,Fuzzy Logic Systems Institute
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper proposes an improved add-if-silent rule, which is suited for training intermediate layers of a multi-layered convolutional network, such as a neocognitron. By the add-if-silent rule, a new cell is generated if all postsynaptic cells are silent. The generated cell learns the activity of the presynaptic cells in one-shot, and its input connections will never be modified afterward. To use this learning rule for a convolutional network, it is required to decide at which retinotopic location this rule is to be applied. In the conventional add-if-silent rule, we chose the location where the activity of presynaptic cells is the largest. In the proposed new learning rule, a negative feedback is introduced from postsynaptic cells to presynaptic cells, and a new cell is generated at the location where the presynaptic activity fails to be suppressed by the feedback. We apply this learning rule to a neocognitron for hand-written digit recognition, and demonstrate the decrease in the recognition error. © 2014 Springer International Publishing Switzerland.


Fukushima K.,Fuzzy Logic Systems Institute
Neural Networks | Year: 2013

This paper proposes new learning rules suited for training multi-layered neural networks and applies them to the neocognitron. The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It acquires the ability to recognize visual patterns through learning. For training intermediate layers of the hierarchical network of the neocognitron, we use a new learning rule named add-if-silent. By the use of the add-if-silent rule, the learning process becomes much simpler and more stable, and the computational cost for learning is largely reduced. Nevertheless, a high recognition rate can be kept without increasing the scale of the network. For the highest stage of the network, we use the method of interpolating-vector. We have previously reported that the recognition rate is greatly increased if this method is used during recognition. This paper proposes a new method of using it for both learning and recognition. Computer simulation demonstrates that the new neocognitron, which uses the add-if-silent and the interpolating-vector, produces a higher recognition rate for handwritten digits recognition with a smaller scale of the network than the neocognitron of previous versions. © 2013 Elsevier Ltd.


Fukushima K.,Fuzzy Logic Systems Institute
Neural Networks | Year: 2013

The neocognitron is a neural network model proposed by. Fukushima (1980). Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to robustly recognize visual patterns through learning. Although the neocognitron has a long history, modifications of the network to improve its performance are still going on. For example, a recent neocognitron uses a new learning rule, named add-if-silent, which makes the learning process much simpler and more stable. Nevertheless, a high recognition rate can be kept with a smaller scale of the network. Referring to the history of the neocognitron, this paper discusses recent advances in the neocognitron. We also show that various new functions can be realized by, for example, introducing top-down connections to the neocognitron: mechanism of selective attention, recognition and completion of partly occluded patterns, restoring occluded contours, and so on. © 2012 Elsevier Ltd.


Fukushima K.,Fuzzy Logic Systems Institute
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. This paper proposes several modifications, by which the neocognitrons can be much more robust against background noise. © 2010 Springer-Verlag.

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