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Prior research lacks understanding about how collaborative arrangements and network structures influence innovation outcomes in platform-based ecosystems. This paper addresses this gap by using an NK simulation model to investigate the impacts of different collaboration patterns and network structures on innovation performance. The NK simulation approach overcomes the shortcomings of empirical methods and enables examining the dynamic impacts. The results reveal that the "special platform + generic complementor" pattern leads to the highest innovation output. The impact of component correlations on the innovation performance follows an inverted U-shape. The small world network structure promotes innovation versus regular or random networks. The results provide novel theoretical insights into strategically configuring platform partnerships and network connections to optimize innovation. The findings offer practical guidance for firms to choose beneficial collaboration pattern and design proper network structure. [ABSTRACT FROM AUTHOR]

In the early stages of health attention, such as in the examination rooms, patients are generally treated using a First In First Treat (FIFT) logic. This treatment style is inefficient from an operational view as it leads to long waiting times for critical patients. Based on optimization and discrete-event simulation, this paper introduces a hybrid model to assign patients priorities and exam rooms. Patients arrive to the system following a probability function, and for every discrete interval, a mixed-integer linear optimization model is used to allocate patients, considering health priority, preparation times, and availability of time and resources. Instances of the problem are defined using information from a health system in Mexico. Additional to the linear model, a heuristic algorithm is proposed, composed of two phases and aims to provide good, feasible, and fast solutions without ensuring optimality. Computational experiments showed that the linear model is more appropriate for small instances since the heuristic is faster and the relative deviations with respect to the linear model are below five percent. Finally, the discrete-event simulation is run for two cases: (1) with a FIFT logic and (2) using the optimization model. The hybrid model reduces the global waiting times up to 90% and the number of high health priority patients who are not treated up to 100%. • A mixed-integer linear model and a heuristic algorithm are used to allocate patients. • Optimization-simulation model reduced waiting times in primary health centers. • Optimization-simulation model reduced the number of non-treated patients. • The hybrid model is applied in a real Mexico's health system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating continuous boundaries between acceptable and unacceptable engineering design parameters in complex engineering applications. In particular, a procedure is proposed to reduce the computational cost of finding and representing the boundary. The proposed methodology combines a low-discrepancy sequence (Sobol) and a support vector machine (SVM) in an active learning procedure able to efficiently and accurately estimate the boundary surface. The paper describes the approach and methodological choices resulting in the desired level of boundary surface refinement and the new algorithm is applied to both two highly-nonlinear test functions and a real-world train stability design problem. It is expected that the new method will provide designers with a tool for the evaluation of the acceptability of designs, particularly for engineering systems whose behaviour can only be determined through complex simulations. [ABSTRACT FROM AUTHOR]

• A novel expert system for checking the correctness of memory systems. • The expert system properly combines simulation and metamorphic testing. • The expert system automatically generates test cases to check memory models. • Mutation testing has been applied to check the effectiveness of the ES. • The ES provides promising results, detecting 99. During the last few years, computer performance has reached a turning point where computing power is no longer the only important concern. This way, the emphasis is shifting from an exclusive focus on the optimisation of the computing system to optimising other systems, like the memory system. Broadly speaking, testing memory systems entails two main challenges: the oracle problem and the reliable test set problem. The former consists in deciding if the outputs of a test suite are correct. The latter refers to providing an appropriate test suite for determining the correctness of the system under test. In this paper we propose an expert system for checking the correctness of memory systems. In order to face these challenges, our proposed system combines two orthogonal techniques – simulation and metamorphic testing – enabling the automatic generation of appropriate test cases and deciding if their outputs are correct. In contrast to conventional expert systems, our system includes a factual database containing the results of previous simulations, and a simulation platform for computing the behaviour of memory systems. The knowledge of the expert is represented in the form of metamorphic relations, which are properties of the analysed system involving multiple inputs and their outputs. Thus, the main contribution of this work is two-fold: a method to automatise the testing process of memory systems, and a novel expert system design focusing on increasing the overall performance of the testing process. To show the applicability of our system, we have performed a thorough evaluation using 500 memory configurations and 4 different memory management algorithms, which entailed the execution of more than one million of simulations. The evaluation used mutation testing, injecting faults in the memory management algorithms. The developed expert system was able to detect over 99% of the critical injected faults, hence obtaining very promising results, and outperforming other standard techniques like random testing. [ABSTRACT FROM AUTHOR]

Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new optimization technique, the hierarchical multi-space competitive distributed genetic algorithm (HmcDGA), which is effective for the hierarchical optimization problem. It is an extension of the multi-space competitive distributed genetic algorithm (mcDGA), which was proposed by the authors. The mcDGA efficiently finds an optimal solution with a low computational cost by increasing the number of individuals in a solution space in which it is likely to exist. An optimization method that is divided into several levels of hierarchy is called a hierarchical optimization . Several hierarchical optimization techniques have been proposed, including the hierarchical genetic algorithm (HGA). In hierarchical optimization, a complex problem is divided into a hierarchical collection of simpler problems, and each level is optimized independently. In this way, complex problems can be solved without the need to develop problem-specific operators. However, in the conventional HGA, this results in a high computational cost because the genetic algorithm (GA) is repeated many times at upper and lower level. The HmcDGA is a hybrid of the mcDGA and HGA, and it has some of the advantages of each one; for example, the HmcDGA can find an optimal solution at low computational cost and without requiring special operations. This allows it to be applied to a wide variety of optimization problems. Therefore, the HmcDGA may become the powerful optimization algorithm that can solve various problems. In this paper, we apply the proposed HmcDGA to the flexible job-shop scheduling problem (FJSP) which is one of the complex combinational optimization problem and confirm its effectiveness. Simulation results show that the HmcDGA can find solutions that are comparable to those found by using GAs developed specifically for the FJSP, the HmcDGA is not required a lot of computational costs comparing to the HGA. [ABSTRACT FROM AUTHOR]

This paper proposes a method to resolve a strong coupling between temperature and relative humidity in drying room control systems. The coupling issue not only affects the control accuracy but also causes system instability, unnecessary adjustment, and extra energy consumption. A real-time temperature and relative humidity decoupling control method is reported in this paper. An improved aspirated psychrometer was used as a relative humidity measurement unit, which achieved relative humidity measurement accuracy of ±0.7%. The decoupling approach was developed based on a large real-time drying room, whose physical size was 22 × 15 × 3.5 m. A decoupling approach was developed using the adaptive neuro-fuzzy inference system for the control of temperature and relative humidity in the drying room. The simulated result shows that after the decoupling treatment, the relative humidity fluctuation was reduced from ±2.5% to ±0.6%. [ABSTRACT FROM AUTHOR]

This paper firstly proposes a bilateral optimized negotiation model based on reinforcement learning. This model negotiates on the issue price and the quantity, introducing a mediator agent as the mediation mechanism, and uses the improved reinforcement learning negotiation strategy to produce the optimal proposal. In order to further improve the performance of negotiation, this paper then proposes a negotiation method based on the adaptive learning of mediator agent. The simulation results show that the proposed negotiation methods make the efficiency and the performance of the negotiation get improved. [ABSTRACT FROM AUTHOR]

In the era of big data, some data records are interrelated with each other in many areas, such as marketing, management, health care, and education. These interrelated data can be more naturally represented as networks with nodes and edges. Inside this type of networks, there is usually a hidden community structure where each community represents a relatively independent functional module. Such hidden community structures are useful for many applications, such as word-of-mouth marketing, promoting decentralized social interactions inside organizations, and searching biological pathways related to various diseases. Therefore, how to detect hidden community structures becomes an important task with wide applications. Currently, modularity-based methods are widely-used among many existing community structure detection methods. They detect communities with more internal edges than expected under the null hypothesis of independence. Since research in correlation analysis also searches for patterns which occur more than expected under the null hypothesis of independence, this paper proposed a framework of changing the original modularity function according to different existing correlation functions in the correlation analysis research area. Such a framework can utilize not only the current but also the future potential research progresses in correlation analysis to advance community detection. In addition, a novel graphical analysis on different modified-modularity functions is conducted to analyze their different preferences, which are also validated by our evaluation on both real life and simulated networks. Our work to connect modularity-based methods with correlation analysis has several significant impacts on the community detection research and its applications to expert and intelligent systems. First, the research progress in correlation analysis can be utilized to define a more effective objective function in community detection for better detection results since different real-life applications might need communities with different resolutions. Second, any existing research progress for the modularity function, such as the Louvain method for speeding up the search and different extensions for overlapping community detection, can be applied in a similar way to the new objective function derived from existing correlation functions, because the new objective function is unified within one framework with the modularity function. Third, our framework opens a large unexplored area for the researchers interested in community detection. For example, what is the best heuristic search method for each different objective function? What are the characteristics of each objective function when applied to overlapping community detection? Among different extensions to overlapping community detection, which extension is better for each objective function? [ABSTRACT FROM AUTHOR]

The simulation and analysis of structure within decision making unit (DMU) is the basis on which network data envelopment analysis (DEA) opens the “black box” and evaluates the efficiency of system with complex internal structure. The efficiency measurements of system with sub-DMU in series or with sub-DMUs in parallel are two common cases in the theory development and applications of two-stage DEA. However, the research on parallel-series hybrid system is not rich enough. The paper develops a set of DEA models to treat a two-stage system comprised of three sub-DMUs in hybrid form with additional inputs to the second stage. The proposed models simulate precisely the system's parallel-series internal structure, employ synthetically additive and multiplicative DEA approaches to estimate and decompose the efficiencies of system, and adopt a heuristic method to convert non linear program due to the additional inputs into a linear program. This approach gives more information about the sources of inefficiency by penetrating into the depth of system and modeling the efficiency formation mechanism. A model application is provided. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a new method for reversible data hiding by employing the hierarchical relationships of original images. There are many parameters for accessing the performances of reversible data hiding algorithms, including the output image quality, the hiding capacity, and the overhead for decoding. Considering the ease of implementation and the little overhead needed for decoding, we employ modification of difference values between pixels by using histogram-based scheme with extensions to pyramidal structure by utilizing inherent characteristics of original images. By doing so, global and local characteristics of original images can be utilized for hiding more capacity with acceptable quality of output image. With our method, better performances can be obtained with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and hidden message can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms. [Copyright &y& Elsevier]

Abstract: Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism. [Copyright &y& Elsevier]

Abstract: The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed. [Copyright &y& Elsevier]

Early diagnosis and prevention of problematic behaviors in youth play an important role in reducing treatment costs and decreasing the toll of antisocial behavior. Over the last several years, the science of preventing antisocial behavior in youth has made significant strides, with the development of evidence-based prevention programs (EBP) using randomized clinical trials. In this paper, we use a real case implemented by schools in an urban school district of 80,000 students in a mid-Atlantic state to show how predictive analytics can help to improve the quality of prevention programs and reduce the cost of delivering associated services. Data patterns are extracted from conduct disorder assessments using the Teacher Observation of Classroom Adaptation (TOCA) screening instrument and evaluated using the results of the Diagnostic Interview Schedule for Children (DISC). A mathematical method called Logical Analysis of Data (LAD) is used to analyze data patterns. Experimental results show that up to 91.58% of the cost of administering DISC would be saved by correctly identifying participants without conduct disorder and excluding them from the DISC test. [ABSTRACT FROM AUTHOR]

• Create an efficient model to build different layout and mode of RMFS models. • Model aims at performance evaluating of both uni- and bidirectional path mode RMFS. • Obtained knowledge is useful for designing new systems with fewer experiments. The robotic mobile fulfillment system (RMFS) is a "goods to man" picking system that employs numerous automatic guided vehicles (AGVs) instead of workers to transport goods from the storage station to the picking station. Using smarter AGVs enhances the performance but also leads to layout problems and the challenge of efficiently estimating the system performance with various layouts. With the same capability of storage, the different row and column of storage position area can lead to different system performance. The number of combinations of rows and columns is huge which makes it time-consuming to compare all the combinations to find the best one, and different path modes (unidirectional path mode and bidirectional path mode) and different number of AGVs further complicate the problem. Quickly modeling and performance estimating of RMFS become the biggest challenge for logistics enterprise. In this paper, according to the modular simulation theory, an efficient model based on discrete-event system simulation is proposed for quickly constructing different RMFS layouts and estimating their performance. The model contains two independent components: a physical component and a control component. The nuclear module for physical component is the track module that forms all tracks and can be temporarily or permanently blocked to satisfy the requirements of both unidirectional and bidirectional path mode models. The main functions of the control component are routing, collision prediction, and collision avoidance. The physical and control components are independent but work together to construct and run a certain model according to the input parameters; thus, an RMFS model can be efficiently built by simply entering parameters. A series of experiments were conducted to evaluate the performance of the RMFS in different layouts with various numbers of AGVs in both the unidirectional and bidirectional path modes. The results indicated that in the same layout, the bidirectional path mode can reach the summit output with fewer AGVs. The experimental results also indicated that in a large-scale RMFS, vertically arranging the storage station on the block island and then horizontally positioning the block island can result in a higher output than other layouts. In addition, the experimental results indicated that the proposed model can quickly construct different RMFS layouts and efficiently determine the best scheme. [ABSTRACT FROM AUTHOR]

Abstract: The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown. [Copyright &y& Elsevier]

Abstract: The influences of the network properties on the transfer of knowledge within the network have been extensively studied. However, the “knowledge” properties of the network largely less-attended in literature. In this paper we investigate whether the performance of knowledge transfer in a network can be influenced by adjusting the “knowledge-connection” structure of that network, as a primitive attempt to study knowledge transfer from the aspect of the “knowledge” properties of the network. By the “knowledge-connection” structure we mean the network structure that describes the knowledge distribution within the network. Therefore, the agent-based modeling approach is adopted in this paper to compare the performance of knowledge transfer in a series of networks which differ from one another in their “knowledge-connection” structures. The results of computational simulations illustrate that the network adjustment to increase the knowledge diversity in the directly-connected agent-pairs is helpful for improving the overall performance of knowledge transfer in the entire network in the short term; but the improvement of the long-term performance is less significant. Especially, if the local knowledge-exchange follows the mutually-advantageous bidirectional-knowledge-diffusion (BKD) model, the proposed network adjustment would instead hamper the long-term effectiveness of knowledge transfer. Further investigations show that the limitations can be overcome by adopting a periodical re-adjustment mechanism, through which the knowledge diversity in the network is maintained and persistent knowledge flow becomes possible. [Copyright &y& Elsevier]

Abstract: A virtual enterprise (VE) is an organization intended to cope with the rapidly changing manufacturing environment. Organization building is important in virtual domains because it has largely been affecting the success of VEs. However, the process of forming a VE is based on self-determination by the participants. This paper adopts a bargaining model under a scenario of incomplete information to formalize the formation process, considers the characteristics of the VE formation process, presents the pricing strategies for the corresponding bargaining, and verifies the correctness and validity of the pricing strategies using computer simulation. This paper breaks through the relative research that compares the formation process with partner selection from the core enterprise’s perspective and also provides the basis for the intelligent information platform of VE, whose key part is pricing software. [Copyright &y& Elsevier]

Abstract: The power quality affects the power stability of power company and customers. In order to avoid economic losses caused by the power disturbances, it is necessary to monitor power parameters. This paper aimed at power quality analyses by wavelet transform and proposed a novel algorithm called extension genetic algorithm (EGA). The paper introduced the fundamental theory of wavelet transform, current applications and the theoretical framework of EGA. Then, it described the definition of power quality problems and the characteristics of power waves. Finally, this paper compared the analysis results of EGA and other methods. As the results of simulation, this paper mentioned of methods has a very high accuracy. It can also provide an application tool on power quality and data classification for future researchers. [Copyright &y& Elsevier]

Abstract: Online privacy has consistently been a major concern for customers that has grown commensurately with the growth of e-services. Service providers have responded by making their privacy policies clearer for customers; however, most providers use legacy systems that are unable to actually change and adapt to user’s concerns, which can lead to fewer customers using the system. Hence, the purpose of this paper is to propose a context-aware privacy policy negotiation service. To do so, we adopt a Galois lattice theory to generate policy concepts embedded in e-services. Based on the Galois lattice, we develop a process for generating privacy policy rules. To show the feasibility of the ideas proposed in this paper, we perform a simulation test with two different online auction sites as an illustrative case in terms of two metrics: the number of rules generated and success throughput. Desirable features in applying the Galois lattice approach to context-aware privacy policy negotiation service are discussed. [Copyright &y& Elsevier]

Abstract: The first part of the paper introduces a novel tool for modeling and simulation of discrete event system. This tool called GPenSIM is a Petri net based simulator and offers significant benefits to model builders such as flexibility to include diverse libraries, ease of extending the models, and ease of programming. The second part of the paper presents a case study on modeling and optimization of airport traffic management; this study is to explore air traffic management capability of Evenes airport in Norway. The case study shows that with GPenSIM, modeling and simulation problems of large industrial discrete event systems can be done. Future research directions are discussed as well. [Copyright &y& Elsevier]

Abstract: Security assessment is a major concern in planning and operation studies of a power system. Conventional method of security evaluation performed by simulation involves long computer time and generates voluminous results. This paper presents a K-means clustering approach for classifying power system states as secure/insecure under a given operating condition and contingency. This paper demonstrates how the traditional K-means clustering algorithm can be profitably modified to be used as a classifier algorithm. The proposed algorithm combines particle swarm optimization (PSO) with the traditional K-means algorithm to satisfy the requirements of a classifier. The proposed PSO based K-means clustering technique is implemented in IEEE 30 Bus, 57 Bus, 118 Bus and 300 Bus standard test systems for static security and transient security evaluation. The simulation results of the proposed algorithm are compared with unsupervised K-means clustering, which uses different methods for cluster center initialization. [Copyright &y& Elsevier]

Abstract: This paper proposes two hybrid algorithms, one between particle swarm optimization (PSO) and differential evolution (DE) and second between PSO and quantum infusion (QI). This paper applies these algorithms for digital filter design. PSO algorithm is used as a basis for comparison. Extensive simulation results show the superiority of algorithms developed in this paper. [Copyright &y& Elsevier]

Abstract: This paper addresses the fuzzy version of Capacitated Location Routing Problem (CLRP). The proposed CLRP is defined considering opening costs for depots, limited capacities of vehicles and a set of customers with known demands. There are some potential nodes to locate depots, fuzzy travel times between nodes, and also a time window to meet the demand of each customer. Although there exists several papers, manuscripts and technical reports in the literature concerning Location Routing Problem (LRP) extensions and solution procedures, most of them assume the variables to be crisp. In this paper, we present a multi-depot capacitated LRP (MDCLRP) in which travel time between two nodes is a fuzzy variable. A simulation-embedded Simulated Annealing (SA) procedure is proposed in order to solve the problem. The proposed framework is tested using a standard test problem of MDCLRP and results are analyzed and justified. This numerical example shows that the proposed method is robust and could be used in real world problems. [Copyright &y& Elsevier]

Abstract: In this paper, a new approach of fault diagnosis in analog circuits, which employs the Fractional Wavelet Transform (FWT) to extract fault features and adopts a fuzzy multi-classifier based on the Support Vector Data Description (SVDD) to diagnose circuit faults, is proposed. Firstly, a discrete FWT algorithm by the fractional kernel matrix is performed to preprocess fault samples. To obtain the optimal fractional order, two methods trained with the genetic algorithm are introduced. One approach is performed by the best diagnostic result, and the other is based on the maximum among-cluster center distance by the Kernel Fuzzy C-Means (KFCM) algorithm. In this paper, a threshold value is used to decrease the fuzzy region which in the overlap between hyperspheres of SVDD. Then, a SVDD fuzzy multi-classifier is applied to diagnose faults in analog circuit, and fuzzy faults are diagnosed in fuzzy sets by the relative distance. The simulation results show that the FWT succeeds in extracting local fault features and the classifier effectively detects faults. [Copyright &y& Elsevier]

Abstract: Optimum prediction is a difficult problem, because there are no optimal models for all forecasting problems. In this paper, the authors attempt to find the high precision prediction for grey forecasting model (GM). Considering that chaotic particle swarm optimization algorithm (CPSO) will not get into local optimum and is easy to implement, the paper develops an approach for grey forecasting model, which is particularly suitable for small sample forecasting, based on chaotic particle swarm optimization and optimal input subset which is a new concept. The input subset of traditional time series consists of the whole original data, but the whole original does not always reflect the internal regularity of time series, so the new optimal subset method is proposed to better reflect the internal characters of time series and improve the prediction precision. The numerical simulation result of financial revenue demonstrates that developed algorithm provides very remarkable results compared to traditional grey forecasting model for small dataset forecasting. [Copyright &y& Elsevier]

Abstract: This paper presents a modeling method of the operation process of repairable technical objects of various classes. A particular attention was paid to the model of the process which includes a service expert system with an artificial neural network. The manner was described of the characterization (description) of the operation processes of repairable objects for the purpose of their simulative examination with the use of computer technology. The present paper also includes theoretical grounds of the modeling process of the operation of objects in the form of the following models: mathematical (analytical), graphical and descriptive. For the purpose of the research, an organization model was developed of the servicing system of those technical objects which require short shutdown time (aircrafts, radiolocation systems, etc.). [ABSTRACT FROM AUTHOR]

Abstract: Since the semiconductor manufacturing system is a large-scale complex system, it is difficult to solve complex problems in semiconductor manufacturing by the mathematical modeling method. This paper presents a multi-agent-based distributed simulation platform to support the extremely complex semiconductor manufacturing analysis. A multi-agent-based distributed simulation platform framework and a multi-agent collaborative control model are proposed to provide a flexible infrastructure and a multi-agent coordination mechanism in distributed environment for semiconductor manufacturing simulation. A multi-agent time synchronization model for distributed simulation is designed to keep events in the correct logical time order in simulation and steps of time synchronization are given. An interaction model and message formats are presented to describe how agents communicate with each other in simulation. The platform development and the design of graphical user interface are also exploited in this paper. Finally, evaluation of this multi-agent-based platform was illustrated with a case study. It indicates that the platform is effective in modeling and simulating the complex semiconductor manufacturing and provides the insights about how to improve the semiconductor manufacturing process with well targeted measures. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA. [ABSTRACT FROM AUTHOR]

Abstract: Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies’ behavior. This paper presents design and CMOS implementation of the ant colony optimization based algorithm for solving the TSP problem. In order to implement ant colony optimization algorithm in CMOS, we will present a new algorithm. This algorithm is based on the original ant colony optimization but it can be implemented in CMOS. Briefly, pheromone matrix is transformed on the chip area and ants move up-down through the pheromone matrix and they make their decisions. Finally ants select a global path. In previous researches only pheromone values is used, but select the next city in this paper is based on heuristics value and pheromone value. In definition of problem, we use heuristics value as a matrix. Previous researches could not be used for wide type of optimization problem but our chip gives heuristics value initially and we can change initial value of heuristics value according to the optimization problem so this capability increases the flexibility of ACO chip. Simple circuit is used in blocks of our chip to increase the speed of convergence of ACO chip. We use Linear Feedback Shift Register (LSFR) circuit for random number generator in ACO chip. ACO chip has capability of solving the big TSP problem. ACO chip is simulated by HSPICE software and simulation results show the good performance of final chip. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the development of the data driven approach first introduced in and , which enabled one to obtain a global measure of comparability between treatment groups within a non-experimental framework. This paper points to better formalize the global measure of imbalance reported in and and to introduce a multivariate imbalance test. We consider the global measure of imbalance and the multivariate imbalance test as tools for investigating the dependence relationship between categorical covariates and the assignment-to-treatment indicator variable within a more complex strategy whose final aim is to find balanced groups. We will show in simulated data how the strategy works in practice. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section . Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Customer relationship is increasingly influencing the performance of inventory management in a supply chain, and customer retention or migration directly affect the number of customer orders and demands as well as the inventory level. Therefore, the aim of this paper is to propose a customer satisfaction inventory (CSI) model that incorporates customer relationship management into an inventory model, where the probabilistic concepts of Markov chains of uncertainties in customer relationships of retention or migration are adopted. This enables us to determine both a CSI level for replenishing the inventory level to best fit future customer demand and a customer CSI value of the net profit or loss of an organization from a customer over its purchasing life against the inventory cost of an organization. The modeling and development of the proposed customer satisfaction inventory CSI model are discussed in this paper. Simulation and analysis are conducted for the proposed model, and satisfactory results are achieved, such that the proposed model can help to determine a CSI level that can fully meet orders and demand as well as minimizing inventory cost and maximizing CSI value. [Copyright &y& Elsevier]

Abstract: In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided into several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations. [Copyright &y& Elsevier]

Abstract: The College Entrance Examination in Korea consists of four components: The College Scholastic Ability Test, the student’s high school academic record, an essay, and an interview. Each university has the freedom to assign varying weights to these four components but it is not allowed to consider different levels of various high schools on the reflection of student’s high school academic record. In this paper, we use conjoint analysis to find the way to assign the weights of those four components to effectively recruit the science high school students who have a high quality. We expect that the results of this paper will provide information useful for creating a College Entrance Examination policy that can be used to recruit competent students in Korea. [Copyright &y& Elsevier]

Abstract: A belief rule base inference methodology using the evidential reasoning (RIMER) approach has been developed recently. A belief rule base (BRB), which can be treated as a more generalized expert system, extends traditional IF-THEN rules, but requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees. These parameters need to be determined with care for reliable system simulation and prediction. Some off-line optimization models have been proposed, but it is expensive to train and re-train these models in particular for large-scale systems. Moreover, the recursive algorithms are also proposed to fine tune a BRB online, which require less calculation time and satisfy the real-time requirement. However, the earlier mentioned learning algorithms are all based on a predetermined structure of the BRB. For a complex system, prior knowledge may not be perfect, which leads to the construction of an incomplete or even inappropriate initial BRB structure. Also, too many rules in an initial BRB may lead to over fitting, whilst too few rules may result in under fitting. Consequently, such a BRB system may not be capable of achieving overall optimal performance. In this paper, we consider one realistic and important case where both a preliminary BRB structure and system parameters assigned to given rules can be adjusted online. Based on the definition of a new statistical utility for a belief rule as investigated in this paper, a sequential learning algorithm for online constructing more compact BRB systems is proposed. Compared with the other learning algorithms, a belief rule can be automatically added into the BRB or pruned from the BRB, and our algorithm can also satisfy the real-time requirement. In addition, our algorithm inherits the feature of RIMER, i.e., only partial input and output information is required, which could be either incomplete or vague, either numerical or judgmental, or mixed. In order to verify the effectiveness of the proposed algorithm, a practical case study about oil pipeline leak detection is studied and examined to demonstrate how the algorithm can be implemented. [Copyright &y& Elsevier]

Abstract: Service robots will play an increasing and more important role in the society in the next years. One of the main challenges is to endow robots with enough autonomy to operate on real environments. To reach that goal, the design of controllers to solve simple tasks must be automatized. Engineers look for learning algorithms that are general, robust, require low expertise knowledge, and generate controllers that can run on the real robot without any tuning stage. In this paper, a framework to learn behaviors (controllers) in mobile robotics, fulfilling the previous requirements, has been used. The framework is based on two modules: dataset generation and a data-driven evolutionary-based learning algorithm to obtain fuzzy controllers. Nevertheless, the design of a fuzzy controller still requires the selection of the type of learning algorithm, and also to choose the value of some design parameters. In this paper we present an exhaustive study on a set of evolutionary-based data-driven learning algorithms, for learning fuzzy controllers in mobile robotics, that cover a wide range of the accuracy/interpretability trade-off. The study has also evaluated the influence of the values of all the design parameters over accuracy and interpretability. The objective is to analyze the performance of the different algorithms for the design of behaviors in mobile robotics, and to extract some general rules that can help in the process to design new behaviors. The analysis comprises two different behaviors (wall-following and moving object following) and more than 450 tests, both in simulation and on a Pioneer II AT robot. Results have shown very good performances in complex and realistic conditions for the different combinations of algorithms and parameters. [Copyright &y& Elsevier]

Abstract: In this paper, we derive a new application of fuzzy systems designed for a generalized autoregression conditional heteroscedasticity (GARCH) model. In general, stock market performance is time-varying and nonlinear, and exhibits properties of clustering. The latter means simply that certain large changes tend to follow other large changes, and in general small changes tend to follow other small changes. This paper shows results from using the method of functional fuzzy systems to analyze the clustering in the case of a GARCH model. The optimal parameters of the fuzzy membership functions and GARCH model are extracted using a genetic algorithm (GA). The GA method aims to achieve a global optimal solution with a fast convergence rate for this fuzzy GARCH model estimation problem. From the simulation results, we have determined that the performance is significantly improved if the leverage effect of clustering is considered in the GARCH model. The simulations use stock market data from the Taiwan weighted index (Taiwan) and the NASDAQ composite index (NASDAQ) to illustrate the performance of the proposed method. [Copyright &y& Elsevier]

Abstract: Group conflict that occurs when delivering context-aware services negatively affects service usage within a specific service space, and researchers have proposed few conflict resolution methods to address the issue. This paper presents a methodology to autonomously resolve the conflict arising between users in identical contexts, such as place and computing resources. To do so, a two-phase case-based reasoning method is proposed. To minimize privacy concerns, a social network model is applied to produce key social context data, secondary to personal data, and which is used in place of the users’ personal data for case-based reasoning. To show the feasibility of the idea proposed in this paper, a prototype system with simulation-based validation is tested to determine conflict resolution strategies and a simulation test has been performed. [Copyright &y& Elsevier]

In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method. [Copyright &y& Elsevier]

Abstract: In this paper, the problem of simultaneous and coordinated tuning of brushless exciter and lead-lag power system stabilizer parameters of a single infinite bus power system is considered. This problem is formulated as an optimization problem, which is solved using particle swarm optimization technique. Also in this paper, the optimal tuning of a fuzzy logic power system stabilizer using particle swarm optimization method is carried out. Simulation results show the effectiveness of the proposed particle swarm optimization-based lead-lag power system stabilizer and particle swarm optimization-based fuzzy logic power system stabilizer to damp the oscillation of multimachine system and work effectively under variable loading and fault conditions. [Copyright &y& Elsevier]

This paper investigates how intelligent an agent may utilize a Q-learning approach, a simulation-based stochastic technique, to make optimal dynamic packaging decision in e-retailing setting. When the practical application of dynamic packaging involves a large number of products, normal Q-learning approach would encounter two major problems due to excessively large state space. First, learning the Q-values in tabular form may be infeasible because of the excessive amount of memory needed to store the table. Second, rewards in the state space may be so sparse that with random exploration they will only be discovered extremely slowly. This paper first describes the state-dependent and event-driven nature of the dynamic packaging problem with a Markov decision process model, then proposes a states generalization approach based on distortion measure, and finally puts forward a heuristic based exploration/exploitation policy which is used to improve the convergence in Q-learning. We validate our approach in a simulated test. [Copyright &y& Elsevier]

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper proposes a hybrid methodology that exploits strengths of wavelet analysis and support vector machine model in forecasting time series, and deals with the application of proposed methodology in manufacturing time series forecasting. This method is characteristic of the preprocessing of sample data using wavelet transformation for forecast, i.e., the data sequence of evolvement of share of some sectors in manufacturing is first mapped into several time-frequency domains, and then a support vector machine is established for each domain. The final forecasting results are the algebraic sums of all the forecasted components obtained by respective support vector machine models corresponding to different time-frequency domains. Nevertheless, one of disadvantages of the method is dilemma of selection of values of parameters in support vector machine because the way of selecting values for the parameters will affect the generalization performance remarkably. In this paper, chaos optimization is applied to accomplish selection of values of parameters. Results of experiments based on gross values of textile product in Japan suggest that this hybrid method can both achieve higher accuracy in manufacturing forecasting. [Copyright &y& Elsevier]

We introduce a parsimonious model-based framework for clustering time course data. In these applications the computational burden becomes often an issue due to the large number of available observations. The measured time series can also be very noisy and sparse and an appropriate model describing them can be hard to define. We propose to model the observed measurements by using P-spline smoothers and then to cluster the functional objects as summarized by the optimal spline coefficients. According to the characteristics of the observed measurements, our proposal can be combined with any suitable clustering method. In this paper we provide applications based on non-hierarchical clustering algorithms. We evaluate the accuracy and the efficiency of our proposal by simulations and by analyzing two real data examples. [ABSTRACT FROM AUTHOR]

A number of marketing phenomena are too complex for conventional analytical or empirical approaches. This makes marketing a costly process of trial and error: proposing, imagining, trying in the real world, and seeing results. Alternatively, Agent-based Social Simulation (ABSS) is becoming the most popular approach to model and study these phenomena. This research paradigm allows modeling a virtual market to: design, understand, and evaluate marketing hypotheses before taking them to the real world. However, there are shortcomings in the specialized literature such as the lack of methods, data, and implemented tools to deploy a realistic virtual market with ABSS. To advance the state of the art in this complex and interesting problem, this paper is a seven-fold contribution based on a (1) method to design and validate viral marketing strategies in Twitter by ABSS. The method is illustrated with the widely studied problem of rumor diffusion in social networks. After (2) an extensive review of the related works for this problem, (3) an innovative spread model is proposed which rests on the exploratory data analysis of two different rumor datasets in Twitter. Besides, (4) new strategies are proposed to control malicious gossips. (5) The experimental results validate the realism of this new propagation model with the datasets and (6) the strategies performance is evaluated over this model. (7) Finally, the article is complemented by a free and open-source simulator. [ABSTRACT FROM AUTHOR]

This paper presents the first results of a research which applied a genetic algorithm to calibrate a microscopic traffic simulation model based on speed–density relationships. A large set of traffic data collected from the A22 Freeway, Italy, was used and a comparison was performed between the field measurements and the simulation outputs obtained for a test freeway segment by using the Aimsun microscopic simulator. The calibration was formulated as an optimization problem to be solved based on a genetic algorithm; the objective function was defined in order to minimize the differences between the simulated and real data sets in the speed–density graphs. For this purpose, the genetic algorithm tool in MATLAB ® was applied. Keeping in mind the objective to automatize this process, the optimization technique was attached to Aimsun via a subroutine, so that the data transfer between the two programs could automatically happen. An external script written in Python allowed the MATLAB ® software to interact with Aimsun software. A better match to the field data was reached with the optimization parameters set with the genetic algorithm. In order to check to what extent the model replicated reality, model validation was also addressed. Results showed that a genetic algorithm is usefully applicable in the calibration process of the microscopic traffic simulation model. Beneficial effects are expected by applying the suggested optimization technique since it searches for an optimum set of parameters through an efficient search method. [ABSTRACT FROM AUTHOR]

Theory of constraints (TOC) has emerged as an effective management philosophy suggesting practical solutions to various complex problems including product mix problem. Scholars have shown the superiority of various optimization (and artificial intelligence) approaches over the TOC-based heuristic to a “static” product mix problem, but have failed to see TOC as a whole, i.e. to take into consideration the impact of resource inter-dependencies, feedback loops, and importantly, statistical fluctuations ever present in any dynamic business environment. We proposed a system dynamics (SD) based simulation model to investigate product-mix problem under stochastic demand and scrap rates. We analyzed various scenarios by employing the drum-buffer-rope approach and constraint focused systematic scrap rate reduction approach, and importantly, evaluating performance using throughput accounting based global measures such as throughput and inventory. We conclude this paper by suggesting that future research efforts should be directed to develop an enabling hybrid expert simulation system to learn fundamental and powerful concepts underlying the theory of constraints. [ABSTRACT FROM AUTHOR]

The paper presents the application of a Technical Mapping and tacit knowledge elicitation in industry in order to promote the modeling of tacit knowledge to explicit and represent it in the form of production rules for use in manufacturing processes. The technique was applied with the involved people in the lithographic process in a Metallurgical Company located in southern Brazil. Knowledge of two production coordinators were modeled. For the process of knowledge acquisition and mapping of attributes and values to feed the knowledge base of an expert system, were used quality tools such as Brainstorming, Pareto Chart and Ishikawa Diagram associated with knowledge elicitation techniques such as unstructured interview, rating chips, observation technique, limitation of information and protocol analysis. Quality tools and techniques of knowledge elicitation were systematized to promote process mapping and the elicitation of tacit knowledge, with the aim of representing knowledge by means of production rules. We constructed two knowledge bases with the same methods of production, one in a non-probabilistic expert system (knowledge-based system) and the other in a probabilistic expert system (Bayesian networks) in order to perform comparisons and simulations of the results found. Expert systems perform systematic analysis from the answers given by those involved in lithographic labels process while the defect is identified in order to support the user in diagnosing the root cause of the failure process. From simulations of changes in process variables was possible to prove the hypothesis of the use of probabilistic expert system as industrial support tool in preventing the occurrence of defects in the process and result in a productivity gain. [ABSTRACT FROM AUTHOR]

Through the development of management and intelligent control systems, we can make useful decision by using incoming data. These systems are used commonly in dynamic environments that some of which are been rule-based architectures. Event-Condition-Action (ECA) rule is one of the types that are used in dynamic environments. ECA rules have been designed for the systems that need automatic response to certain conditions or events. Changes of environmental conditions during the time are important factors impacting a reduction of the effectiveness of these rules which are implied by changing users demands of the systems that vary over time. Also, the rate of the changes in the rules are not known which means we are faced with the lack of information about rate of occurrence of new unknown conditions as a result of dynamics environments. Therefore, an intelligent rule learning is required for ECA rules to maintain the efficiency of the system. To the best knowledge of the authors, ECA rule learning has not been investigated. An intelligent rule learning for ECA rules are studied in this paper and a method is presented by using a combination of multi flexible fuzzy tree (MFlexDT) algorithm and neural network. Hence data loss could be avoided by considering the uncertainty aspect. Owing to runtime, speed, and also stream data in dynamic environments, a hierarchical learning model is proposed. We evaluate the performance of the proposed method for resource management in the Grid and e-commerce as case studies by modeling and simulating. A case study is presented to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]