Search

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

In this paper, we study the sum rate maximization for successive zero-forcing dirty-paper coding (SZFDPC) with per-antenna power constraint (PAPC). Although SZFDPC is a low-complexity alternative to the optimal dirty paper coding (DPC), efficient algorithms to compute its sum rate are still open problems especially under practical PAPC. The existing solution to the considered problem is computationally inefficient due to employing high-complexity interior-point method. In this study, we propose two new low-complexity approaches to this important problem. More specifically, the first algorithm achieves the optimal solution by transforming the original problem in the broadcast channel into an equivalent problem in the multiple access channel, then the resulting problem is solved by alternating optimization together with successive convex approximation. We also derive a suboptimal solution based on machine learning to which simple linear regressions are applicable. The approaches are analyzed and validated extensively to demonstrate their superiors over the existing approach., Comment: 5 pages, 4 figures

The problem of joint information transmission and input signalling estimation over a state-dependent channel is considered. The general single-letter upper and lower bounds of the optimal capacity-distortion trade-offs are provided. For the Gaussian dirty paper channel, we calculate the bounds by choosing parameters carefully, and obtain a computable form eventually. In such a way, we obtain a complete characterization for this problem setting.

There has been an increasing interest in mobile edge computing (MEC) in recent years. Different from the traditional centralized cloud computing, MEC servers are deployed at the edges of networks such as at base stations (BSs) and access points (APs), in order to support computation-intensive and latency-critical applications. In this paper, we consider a multi-user mobile edge computing (MEC) network in which the wireless data transmission/offloading is performed using finite blocklength (FBL) codes to satisfy the latency constraints. The reliability in the communication phase is characterized in the FBL regime, while the event of queue length violation in the computation phase is investigated via exploiting the extreme value theory. We first formulate the overall optimization problem in the scenario of multiple user equipments (UEs) aiming to minimize the maximal end-to-end error probability among all UEs under both the FBL and energy consumption constraints. We further propose a two-level learning-based approach to jointly determine time allocations in the FBL regime, the UEs' offloading decisions and MEC computational resource allocations to solve the overall optimization problem. Simulation results demonstrate that the proposed two-level learning-based algorithm solves the problem efficiently.

We explore the power of parallelism in a stochastic search for global optimum in high-dimensional optimization problems. This search, that often navigates a large solution space through a Markov-Chain Monte Carlo (MCMC) process, needs to make stochastic decisions at every step and needs to escape from local optima. Through parallelism, we are able to survey an entire neighbourhood (all states with Hamming distance of 1) to make efficient moves, use multiple replicas at different temperatures, such as in parallel tempering, or deploy a population of replicas at the same temperature. Once combined, these methods of parallelism can yield 100x to 10,000x speedups.

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

The study aimed to realize the automatic production scheduling of waste paper in paper mills. Based on the quantities of the field data (mixing ratios of waste paper and pulp properties) stored in a paper mill, the methods of back propagation neural networks (BP-NN) and support vector machine (SVM), the all data set and the average data set, were firstly applied to develop the prediction models of the pulp properties; and then the genetic algorithm was used to search for the suitable scheduling solutions. The simulation results revealed that: demonstrating its acceptable prediction accuracy and fast training time, the prediction model with SVM method and the average data set was good enough to be used in the genetic algorithm to search for the optimal scheduling ratios of waste paper, the obtained optimal scheduling solution not only had the lowest purchase cost of waste paper, but also met the requirements of the mill: specified types of waste paper, specified ratio of some kind of waste paper and the required pulp brightness.

This work considers sustainable scheduling of manufacturing operations and preventive maintenance activities in a single-machine environment where the machine works continuously in three eight-hour shifts per day. The jobs can be produced at different processing speeds, which reduces energy consumption and/or processing times. In a tri-objective mixed integer linear programming model, sustainability is attained through minimizing total weighted earliness/ tardiness - economic pillar, total energy consumption - environmental pillar, and number of undesired activities - social pillar. Moreover, a multi-objective genetic algorithm finds near optimal solutions in a timely manner. Numerical results will be presented at the conference.

Vehicle-to-vehicle (V2V) communication plays an important role in next generation mobile communications, where security is a critical issue. Most previous works relied on encryption/authentication algorithms to ensure V2V communication security. In this paper, we explore physical-layer security in V2V communications. We formulate a secrecy capacity optimization problem, which is solved via decomposing a joint optimization problem into two subproblems, e.g., optimal subcarriers and power allocation problems. Because of the co-channel interferences from different users, the subcarriers allocation subproblem is a three-dimensional (3D) search problem. We develop an iterative algorithm, based on which we transform a non-convex power allocation problem to a convex form before solving it using an alternating maximization (AM) algorithm. Simulations are used to validate the performance of the proposed physical-layer security scheme.

The problem of regulators (controllers) design is extremely relevant in connection with the penetration into all technological areas of the methods of precise control of objects with feedback. Such devices are being more and more used, which increases the variety of controlled objects. This list includes all new objects of robotics and technological setups, which have much more complex mathematical models. One of the most urgent problems in this area is control of non-stationary objects. Parameters of mathematical model of these objects are given with some uncertainty, but not in fixed values, i.e. by only intervals of their possible changes. If the intervals are too wide, adaptive systems have to be designed, but if they are not too wide, then one can try to find a universal regulator setting that will be sufficiently effective for all ratios of the model parameters within the acceptable intervals of their changes. The paper proposes a new method of the numerical optimization of regulator for finding such a setting for the worst object model that is for the worst combination of all variable parameters. Finding the worst model is also carried out by a numerical optimization method, in which a combination of model parameters is found where the best regulator gives the worst result. The problem is proposed to solve in several optimization cycles with alternation of objective functions. The technique has been well illustrated by a numerical example.

Several studies have found methods of optimizing order allocation and trim loss in paper industry. However, the resulting solution may not be adoptable due to some widths of stocked rolls exceed or less than those required for acceptable scheduling. To meet actual customer demand and better suitability of results, a new decision model has been presented which can be used for simultaneous cutting of master reels and stocked rolls with limitation on cutting blades at winder. The objective is to fulfill customer need using minimum possible number of master reels and stocked rolls. The problem is solved by simplex method which generally leads to non-integer optimal solution. Phase-II is solved by a new method to make the final solution integer using binary programming. Mathematical and decision models are given and a numerical example has been solved to illustrate the applicability of proposed method.

The F composite fading model was recently proposed as an accurate and tractable statistical model for the characterization of the simultaneous occurrence of multipath fading and shadowing conditions encountered in realistic wireless communication scenarios. In the present contribution we capitalize on the distinct properties of this composite model to derive the achievable capacity over F composite fading channels assuming fixed rate quality of service requirements. To this end, novel exact and tractable analytic expressions are derived for both the exact and the truncated channel inversion strategies. This also enables the derivation of additional simplified approximate and asymptotic expressions for these cases, which provide meaningful insights on the effect of fading conditions on the overall system performance. This is particularly useful in the context of numerous emerging wireless applications of interest that exhibit stringent fixed rate requirements such as vehicular communications, body area networks and telemedicine, among others. acceptedVersion

Concerning intelligent paper composition, the article puts forward an improved genetic algorithm (GA) based on the multi-population division. According to the ideas of population division, the genes of individuals in the population which control the same characters are further divided into the same sub population so that they can be performed by the genetic operators. In order to avoid the local optimum, the optimal solution of each sub population is selected to be recombined global optimum. Compared with the simple genetic algorithm (SGA), this algorithm significantly reduces the search space of the problem solution and accelerates convergence speed. Finally, the algorithm is verified in the problem of intelligent paper composition and the results indicate that the algorithm has obviously improved the quality of the intelligent paper composition compared with the SGA.

We consider the beamformer design for zero-forcing dirty paper coding (ZF-DPC), a suboptimal transmission technique for MISO broadcast channels (MISO BCs). For the sum rate maximization problem under a total power constraint, the existing beamformer designs in the literature are based on the QR decomposition (QRD), which is used to satisfy the ZF constraints. However, the optimality of the QRD-based design has been unknown previously. In this paper, we prove that the QRD-based design is optimal for ZF-DPC for any performance measure under a sum power constraint. For the per-antenna power constraints, the QRD-based designs become suboptimal, and we propose an optimal design, using a convex optimization framework. Low-complexity suboptimal designs are also presented, and numerically shown to achieve a significant fraction of the sum rate provided by the optimal design.

Dirty paper coding scheme (DPC) is known to be a capacity achieving transmission technique in downlink multiuser MIMO channels. As a suboptimal solution to DPC, a precoding technique called successive zero-forcing dirty paper coding (SZF-DPC) has been proposed recently. The condition for the existence of the precoding matrices in SZF-DPC restricts the capability of supporting multiple users simultaneously. Thus, a user scheduling algorithm is needed to achieve multiuser diversity in SZF-DPC as the number of users grows. In this paper, we propose three low-complexity suboptimal scheduling algorithms to exploit the multi-user diversity gain for SZF-DPC. The first algorithm greedily maximizes the true sum capacity. The second algorithm is based on eigenvalues that are closely related to the true capacity. The third algorithm relies on the diagonal elements of the effective channel matrix since we realize a strong relationship between the eigenvalues and diagonal entries of a Hermitian matrix. Simulation results of several cases show that the proposed scheduling algorithms can obtain a significant fraction of sum capacity of the optimal solution.

A Dirty Paper Coding (DPC) based transmission scheme for the Gaussian multiple-input multiple-output (MIMO) cognitive radio channel (CRC) is studied when there is imperfect and perfect channel knowledge at the transmitters (CSIT) and the receivers, respectively. In particular, the problem of optimizing the sum-rate of the MIMO CRC over the transmit covariance matrices is dealt with. Such an optimization, under the DPC-based transmission strategy, needs to be performed jointly with an optimization over the inflation factor. To this end, first the problem of determination of inflation factor over the MIMO channel $Y=H_1 X + H_2 S + Z$ with imperfect CSIT is investigated. For this problem, two iterative algorithms, which generalize the corresponding algorithms proposed for the channel $Y=H(X+S)+Z$, are developed. Later, the necessary conditions for maximizing the sum-rate of the MIMO CRC over the transmit covariances for a given choice of inflation factor are derived. Using these necessary conditions and the algorithms for the determination of the inflation factor, an iterative, numerical algorithm for the joint optimization is proposed. Some interesting observations are made from the numerical results obtained from the algorithm. Furthermore, the high-SNR sum-rate scaling factor achievable over the CRC with imperfect CSIT is obtained., To be presented at ISIT 2009, Seoul, S. Korea

In this paper, we study the resource allocation for an orthogonal frequency division multiple access (OFDMA) radio system employing a full-duplex base station for serving multiple half-duplex downlink and uplink users simultaneously. The resource allocation design objective is the maximization of the weighted system throughput while limiting the information leakage to guarantee secure simultaneous downlink and uplink transmission in the presence of potential eavesdroppers. The algorithm design leads to a mixed combinatorial non-convex optimization problem and obtaining the globally optimal solution entails a prohibitively high computational complexity. Therefore, an efficient successive convex approximation based suboptimal iterative algorithm is proposed. Our simulation results confirm that the proposed suboptimal algorithm achieves a significant performance gain compared to two baseline schemes.

A decentralized control scheme is proposed to address a spatially distributed paper machine control problem under industrial investigation. The methodology is based on constrained optimal control and tackles the complexity of the problem by solving local problems of small size, which are coupled through their performance objectives and constraints. Properties of the decentralized scheme are analyzed in detail and compared to the existing centralized receding horizon controller currently used in industry. In terms of the simulated example the decentralized controller can achieve similar performance to the centralized controller but at a fraction of the computational cost.

This paper mainly studies an intelligent test paper generation method based on genetic particle swarm optimization. Firstly, this paper introduces the index system and the constraint condition of the intelligent test paper generation. Moreover, this paper proposes the intelligent test paper generation method that combines an improved adaptive genetic algorithm with particle swarm optimization. In the algorithm, the initial population is generated using the particle swarm optimization by designing proper chromosome coding and then determining the fitness function. Next, new population are obtained by processing the initial population using the genetic algorithm. As a result, operation results are output. At last, this paper analyzes the factors that affect the result of intelligent test paper generation through experimental verification. The research shows that the genetic particle swarm optimization's operation speed is very fast and its operation results are much accurate.

The paper proposes a model of multi-objective forestry-paper supply chain network with risks under fuzzy environment. Minimizing the risks and the total cost of forestry supply chain network is the objective of this study. The lower semi-variance is used to measure the risks. The total cost is composed of variable cost, fixed cost, and transportation cost. Different from previous literature, an approximation method is put forward to convert a continuous fuzzy issue into discrete fuzzy issue and method of auxiliary variable is introduced to transform multi-objective problem into single-objective problem. Cplex 12.6 is used to deal with this problem. The results are as follows. First, this model can be well solved by Cplex 12.6. Second, the structure of forestry-paper supply chain network keeps unchanged with the increase of discretization points. Third, the lower semi-variance and expected value of total cost converge with the increase of discretization points.

Cognitive radar refers to an adaptive sensing system exhibiting high degree of waveform adaptivity and diversity enabled by intelligent processing and exploitation of information from the environment. The next generation of radar systems are characterized by their application to scenarios exhibiting non-stationary scenes as well as interference caused by use of shared spectrum. Cognitive radar systems, by their inherent adaptivity, seem to be the natural choice for such applications. However, adaptivity opens up reliability issues due to uncertainties induced in the information gathering and processing. This paper lists some of the reliability aspects foreseen for cognitive radar systems and motivates the need for waveform designs satisfying different metrics simultaneously towards enhancing the reliability. An iterative framework based on multi-objective optimization is proposed to provide Pareto-optimal waveform designs.

The final quality of paper depends on many quality and process variables. It is very difficult to find theoretical rules of the behavior of paper properties when variables depend from each other and when the interdependencies are not linear. In this paper we present a neural network based system for estimating the final quality of paper from process measurements. Inverse computation of the network model is used to find a control action that will produce the desired quality. A separate self-organizing map is used to monitor the movement of the operating point of the process and to give a hint of the estimation error of the network. >

Considering the problem on generating test papers is multi-objective parameter optimization under multiple constraints. I proposed a new improved genetic algorithm based on the researches of the mathematical model of generating test paper after encoding segmented chromosome, confirming adaptability function, segmented group initialization, altering adaptive crossover probability and mutation probability and conserve optimization individuals. This method implemented generating test paper well, and the experimental results show that this improved genetic algorithm is more practical and effective compared to the common algorithm in the same conditions.

In this paper, we consider some practical scheduling problems under unavailability constraints (break-down periods, maintenance durations and/or setup times). Such problems can be met in different industrial environments and be associated to numerous real-life applications. The studied criteria are related to the maximum lateness minimization. The paper is organized in two main parts. The first part focuses on some special cases (makespan minimization when tails are equal and maximum lateness minimization when release dates are equal (see Section 2). The second part is devoted to the maximum lateness minimization (see Section 3). In each part, we present the main contributions and explain their principles (complexity results, heuristic algorithms and their worst-case performance, existing approximation schemes, exact methods, dynamic programming,...), Finally, Section 4 concludes the paper.

Due to the increasing cost of energy and the demand of reducing the environmental footprints, energy saving is becoming an important subject in the industry operation. To realize the energy consumption optimization of papermaking, the energy model should be established while the product quality and process model also need to be constructed, which are taken as the constraints for optimization. This paper describes the identification of a forming section of paper machines with Multilayer Perception (MLP) Neural Networks. The process model, product quality model and energy consumption model are established for the energy saving in papermaking. The real industrial step tests are performed and the data are used to model training and validation. The models are validated by means of mean-squared error (MSE), fit measure and Akaike's Final Prediction Error (FPE). The results show the effectiveness of the established models, which are suitable for the next work of energy optimization.

Robust control of dynamic linear systems with parametric uncertainties in the LQG context often leads to the so-called guaranteed cost control approaches. In such cases it is based on the solution of generalized Riccati equations. This paper presents an overview of robust control problems related to the guaranteed cost control approach and the corresponding solutions. It is shown that different uncertainty descriptions and design specifications lead to various forms of generalized Riccati equations that may admit either analytical or computational positive definite solutions. The associated closed-loop systems' robustness features are also discussed.

In order to avoid slow-convergence and local convergence of simple genetic algorithm (SGA ) for intelligent test paper generation, a kind of improved genetic algorithm (IGA)has been proposed in this paper. This algorithm uses unceasing elimination of similar individual method to quickly enlarge the search space and to stabilize the individual diversity of the group. Experiment results show that the test paper formed by the algorithm meets all the user requirements if the quantity of test questions is moderate and reasonable.

According to the research and analysis of auto-generating exam paper issues, identified auto-generating exam paper is a multi-objective constrained optimization problem, this paper puts forward mathematical models for auto-generating paper based on genetic algorithm. In this model, uses the properties of global-selecting-excellent and intelligent search of genetic arithmetic, on the basis of the every properties corresponding with the mathematical model's control index, so we can obtain a satisfying exam paper according with the teacher's demands from item bank, it effectively solves the problem of auto-generating exam paper in item bank. Theoretical analysis shows that the genetic algorithm has polynomial time and space complexity which used. Genetic algorithm can solve effectively the issue of intelligent generating exam paper in item bank is verified through the experiment, compared with other similar methods, it can find the groups in meeting the conditions earlier, thus easier to find the optimal solutions, and has a widely application and promotion worth in auto-generating paper.

Basing on the characteristics of Skill operation paper, a constraint model of generating a paper around skill points is proposed, and the strategy of combining distribution-satisfying skill points is applied to automatically generate a skill operation paper. Besides, a matrix constraint model for skill points-based paper generation is designed and the three key points needed in the strategy are mainly discussed and explored: parameter validation and optimization, skill point combination program generation and skill operation paper generation. In addition, an instance verifies that the method above is capable of fully satisfying the skill point-based matrix constraint conditions in generating a skill operation paper, and with easy operation, high efficiency and good quality, it is very suitable for generating a skill operation paper.

Non-fragile robust control problem of a paper basis weight is studied. It is pointed out that this problem is really a non-fragile robust performance problem, the problem with both non-fragile robust stability and performance requirements(bandwidth and disturbance attenuation). It is also suggested that the design of the non-fragile robust performance can be formulated as a μ-synthesis problem, if the uncertainty weighting function and the performance weighting function to system bandwidth and disturbance attenuation are revised appropriately. The μ-synthesis non-fragile robust PI controllers of the paper basis weight control system are solved. The results show that the proposed method is efficient and feasible.

The basic objective of generating test paper is to generate a high quality paper according to consumer's requirements. The existing algorithms are likely failed because of partially fulfilling constraint conditions of generating test paper. An improved Particle Swarm Optimization is proposed for the problem. Natural number coding and subsection strategy are adopted in the new algorithm. The results of experiment show that the improved Particle Swarm Optimization is helpful to improve efficiency of generating test paper.

A high quality test paper auto-generating is generally at the cost of considerable time. In order to handle the contradiction between quality and speed of combination, an improved subsection-single-point crossover and mutation GA based on real segment coding and condition matrix is proposed in the thesis. In the design of genetic operators roulette wheel selection, subsection-single-point crossover and mutation policy are synthetically used. And the population can be evolved continuously by the optimal-store policy. The algorithm has been proved feasible and effective through test and comparison between other algorithms. At last the validating system enhances the users' condition constraints for test paper through manual inching, thus the system is more simple and practical.

To deal with non-paper test composition algorithm impact on exam quality, we proposed the test-sheet composition algorithms. By comparing a variety of existing intelligent algorithms in the application of test-sheet composition, we identify the shortcomings of existing algorithms, such as the "premature" of algorithm due to the poor local search ability and the low convergence rate, etc. PSO algorithm has no crossover, mutation operators. It directly provides the speed, position update formula, and completes the assessment with the help of the fitness function of iterations. The principles and mechanisms of algorithm are simpler. On the basis of standard PSO algorithm, we proposed a Binary Particle Swarm Optimize (BPSO) algorithm based on probability. Bayes formula was used to overcome the human factors impacting on algorithm convergence speed. The algorithm validity has been shown in the simulation experiment with Java.

Existing research in soft real-time scheduling has focused on determining tardiness bounds given a scheduling algorithm. In this paper, we study lateness bounds, which are related to tardiness bounds, and propose a scheduling algorithm to minimize lateness bounds, namely the global fair lateness (G-FL) algorithm. G-FL is a G-EDF-like scheduler, but has lower maximum lateness bounds than GEDF. Due to its G-EDF-like nature, it can be used within existing systems that implement arbitrary-deadline G-EDF, and with existing synchronization protocols. Therefore, we argue that G-FL should replace G-EDF for SRT applications.

Agent-oriented design is one of the most active areas in the field of deployment of web-based distance education, and test is a popular measurement tool of learnerspsila knowledge in order to verify the learnerpsilas level of understanding and select corresponding educational strategy. In this paper, an innovative approach to seamless integration of the particle swarm optimization (PSO) and multi-agent system (MAS) is proposed. In order to generate a test paper automatically, a modified genetic particle swarm optimization (GPSO) is presented, in which the values of parameters will be decreased linearly with the number of iterations for improving the late convergence rate. For the implementation of GPSO based on multi-agent system, a core agents TPAgent (TPA) is provided to undertake the operations of GPSO and will control the evolution operations of each generation of population. To keep communication between different nodes at a minimum cost, fitness evaluation tasks are implemented by the TPAgents at local nodes, only the local minimum fitness and the corresponding best particle are sent to center node so as to get the global best particle in the parallel computing environment. For avoiding the prematurity, the global best particle will be dispatched to remote node randomly. Based on the JADE, a prototype system is setup , and the simulation results show that the proposed approach is feasible and robust.

Through analyzing the mathematical model and objective function of the composing test paper, this article abstracts that the composing test paper model is really a multi-objective linear programming model, and the binary ant colony algorithm is introduced to solve the problem. Owning to the adoption of the binary coding, each ant chooses the subject or not only need to according to the strength of the pheromone on every edge, and the requirement for the behavior of every single ant is lower, so the corresponding memory is relatively less. Experimental results demonstrate that the algorithm can solve the test paper problem quickly and effectively, and also has practical value.

This invited panel paper discussion will outline a number of aspects of wind energy characteristics relevant to the optimal wind/transmission model formulation task. Optimal placement of wind capacity on a constrained transmission network is a typical example of this type of problem. In particular the relevance of advanced and computationally intensive stochastic unit commitment to the model formulation will be debated. Optimization constraint matrix structure and techniques to exploit it will be shown to be of considerable importance for this type of problem. The relative merits of different model dimensionality reduction schemes, either through multivariate component analysis and probability discretisation or indeed scenario reduction, will be discussed. A pragmatic acceptance of the imprecise impact of long-term power system uncertainties will be maintained throughout, and wherever possible generality to different types of power systems will be considered.

In view of the low efficiency problem of extensively used genetic algorithm in large amount test code algorithm, this paper uses quantum particle swarm algorithm method to solve integer programming. Setting proper parameters and limiting the type and scope of particle position formula, this algorithm can solve large amount test code autogenerating problems. Experiments applying this algorithm have testified that it has stronger optimal ability and speed and better feasibility.

The Xuan-paper modeling, the most basic problem in Chinese painting simulating, is explored in this paper. An effective thickness Xuan-paper model is presented. The important influence of paper thickness to water holding and water transmitting is considered; also the complexity of mathematic solving caused by 3D paper model is avoided. The experimental results demonstrate that the model is simple and effective, and the model can simulate the different quality Xuan-papers by controlling the corresponding parameters. The effective Xuan-paper model establishes the foundation for simulating of water and ink moving in future.

This paper contributes a novel Particle Swarm Optimization (PSO) method. The particle is updated not only by the best position in history (p best ) and the best position among all the particles in the swarm (g best ), but also using the position that is nearest neighbor of p best . Additionally, we introduce a modified PSO algorithm based on the fuzzy clustering of particles to communication with the nearest neighbor for reducing the premature convergence and in sequel enhance the capability of global exploration. We validate our methods by an extensive experimental study on four benchmark test functions and compare the result with basic PSO.

The aim of this paper is to present a multiple objective programming model for crop planning under uncertainty in the presence of production quotas. The model is a 0-1 programming model that uses loss functions. Starting from it, one formulates several single objective models: the average loss minimization model; the expected return maximization model; the financial risk minimization model; the loss-return-risk tradeoff model. The models take into account climate risks, market risks and several classes of land quality. They are based on loss function theory and portfolio theory. Simulations in the above mentioned models are made starting from historical data or from scenarios generated by various probability distribution functions. We analyze losses associated to the optimal production plans that try to comply to the target production quotas versus various parameters of the average loss minimization model.

This article presents the use of the simulated annealing algorithm to solve the waste minimization problem in roll cutting programming, in this case, paper. Client orders, which vary in weight, width, and external and internal diameter, are fully satisfied; and no cuts to inventory are additionally generated, unless, they are specified. Once an optimal cutting program is obtained, the algorithm is applied again to minimize cutting blade movements. Several tests were performed with real data from a paper company in which an average of 30% waste reduction and 100% in production to inventory are obtained compare to the previous procedure. Actual savings represent about $5,200,000 USD in four months with 4 cutting machines.

A fuzzy system is proposed to compute new target values for low level controllers during grade changes in a paper machine. The fuzzy system is designed and tuned in co-operation with human operators of the paper machine. The design method utilizes both heuristic knowledge and estimation techniques based on input-output data. The input fuzzy sets and the rule base are initially designed verbally and thereafter fine-tuned by a least squares algorithm. An industrial example illustrates the iterative design process. It shows that transparency and a priori knowledge included in the model are preserved also during optimization of the fuzzy model.

The problem of estimating state variables and parameters is considered for discrete-time systems in the presence of random disturbances and measurement noise. The solution of the linear problem is given and an approximation technique is developed for nonlinear systems. A dynamic programming formulation of the estimation problem is also developed.

We evaluate the performance of a number of action-selection methods on the multi-armed bandit problem with covariates. We resort to simulations because our primary concern is the speed with which the different methods identify the optimal policy, and not their asymptotic behaviour. The experimental results show that the performance of the e-greedy methods is robust, while the interval estimation strategies achieve the fastest learning of the optimal policy. We propose a metric to quantify the difficulty of a multi-armed bandit problem with covariates and show that there is a trade-off between the satisfaction of the different performance measures.

Object-oriented (OO) state estimation modeling has been recently proposed, for both radial and weakly meshed distribution systems. The OO algorithm is based on the Newton's iterative method, widely adopted to find a solution to the state estimation problem. For radial topologies, the OO algorithm derives from the Newton's method. Weakly meshed topologies are treated with radial equivalents; the OO approach introduces some approximations. The convergence performance of the OO algorithm is investigated on a 69-branch system.

We study performance tradeoffs for joint diversity enhancement and interference mitigation in multi-cell networks with multiple antennas at the transmitter. Our transmitter combines a space time coding block followed by a multimode beamformer. We assume that the transmitter knows the channel covariance for its own and the interfered user. The coding techniques maximize link performance via diversity gain for its own user while constraining the interference power to the interfered (or other) user. We examine strategies for choosing this interference constraint based on greedy or co-operative methods for a symmetric two cell scenario where each link interferes with the other. In the greedy algorithm, each user selfishly maximizes diversity to its own user without regard to the interference it generates to the other user. Both users account for the incoming interference and they eventually iteratively reach the Nash equilibrium (NE). In the co-operative algorithm, each user balances the diversity to its own user but co-operates by limiting the amount of interference to the other user. We show that in the co-operative case, it is possible to find Pareto dominant solutions where the users either maintain or improve their symbol error rate performance