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The notion of multi-granularity has been introduced into various mathematical models in granular computing. For example, neighborhood rough sets can derive a good multi-granularity structure by gradually changing the size of neighborhood radius. Attribute reduction is an important topic in neighborhood rough sets. However, in the case of multi-granularity, the challenge of high computational complexity and difficulty in synthesizing multi-granularity information when performing reduction algorithms always exists. To address such limitations, an accelerated algorithm for multi-granularity reduction is designed. Firstly, we construct a multi-granularity reduction structure with multiple different neighborhood radii to reduce the elapsed time of computing reducts. In this way, the consumed time of calculating the distance is similar to the one of single granularity reduction, and the elapsed time of computing multi-granularity reducts can be reduced. Secondly, multiple granularity information is integrated in each attribute evaluation. Finally, we evaluated the proposed method from multiple perspectives on 12 UCI datasets. Compared with other multi-granularity reduction algorithms, the proposed method not only generates reducts with relatively high quality, but also improves the time efficiency of multi-granularity reduction algorithm. [ABSTRACT FROM AUTHOR]

Successful progression from bench to bedside for regenerative medicine products is challenging and requires a multidisciplinary approach. What has not yet been fully recognised is the potential for quantitative data analysis and mathematical modelling approaches to support this process. In this review, we highlight the wealth of opportunities for embedding mathematical and computational approaches within all stages of the regenerative medicine pipeline. We explore how exploiting quantitative mathematical and computational approaches, alongside state-of-the-art regenerative medicine research, can lead to therapies that potentially can be more rapidly translated into the clinic. [ABSTRACT FROM AUTHOR]

It is a widespread financial practice in procurement management for a supplier aiming to attract customers to offer trade credits when customers make inquiries about the sold products. In this paper, we examine the joint replenishment problem considering trade credits (JRP-TC) under the power-of-two (PoT) policy. We formulate a mathematical model and conduct an analysis that reveals insights into the properties of the optimal objective function. From there, we propose an effective search algorithm that obtains the global optimal solution for the JRP-TC under the PoT policy. Our complexity analysis shows that the JRP-TC and the conventional joint replenishment problem under the PoT policy are pseudo-polynomial time solvable. [ABSTRACT FROM AUTHOR]

This paper aims to develop a recovery planning approach in a three-tier manufacturing supply chain, which has a single supplier, manufacturer, and retailer under an imperfect production environment, in which we consider three types of sudden disturbances: demand fluctuation, and disruptions to production and raw material supply, which are not known in advance. Firstly, a mathematical model is developed for generating an ideal plan under imperfect production for a finite planning horizon while maximizing total profit, and then we re-formulate the model to generate the recovery plan after happening of each sudden disturbance. Considering the high commercial cost and computational intensity and complexity of this problem, we propose an efficient heuristic, to obtain a recovery plan, for each disturbance type, for a finite future period, after the occurrence of a disturbance. The heuristic solutions are compared with a standard solution technique for a considerable number of random test instances, which demonstrates the trustworthy performance of the developed heuristics. We also develop another heuristic for managing the combined effects of multiple sudden disturbances in a period. Finally, a simulation approach is proposed to investigate the effects of different types of disturbance events generated randomly. We present several numerical examples and random experiments to explicate the benefits of our developed approaches. Results reveal that in the event of sudden disturbances, the proposed mathematical and heuristic approaches are capable of generating recovery plans accurately and consistently. [ABSTRACT FROM AUTHOR]

Based on cylindrical algebraic decomposition (CAD) technique, an algorithm for solving the stable parameter region of factional-order systems with structured perturbations (FOSSP) is presented. The algorithm is nonconservative and universal for fractional order systems with order 0 < α < 2. And the computational complexity of the algorithm is less than existing methods. Two examples are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Resource-conscious technologies for cutting sheet material include the ICP and ECP technologies that allow for aligning fragments of the contours of cutouts. In this work, we show the mathematical model for the problem of cutting out parts with these technologies and algorithms for finding cutting tool routes that satisfy technological constraints. We give a solution for the problem of representing a cutting plan as a plane graph G = ( V, F, E), which is a homeomorphic image of the cutting plan. This has let us formalize technological constraints on the trajectory of cutting the parts according to the cutting plan and propose a series of algorithms for constructing a route in the graph G = ( V, F, E), which is an image of an admissible trajectory. Using known coordinates of the preimages of vertices of graph G = ( V, F, E) and the locations of fragments of the cutting plan that are preimages of edges of graph G = ( V, F, E), the resulting route in the graph G = ( V, E) can be interpreted as the cutting tool's trajectory. The proposed algorithms for finding routes in a connected graph G have polynomial computational complexity. To find the optimal route in an unconnected graph G, we need to solve, for every dividing face f of graph G, a travelling salesman problem on the set of faces incident to f. [ABSTRACT FROM AUTHOR]

Aiming to accelerate convergence speed and reduce steady-state misalignment of adaptive filter, a new data-reusing algorithm is proposed. Different from conventional affine projection algorithm (APA) based on minimum disturbance principle (MDP), the proposed algorithm obtains its cost function based on a convex combination of the minimum norm principle (MNP) and the MDP. Weight factor (named as mixing parameter) used in the combination determines the approach for updating. The proposed algorithm has a performance similar to the APA when the mixing parameter closes to 0 and yields results to the data-reusing algorithm based on the MNP if the mixing parameter closes to 1. By minimizing mean-square deviation, the variable mixing parameter has a large value at initial stage and gradually decreases as iteration number increases. At the initial stage, the proposed algorithm achieves fast convergence since the MNP plays a leading role. At the steady-state, the proposed algorithm obtains a low misalignment since the MDP gives a major impact and the updated results have minimum disturbance from the previous ones. At the transition stage, the proposed algorithm is a combination of the APA and the MNP-based data-reusing algorithm. Simulation results show that the proposed algorithm exhibits faster convergence rate and lower steady-state misalignment than some other derivatives of the APA without significantly increasing computational complexity. [ABSTRACT FROM AUTHOR]

A line integration method (LIM) is proposed to calculate the domain integrals for 3D problems. In the proposed method, the domain integrals are transformed into boundary integrals and only line integrals on straight lines are needed to be computed. A background cell structure is applied to further simplify the line integrals and improve the accuracy. The method creates elements only on the boundary, and the integral lines are created from the boundary elements. The procedure is quite suitable for the boundary element method, and we have applied it to 3D situations. Directly applying the method is time-consuming since the complexity of the computational time is O( NM), where N and M are the numbers of nodes and lines, respectively. To overcome this problem, the fast multipole method is used with the LIM for large-scale computation. The numerical results show that the proposed method is efficient and accurate. [ABSTRACT FROM AUTHOR]

Population protocols (Angluin et al. in PODC, 2004) are a formal model of sensor networks consisting of identical mobile devices. Two devices can interact and thereby change their states. Computations are infinite sequences of interactions satisfying a strong fairness constraint. A population protocol is well specified if for every initial configuration C of devices, and every computation starting at C, all devices eventually agree on a consensus value depending only on C. If a protocol is well specified, then it is said to compute the predicate that assigns to each initial configuration its consensus value. While the computational power of well-specified protocols has been extensively studied, the two basic verification problems remain open: Is a given protocol well specified? Does a given protocol compute a given predicate? We prove that both problems are decidable by reduction to the reachability problem of Petri nets. We also give a new proof of the fact that the predicates computed by well-defined protocols are those definable in Presburger arithmetic (Angluin et al. in PODC, 2006). [ABSTRACT FROM AUTHOR]

Smale's 17th problem asks for an algorithm which finds an approximate zero of polynomial systems in average polynomial time (see Smale in Mathematical problems for the next century, American Mathematical Society, Providence, 2000). The main progress on Smale's problem is Beltrán and Pardo (Found Comput Math 11(1):95-129, 2011) and Bürgisser and Cucker (Ann Math 174(3):1785-1836, 2011). In this paper, we will improve on both approaches and prove an interesting intermediate result on the average value of the condition number. Our main results are Theorem 1 on the complexity of a randomized algorithm which improves the result of Beltrán and Pardo (2011), Theorem 2 on the average of the condition number of polynomial systems which improves the estimate found in Bürgisser and Cucker (2011), and Theorem 3 on the complexity of finding a single zero of polynomial systems. This last theorem is similar to the main result of Bürgisser and Cucker (2011) but relies only on homotopy methods, thus removing the need for the elimination theory methods used in Bürgisser and Cucker (2011). We build on methods developed in Armentano et al. (2014). [ABSTRACT FROM AUTHOR]

In this paper, we propose a procedure for detecting multiple change-points in a mean-shift model, where the number of change-points is allowed to increase with the sample size. A theoretic justification for our new method is also given. We first convert the change-point problem into a variable selection problem by partitioning the data sequence into several segments. Then, we apply a modified variance inflation factor regression algorithm to each segment in sequential order. When a segment that is suspected of containing a change-point is found, we use a weighted cumulative sum to test if there is indeed a change-point in this segment. The proposed procedure is implemented in an algorithm which, compared to two popular methods via simulation studies, demonstrates satisfactory performance in terms of accuracy, stability and computation time. Finally, we apply our new algorithm to analyze two real data examples. [ABSTRACT FROM AUTHOR]

Logit models are popular tools for analyzing discrete choice and ranking data. The models assume that judges rate each item with a measurable utility, and the ordering of a judge's utilities determines the outcome. Logit models have been proven to be powerful tools, but they become difficult to interpret if the models contain nonlinear and interaction terms. We extended the logit models by adding a decision tree structure to overcome this difficulty. We introduced a new method of tree splitting variable selection that distinguishes the nonlinear and linear effects, and the variable with the strongest nonlinear effect will be selected in the view that linear effect is best modeled using the logit model. Decision trees built in this fashion were shown to have smaller sizes than those using loglikelihood-based splitting criteria. In addition, the proposed splitting methods could save computational time and avoid bias in choosing the optimal splitting variable. Issues on variable selection in logit models are also investigated, and forward selection criterion was shown to work well with logit tree models. Focused on ranking data, simulations are carried out and the results showed that our proposed splitting methods are unbiased. Finally, to demonstrate the feasibility of the logit tree models, they were applied to analyze two datasets, one with binary outcome and the other with ranking outcome. [ABSTRACT FROM AUTHOR]

The carrier frequency offset (CFO), destroying the orthogonality between subcarriers, greatly degrades the performance of an orthogonal frequency-division multiplexing system. One of the most frequently used ways for a CFO estimator design is to adopt maximum-likelihood (ML) estimation, achieving high accuracy tightly close to the Cramer-Rao lower bounds (CRLBs). One of the ML-based algorithms, called linearly-combined CFO (LC-CFO), evaluates all the single-time-slot CFO estimates first and then linearly combined these CFO estimates in the minimum mean-square-error sense. Its tracking range is quite wide up to half the carrier spacing, and convergence speed is very fast, costing only several tens of iterations; moreover, its mean-square error (MSE) performances are very much close to CRLBs at medium-to-high signal-to-noise ratio (SNR) values. However, a set of arctangent functions is needed to be evaluated for each iteration, which increases the computational complexity. In this article, a low complexity, called simplified LC-CFO (SLC-CFO), is proposed that the set of arctangent functions are replaced by low-complexity limiters, resulting in simplifying the receiver design and reducing the computational load while keeping nearly the same tracking range and MSE performances. With proper choice of a parameter, SLC-CFO even shows faster convergence speed and lower MSE value at low SNR, compared with LC-CFO. Simulation results demonstrate all these aforementioned properties. [ABSTRACT FROM AUTHOR]

In this paper, we use the properties of Chebyshev polynomials, elementary methods, and combinational techniques to study the computational problem of one kind of convolution sums involving second kind Chebyshev polynomials, and we give an exact computational method, which expresses the sums as second kind Chebyshev polynomials. As some applications of our results, we also obtain several new identities and congruences involving the second kind Chebyshev polynomials, Fibonacci numbers, and Lucas numbers. [ABSTRACT FROM AUTHOR]

In the paper 'The complexity of mean flow time scheduling problems with release times', by Baptiste, Brucker, Chrobak, Dürr, Kravchenko and Sourd, the authors claimed to prove strong $${\mathbb {NP}}$$ -hardness of the scheduling problem $$P|{\textit{pmtn}},r_j|\sum C_j$$ , namely multiprocessor preemptive scheduling where the objective is to minimize the mean flow time. We point out a serious error in their proof and give a new proof of strong $${\mathbb {NP}}$$ -hardness for this problem. [ABSTRACT FROM AUTHOR]

In this paper, we address a deterministic scheduling problem in which two agents compete for the usage of a single machine. Each agent decides on a fixed order to submit its tasks to an external coordination subject, who sequences them according to a known priority rule. We consider the problem from different perspectives. First, we characterize the set of Pareto-optimal schedules in terms of size and computational complexity. We then address the problem from the single-agent point-of-view, that is, we consider the problem of deciding how to submit one agent's tasks only taking into account its own objective function against the other agent, the opponent. In this regard, we consider two different settings depending on the information available to the agents: In one setting, the considered agent knows in advance all information about the submission sequence of the opponent; and in the second setting (as in minimax strategies in game theory), the agent has no information on the opponent strategy and wants to devise a strategy that minimizes its solution cost in the worst possible case. Finally, we assess the performance of some classical single-agent sequencing rules in the two-agent setting. [ABSTRACT FROM AUTHOR]

Heterogeneous analytical models are valuable tools that facilitate optimal application tuning via runtime prediction; however, they require several man-hours of effort to understand and employ for meaningful performance prediction. Consequently, developers face the challenge of selecting adequate performance models that best fit their design goals and level of system knowledge. In this research, we present a classification that enables users to select a set of easy-to-use and reliable analytical models for quality performance prediction. These models, which target the general-purpose graphical processing unit (GPGPU)-based systems, are categorized into two primary analytical classes: subjective-analytical and objective-analytical. The subjective-analytical models predict the computation and communication components of an application by describing the system using minimum qualitative relations among the system parameters; whereas the objective-analytical models predict these components by measuring pertinent hardware events using micro-benchmarks. We categorize, enhance, and characterize the existing analytical models for GPGPU computations, network-level, and inter-connect communications to facilitate fast and reliable application performance prediction. We also explore a suitable combination of the aforementioned analytical classes, the hybrid approach, for high-quality performance prediction and report prediction accuracy up to 95 % for several tested GPGPU cluster configurations. The research aims to ultimately provide a collection of easy-to-select analytical models that promote straightforward and accurate performance prediction prior to large-scale implementation. [ABSTRACT FROM AUTHOR]

In practice nurse rostering problems are often too complex to be expressed through available academic models. Such models are not rich enough to represent the variegated nature of real world scenarios, and therefore have no practical relevance. This article focuses on two particular modelling issues that require careful consideration in making academic nurse rostering approaches re-usable in a real world environment. First: introducing several complex problem characteristics, resulting in a rich, generic model. A detailed description is provided for researchers interested in using this new model. We also present a novel benchmark dataset based on this rich model. Second: the consideration of a consistent evaluation procedure that corresponds to realistic quality measurement. These contributions will enable faster implementation of academic nurse rostering achievements in real hospital environments. A suite of hyper-heuristics is presented. These are capable of solving these rich personnel rostering problems using the presented evaluation procedures. Their performance is compared to that of another meta-heuristic. [ABSTRACT FROM AUTHOR]

A new modeling method, related to multiple inputs and multiple outputs (MIMO), simultaneously based on Gaussian process (GP), is proposed to optimize the combinations of process parameters and improve the quality control for multi-objective optimization problems in sheet metal forming. In the MIMO surrogate model, for the use of the system information in processing and the accuracy of the model, quantitative and categorical input variables are both taken into account in GP simultaneously. Firstly, a general method is proposed for constructing covariance functions for GP simultaneous MIMO surrogate model based on correlation matrices. These covariance functions must be able to incorporate the valid definitions of both the spatial correlation based on quantitative input variables and the cross-correlation based on categorical input variables. Secondly, the unrestrictive correlation matrices are constructed by the hypersphere decomposition parameterization, thus directly solving optimization problems with positive definite constraints is needless, and the computational complexity is simplified. Compared with independent modeling method, the proposed GP simultaneous MIMO model has higher accuracy and needs less number of estimated parameters. Moreover, the cross-correlation between the outputs (quality indexes) obtained by proposed model provides some reference to further develop quality intelligent control strategies. Finally, a drawing-forming process of auto rear axle housing is taken as an example to validate the proposed method. The results show that the proposed method can effectively decrease the crack and wrinkle in sheet metal forming. [ABSTRACT FROM AUTHOR]

Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this work, a brief overview of different vehicle crash modeling methodologies is proposed. The acceleration of a colliding vehicle is measured in its center of gravity-this crash pulse contains detailed information about vehicle behavior throughout a collision. A virtual model of a collision scenario is established in order to provide an additional data set further used to evaluate a suggested approach. Three different approaches are discussed here: lumped parameter modeling of viscoelastic systems, data-based approach taking advantage of neural networks and autoregressive models and wavelet-based method of signal reconstruction. The comparative analysis between each method's outcomes is performed and reliability of the proposed methodologies and tools is evaluated. [ABSTRACT FROM AUTHOR]

Economic forces, driven by the desire to introduce flash into the high-end storage market without changing existing software-base, have resulted in the emergence of solid-state drives (SSDs), flash packaged in HDD form factors and capable of working with device drivers and I/O buses designed for HDDs. Unlike the use of DRAM for caching or buffering, however, certain idiosyncrasies of NAND Flash-based solid-state drives (SSDs) make their integration into hard disk drive (HDD)-based storage systems nontrivial. Flash memory suffers from limits on its reliability, is an order of magnitude more expensive than the magnetic hard disk drives (HDDs), and can sometimes be as slow as the HDD (due to excessive garbage collection (GC) induced by high intensity of random writes). Given the complementary properties of HDDs and SSDs in terms of cost, performance, and lifetime, the current consensus among several storage experts is to view SSDs not as a replacement for HDD, but rather as a complementary device within the high-performance storage hierarchy. Thus, we design and evaluate such a hybrid storage system with HybridPlan that is an improved capacity planning technique to administrators with the overall goal of operating within cost-budgets. HybridPlan is able to find the most cost-effective hybrid storage configuration with different types of SSDs and HDDs [ABSTRACT FROM AUTHOR]

In this paper the capacitated lot sizing and scheduling problem with sequence-dependent setups, setup carryover, and backlogging has been studied. The problem can be formulated as a mixed-integer program. Most lot sizing problems are hard to solve, especially in medium and large scale. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed metaheuristic approaches to lot sizing problems. One of the most popular metaheuristics is genetic algorithm which has been applied to different optimization problems successfully. Therefore, we have developed a genetic algorithm to solve this model. To test the accuracy of the genetic algorithm, a lower bound is developed and compared against the genetic algorithm. In computational experiments, proposed genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for this problem. [ABSTRACT FROM AUTHOR]

We consider a multistage processing system, which includes both identical (parallel) machines that can process the same set of operations and different machines that can process only different operation sets. A release time r is given for each job J to be processed. For such a processing system, we minimize the makespan, i.e., problem $IJ|r_i|C_{\max }$ is considered. The problem $IJ|r_i|C_{\max }$ is an extension of the classical job-shop problem $J|r_i|C_{\max }$ for the case when parallel (or identical) machines are also given. Both problems $J|r_i|C_{\max }$ and $IJ|r_i|C_{\max }$ are strongly NP-hard. A mixed graph model used for solving the problem $J|r_i|C_{\max }$ is generalized for the problem $IJ|r_i|C_{\max }$. Using the mixed graph model, we developed a fast heuristic algorithm for solving the problem $IJ|r_i|C_{\max }$. Computational experiments were conducted to evaluate the performance of the algorithm on the 22 benchmark instances and on the 40 new randomly generated instances of the problem $IJ||C_{\max }$. For the small and moderate instances, the exact values of the objective function were compared with those calculated by the proposed heuristic algorithm. The average relative error was not greater than $2.4~\%$ for all instances with available optimal schedules. Computational results showed that the developed algorithm runs faster than some other heuristics being tested, and the schedules constructed by the developed algorithm have smaller makespans. [ABSTRACT FROM AUTHOR]

A matrix M is said to be k-anonymous if for each row r in M there are at least k − 1 other rows in M which are identical to r. The NP-hard k- Anonymity problem asks, given an n × m-matrix M over a fixed alphabet and an integer s > 0, whether M can be made k-anonymous by suppressing (blanking out) at most s entries. Complementing previous work, we introduce two new 'data-driven' parameterizations for k- Anonymity-the number t of different input rows and the number t of different output rows-both modeling aspects of data homogeneity. We show that k- Anonymity is fixed-parameter tractable for the parameter t, and that it is NP-hard even for t = 2 and alphabet size four. Notably, our fixed-parameter tractability result implies that k- Anonymity can be solved in linear time when t is a constant. Our computational hardness results also extend to the related privacy problems p- Sensitivity and ℓ- Diversity, while our fixed-parameter tractability results extend to p- Sensitivity and the usage of domain generalization hierarchies, where the entries are replaced by more general data instead of being completely suppressed. [ABSTRACT FROM AUTHOR]

Complex real-time systems exhibit dynamic behaviours on many different time levels. To cope with the wide range of time scales and produce more dependable computer-based systems, we develop a Timebands model that can explicitly recognise a finite set of distinct time bands in which temporal properties and associated behaviours are described. In order to formalise the Timebands model, we propose a new timed model, named Timed Circus, of Circus, which is the combination of Z, CSP, and the refinement calculus in the setting of Unifying Theories of Programming. Different from most approaches such as Timed CSP, Timed Circus uses a complete lattice in the implication ordering to model the distinctive features of the Timebands model. As a result, the semantics of the Timebands model is built upon Timed Circus to guarantee soundness of each operator and maintain consistency and coordination between different time bands. By means of two small systems, we demonstrate how the Timebands model contributes to describing complex real-time systems with multiple time scales. [ABSTRACT FROM AUTHOR]

Formal and symbolic techniques are extremely useful for modelling and analysing security protocols. They have helped to improve our understanding of such protocols, allowed us to discover flaws, and they also provide support for protocol design. However, such analyses usually consider that the protocol is executed in isolation or assume a bounded number of protocol sessions. Hence, no security guarantee is provided when the protocol is executed in a more complex environment. In this paper, we study whether password protocols can be safely composed, even when a same password is reused. More precisely, we present a transformation which maps a password protocol that is secure for a single protocol session (a decidable problem) to a protocol that is secure for an unbounded number of sessions. Our result provides an effective strategy to design secure password protocols: (i) design a protocol intended to be secure for one protocol session; (ii) apply our transformation and obtain a protocol which is secure for an unbounded number of sessions. Our technique also applies to compose different password protocols allowing us to obtain both inter-protocol and inter-session composition. [ABSTRACT FROM AUTHOR]

In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question. [ABSTRACT FROM AUTHOR]

This paper presents the testing automation procedure for automatic control systems of gas-turbine units used as drives in small-size power stations. We substantiate the applicability of mathematical modeling techniques for power systems in testing automation problems of the control systems. Finally, multitasking mathematical models are derived, and a simulation software complex is developed for testing automation of the control systems; this is done by employing the mathematical model of a power system with an arbitrary configuration and structural elements. [ABSTRACT FROM AUTHOR]

In this paper, we first investigate a semi-automated automotive engine assembly line in which the traditional strategy of using fixed workers in each manual assembly section is replaced by a new strategy of using walking workers. With this approach, both worker and engine travel simultaneously down the line; each worker is previously trained to accomplish a series of assembly tasks independently from start to finish in each manual assembly section. The study has shown great improvement of the overall system performance in terms of flexibility, efficiency, responsiveness and re-configurability using dynamic, flexible and skilled walking workers. Nevertheless, the main problem of this design is that each worker needs to be cross-trained to acquire a satisfactory level of skills associated with the assignment of assembly tasks. This is crucial for achieving a relatively even working speed at which each worker assembles a product down the line without major interruption between two adjacent workstations. In theory, the familiar degree of completing assigned tasks by each worker through training can be measured and expressed as a learning curve. In this case, the learning curve has been used to determine a trade-off decision between the complexity of assigned tasks and the duration of completing these tasks by a walking worker at a stabilised level. It has also been used to investigate the impact of the system variation that may affect the performance of individual walking workers through a learning process. Thus, the paper also describes a framework to assess the human performance by modelling the learning curve for each walking worker based in an integrated model. This model was created using a simulation tool Witness with its key input/output data manipulated externally by a series of Microsoft Excel worksheets incorporating the effect of a number of human factors in terms of cognitive and physical elements. With this method, the possible and realistic assignment of selected assembly tasks for each walking worker can be quantified. [ABSTRACT FROM AUTHOR]

The numerical control (NC) program for multi-axis end milling depends on the parameters of the tool, in particular, the radius of the tool. When there is any tool dimensional change due to various reasons, the user needs to re-generate the NC program, which is a time-consuming procedure. In this article, a cutter radius compensation method for multi-axis end milling is proposed. It takes a general NC program as the input, recovers the normal vectors of the machined surface from the NC program via surface reconstruction, and uses these vectors as compensation vectors to realize space cutter radius compensation. The proposed algorithm of shape reconstruction and normal vector computation has a linear complexity in terms of the number of cutter center location points in the NC program. Thus, real-time computation and compensation is possible. Our method also provides a way to restore the machined surface if the CAD model of the machines surface is not accessible. This has other applications, such as interference detection and manufacturing simulation. The compensation algorithm is shown to be very effective in reducing the number of undercut points through simulation with the software VERICUT and with real milling for real-world NC programs. [ABSTRACT FROM AUTHOR]

Jobs with different processing complexity may have different effects on human learning, which in turn will affect the job processing times. So, the learning effect on job processing times in scheduling is not only sum-of-processing-time-based and job-position-based, but also depends on the processing complexity of the jobs already processed. We introduce in this paper a general learning effect model for scheduling. We provide some properties that are helpful for finding the optimal solutions for some single-machine and flowshop scheduling problems under the general effect model. [ABSTRACT FROM AUTHOR]

This paper investigates how firms can use synergy to optimize their information technology portfolios. We begin by developing a framework for the portfolio selection by identifying three types of information technology synergy. Next, we use this framework to examine the impact of different types of synergy on the portfolio selection. Analytical models are developed to illustrate the roles of different types of the synergy, and analytical and computational methods are used to investigate the impact of the synergy. The analysis in this paper provides conditions in which synergy enhancement results in a more efficient or a less efficient portfolio. Our study establishes that firms with higher risk thresholds are more likely to obtain more efficient information technology portfolios by enhancing synergy, whereas firms with lower risk thresholds are less likely to benefit from enhancing synergy. [ABSTRACT FROM AUTHOR]

The basic attributes of any industrial machine are productivity rate, quality of products, as well as the cost and flexibility of manufacturing systems. Attributes of productivity are related to the theory of reliability, let alone the theory of efficiency of machines. The main attribute of reliability with any industrial machine is availability, which is the integrated index of the machine's reliability. The availability of industrial machines with complex designs should be a constituent of equations on productivity rate. This paper develops analytical equations of productivity rate with the availability of automated lines as a function of structural, technical and technological parameters of industrial machines. The equations allow for the output of automated lines to be modelled and their structures to be defined according to the level of productivity and availability. [ABSTRACT FROM AUTHOR]

Annualised hours (AH) is a method for meeting seasonal demand which involves irregular distribution of working hours. This paper considers an AH scenario where different category of workers are contracted to work for a certain number of hours per year and each category has different efficiencies for different types of task. The number of workers required is estimated based on the forecasted hours considering the same efficiency for all types of task by a worker. In a number of cases, tasks are too difficult to be performed by temporary workers. In situations where overtime or hiring temporary workers are not considered, capacity shortage is possible during certain weeks as a result of the relative efficiency. The relative capacity shortage, which is defined as the capacity shortage related to the required capacity, has to be minimised. This paper proposes an AH planning problem using linear programming model to minimise relative capacity shortage in which workers are considered to be cross trained. The computational demonstration shows the effectiveness of the model. [ABSTRACT FROM AUTHOR]

First-order applicative rewrite systems provide a natural framework for modeling higher-order aspects. In this article we present a transformation from untyped applicative term rewrite systems to functional term rewrite systems that preserves and reflects termination. Our transformation is less restrictive than other approaches. In particular, head variables in right-hand sides of rewrite rules can be handled. To further increase the applicability of our transformation, we study the method for innermost rewriting and derivational complexity, and present a version for dependency pairs. [ABSTRACT FROM AUTHOR]

We consider linear approximations of an iterated block cipher in the presence of several strong linear approximation trails. While the effect of such trails in Matsui's Algorithm 2, also called the linear hull effect, has been previously studied by a number of authors, their effect on Matsui's Algorithm 1 has not been investigated until now. The goal of this paper is to fill this gap and examine how to generalize Matsui's Algorithm 1 to work also on linear hulls. We restrict to key-alternating ciphers and develop a mathematical framework for this kind of attacks. The complexity of the attack increases with the number of linear trails that have significant contribution to the correlation. We show how to reduce the number of trails and thus the complexity using related keys. Further, we illustrate our theory by experimental results on a reduced round version of the block cipher PRESENT. [ABSTRACT FROM AUTHOR]

Frequency-domain (FD) adaptive filter algorithms are able to achieve a low computational complexity by using the overlap-and-save implementation means compared to time-domain (TD) ones. In this article, we propose a new FD least-mean-square (FD-LMS) algorithm which dynamically selects frequency bins in order to reduce the computational complexity and maintain the convergence performance of the conventional FD-LMS. The optimal selection of frequency bins is derived by the largest decrease between the successive FD mean square deviations (MSDs) at every data block. Simulation results show that the proposed algorithm provides a low steady-state normalized MSD (NMSD) and similar convergence rate compared to the conventional FD-LMS algorithm. In addition, it gains a low computational complexity. [ABSTRACT FROM AUTHOR]

In the study of random access machines (RAMs) and the complexities associated with their algorithms, the availability of indirect addressing often creates an analysis obstacle. We show that for RAMs equipped with a sufficiently rich set of basic operations, indirect addressing does not increase computational power, and can be simulated either in linear time or on-line in real time. These results pertain to the uniform cost model and, particularly, assume a unit cost variable shift. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the use of Multiple Background Models (M-BMs) in Speaker Verification (SV). We cluster the speakers using either their Vocal Tract Lengths (VTLs) or by using their speaker specific Maximum Likelihood Linear Regression (MLLR) super-vector, and build a separate Background Model (BM) for each such cluster. We show that the use of M-BMs provide improved performance when compared to the use of a single/gender wise Universal Background Model (UBM). While the computational complexity during test remains same for both M-BMs and UBM, M-BMs require switching of models depending on the claimant and also score-normalization becomes difficult. To overcome these problems, we propose a novel method which aggregates the information from Multiple Background Models into a single gender independent UBM and is inspired by conventional Feature Mapping (FM) technique. We show that using this approach, we get improvement over the conventional UBM method, and yet this approach also permits easy use of score-normalization techniques. The proposed method provides relative improvement in Equal-Error Rate (EER) by 13.65 % in the case of VTL clustering, and 15.43 % in the case of MLLR super-vector when compared to the conventional single UBM system. When AT-norm score-normalization is used then the proposed method provided a relative improvement in EER of 20.96 % for VTL clustering and 22.48 % for MLLR super-vector based clustering. Furthermore, the proposed method is compared with the gender dependent speaker verification system using Gaussian Mixture Model-Support Vector Machines (GMM-SVM) super-vector linear kernel. The experimental results show that the proposed method perform better than gender dependent speaker verification system. [ABSTRACT FROM AUTHOR]

In this paper, we analyse single machine scheduling problems with learning and aging effects to minimize one of the following objectives: the makespan with release dates, the maximum lateness and the number of late jobs. The phenomena of learning and aging are modeled by job processing times described by non-increasing (learning) or non-decreasing (aging) functions dependent on the number of previously processed jobs, i.e., a job position in a sequence. We prove that the considered problems are strongly NP-hard even if job processing times are described by simple linear functions dependent on a number of processed jobs. Additionally, we show a property of equivalence between problems with learning and aging models. We also prove that if the function describing decrease/increase of a job processing time is the same for each job then the problems with the considered objectives are polynomially solvable even if the function is arbitrary. Therefore, we determine the boundary between polynomially solvable and strongly NP-hard cases. [ABSTRACT FROM AUTHOR]

For more than three decades, empirical analysis of stochastic dominance was restricted to settings with mutually exclusive choice alternatives. In recent years, a number of methods for testing efficiency of diversified portfolios have emerged, which can be classified into three main categories: (1) majorization, (2) revealed preference and (3) distribution-based approaches. Unfortunately, some of these schools of thought are developing independently, with little interaction or cross-referencing among them. Moreover, the methods differ in terms of their objectives, the information content of the results and their computational complexity. As a result, the relative merits of alternative approaches are difficult to compare. This paper presents the first systematic review of all three approaches in a unified methodological framework. We examine the main developments in this emerging literature, critically evaluating the advantages and disadvantages of the alternative approaches. We also point out some misleading arguments and propose corrections and improvements to some of the methods considered. [ABSTRACT FROM AUTHOR]

As to learning effect, it may be more appropriate to assume that position-based learning takes place during machine setups only, while sum-of-processing-time-based learning occurs in considering the experience that workers have gained from producing jobs. Thus, in this paper, we consider sum-of-processing-time-based learning on job processing time and position-based learning on setup time in single-machine group scheduling problems. The objectives are to minimize the makespan and the total completion time, respectively. We provide two polynomial time algorithms to solve the makespan minimization problems. On the other hand, we also provide two polynomial time algorithms to solve the total completion time minimization problems under certain conditions. [ABSTRACT FROM AUTHOR]

The increasing variety of products complicates the mixed-model assembly process and affected the mixed-model assembly system in terms of product quality and productivity. Choice complexity comes from the process of making choices for various assembly operations due to the product variety and impacts production rate which is the performance measure of the system. The choice complexity is measured with information entropy, and the relational expression between choice complexity and error rate is analyzed by means of those research finds on average reaction time and speed-accuracy trade-off. The main achievement of our study is establishing an artificial neural network meta-model for the impact of choice complexity on production rate. The meta-model performs better than a multiple linear regression meta-model in terms of experiment results and appears to be the optimal model of the impact of choice complexity on production rate in the mixed-model assembly system. [ABSTRACT FROM AUTHOR]

The majority of current feature-based modeling systems employ model history to store feature creation operations and re-execute them sequentially after each modification, which is called history-based modeling. Due to the static feature creation order, history-based modeling causes some problems during the reevaluation of the model, such as the evaluated model does not correspond to its specification, the operation can only refer to the boundary entities created by the previous operations, high computation cost, etc. In this research work, a new history-independent modeling approach is proposed. For the 'remove feature' operation, only boundary faces originating from the feature being removed, and the intersecting features are modified for updating the resulting BRep model. For the 'modify feature' operation, the feature being modified is first removed from the product model and then re-added with the new specifications, thus its creation step in the model history is changed. As a result, some of the problems caused by the static feature creation order are solved. During the reevaluation, the computation time is dependent solely on the execution of the feature and its intersecting features being edited. The computational complexity of the boundary evaluation using the proposed method for three representative models has been analyzed based on Open CASCADE. A case model was studied in a proof-of-concept prototype system to demonstrate the proposed modeling approach. [ABSTRACT FROM AUTHOR]

In this paper, we analyze some single machine scheduling problems with the aging effect. We extend the sum-of-processing-time-based aging model such that the fatigue caused by each job to the machine is equal to a non-increasing function dependent on the normal processing time of a job and the aging effect is job dependent. Although the proposed model is more general and describes more precisely the real-life settings, we show that the special cases of the maximum completion time and the maximum lateness minimization problems with this model are still polynomially solvable. However, we prove the maximum completion time minimization problem with the sum-of-processing-time-based aging model is strongly NP-hard if some jobs have deadlines and constant processing times. On this basis, we show that the maximum lateness minimization problem with this aging model is also strongly NP-hard. [ABSTRACT FROM AUTHOR]

This paper studies mathematical modeling of motion of a nonlinearly viscous fluid in the case of strong solutions. [ABSTRACT FROM AUTHOR]

In this paper, the problem of speech deconvolution is solved. This problem is encountered in limited-bandwidth speech communication systems such as telephone systems. Three solutions are presented for this problem. In the first solution, a Linear Minimum Mean Square Error (LMMSE) approach is used. The necessary assumptions required to reduce the computational complexity of the LMMSE solution are presented. In the second solution, an inverse filter deconvolution approach is presented. Finally, the regularization theory is used to solve this problem. The common thread between all these solutions is that they treat the speech deconvolution problem as an inverse problem considering the speech degradation model. Simulation results reveal the superiority of these solutions for solving the speech deconvolution problem. [ABSTRACT FROM AUTHOR]

Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Multi-period project portfolio selection problems can be modeled by multistage stochastic programs. These models utilize a set of scenarios and corresponding probabilities to model the multivariate random data process (costs or revenues, available budget, chance of success). For most practical problems, the optimization problem that contains all possible scenarios is too large. Due to computational complexity, this program is often approximated by a model involving a (much) smaller number of scenarios. The scenario reduction algorithms determine a subset of the initial scenario set and assign new probabilities to the preserved scenarios such that optimal values and approximate solution sets remain close to each other. This paper presents a stochastic optimization model of pharmaceutical multi-period project selection problem using a real-option valuation method. Subsequently, it reduces scenarios via existing algorithms to generate a new (smaller) scenario tree. Model with reduced scenarios has been solved in this research. Numerical results indicate improved portfolios and show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Fast and accurate fitting of non-uniform rational B-spline (NURBS) curves and surfaces through large sets of measured data is an important problem in applications such as reverse engineering and geometric modelling. This paper presents a method for realising significant improvements in the computational efficiency of this task. The basic idea is that the sparsity structures of the relevant matrices that are specific to the problem of NURBS fitting can be precisely defined and that full exploitation of these structures leads to significant savings in both computational and storage requirements. These savings allow for a large number of control points to be used in order to define the surface and consequently to improve the accuracy of shape representation. The achieved computational complexity is linear in both the number of measured points and the number of control points while the storage requirements of the algorithm are linear with the number of control points only. The complexity analysis, as well as the analysis of actual running times is presented. The results demonstrate that, using this approach, highly complex shapes may be modelled accurately with a single NURBS surface. [ABSTRACT FROM AUTHOR]

luminium coil production plants can be described as multi-stage processes which are often characterised by complexity and are expected to manipulate huge amount of data. Effective scheduling is a key issue for these processes since it can improve throughput rates and machine utilisation. This paper studies the production line of companies which produce high-quality rolled aluminium lithographic strips. An efficient representation for such production processes is provided and subsequently used for an extensive analysis and performance evaluation through appropriate metrics. In particular, the work addresses the implementation of an overall model in a simulation environment, capable of integrating the various aspects of the specific production management processes. The model was successfully validated using actual production data, and it was found that it is suitable for the modelling, analysis and performance evaluation of the complex aluminium coils production process. With the aid of the model, various scenarios were investigated via extensive simulation runs, such as installing additional machine centres and reducing pre-set times the products spend in intermediate storage areas. [ABSTRACT FROM AUTHOR]