An inexact-stochastic dual water supply programming model

This paper introduces an inexact-stochastic dual water supply programming (ISDWSP) model based on analysis of the inexact characteristics in demand and supply subsystems of dual water supply system and their dynamic interactions. The model is based on an inexact chance-constrained programming (ICCP) method allowing both distribution information in B (right parameter in the model constrain) and uncertainties in A (left parameter in the model constrain) and C (parameter in the model function) with objective of maximizing economic return, and constrained to available water resource, economical, environmental and social constrains. The decision-making variables of ISDWSP model are water demanded amount by different sectors and waterworks building scale. In the solution process, the ISDWSP is transformed into two deterministic sub-models, which correspond to the upper and lower bounds of the objective function, and the reasonable interval solution set in the given decision space can be obtained by solving the two sub-models. Thus, decision alternatives can be obtained by adjusting decision variable values within their solution intervals and will be useful for decision makers to choose the projected applicable conditions considering tradeoffs between eco-environmental and economic objectives. The model is also applied in a new developing zone of North China with the results of the case study providing reasonable solutions for dynamic planning of different source water (DSW) allocation in a regional system. Finally, waterworks building plan is generated based on the projected applicable conditions.     

A hybrid simulation/optimisation scenario model for asset/liability management

In this paper we desciibe a decision support model to sustain management of pension-funds in the strategic planning of the available asset- and liability policy instruments. A main characteristic of the approach is that the relevant risk-drivers are modelled by scenarios, rather than by probability distributions. We will describe the scenario generation methodology, and how the scenarios are used by pension-fund managers to simulate and improve asset/liability strategies until a strategy is identified which is agreed upon by all who carry responsibility for the pension-fund and her sponsors and trustees. Next, we will describe how this process of managerial learning can be improved by a hybrid simulation/optimisation method which applies concepts from the field of non-linear global optimisation to determine the asset-allocations which determine efficient frontiers of contribution rates and downside insolvency risks. We will conclude by showing that the application of the developed models to a particular pension-fund leads to the annulment of the infeasibility of the current asset/liability policy on the one hand, and to a reduction of the expected yearly contributions of US $100 million on the other.     

A hybrid model for opinion formation

This paper presents a hybrid model for opinion formation in a large group of agents exposed to the persuasive action of a small number of strong opinion leaders. The model is defined by coupling a finite difference equation for the dynamics of leaders opinion with a continuous integro-differential equation for the dynamics of the others. Such a definition stems from the idea that the leaders are few and tend to retain original opinions, so that their dynamics occur on a longer time scale with respect to the one of the other agents. A general well-posedness result is established for the initial value problem linked to the model. The asymptotic behavior in time of the related solution is characterized for some general parameter settings, which mimic distinct social scenarios, where different emerging behaviors can be observed. Analytical results are illustrated and extended through numerical simulations.     

A hybrid stochastic model of retinal angiogenesis

Understanding the biological principles that govern blood vessel growth in the retina has important clinical implications, for the prevention of possible retinopathies, which may eventually lead to blindness. The availability of a realistic mathematical model of the relevant phenomenon may support the medical community in both issues, diagnosis being related to inverse problems, and therapy to optimal control strategies. The mathematical modeling of retinal angiogenesis leads to an highly complex problem, because of dimension, nonlinearity, and intrinsic randomness. In this paper, we propose a reduced model which leads to numerical simulations that somehow reproduce normal vascularization and predict possible pathologies. We call our model hybrid because it includes the coupling of a fully stochastic model for the construction of a vessel network in the retina, with continuum underlying fields describing relevant factors, such as growth factors and oxygen. We perform numerical simulations of a stochastic particle system coupled with partial differential equations (PDEs)' so to obtain images of vessel structure resembling real retina vasculatures. We then derive a possible mean field approximation of the stochastic vessel network, so to obtain a fully deterministic PDE system for the evolution of the underlying fields. Actually, in order to reproduce the geometric structure of the retina vessel network, we have to keep a stochastic model (though simplified) for its construction. Future investigations may concern the use of such a mean field approximation in the numerical simulations of the retina vasculature. Inverse and optimal control problems being the final goal of our research plan.     

A hybrid convex variational model for image restoration

We propose a new hybrid model for variational image restoration using an alternative diffusion switching non-quadratic function with a parameter. The parameter is chosen adaptively so as to minimize the smoothing near the edges and allow the diffusion to smooth away from the edges. This model belongs to a class of edge-preserving regularization methods proposed in the past, the ?-function formulation. This involves a minimizer to the associated energy functional. We study the existence and uniqueness of the energy functional of the model. Using real and synthetic images we show that the model is effective in image restoration.     

A hybrid model for selection of an outsourcing provider

Outsourcing is a good strategy for firms that need to reduce operating costs and improve competitiveness and it is important that firms scientifically select appropriate outsourcing providers. Some efforts have been made to find systematic ways to deal with outsourcing problems, but these efforts incorrectly assumed that the criteria used in the decision process are independent, which is not true in the real world. In this study, we propose a new hybrid multiple criteria decision-making (MCDM) model, which addresses the dependent relationships between the various criteria. The relations-structure among the criteria is built with the aid of the decision-making trial and evaluation laboratory (DEMATEL) method. Decision-makers tend to hold diverse opinions about their preferences due to incomplete information and knowledge, or inherent conflict between various departments. We further used the fuzzy preference programming and the analytic network process (ANP) to form a model for the selection of partners for outsourcing providers. The proposed model can help practitioners improve their decision making process, especially when criteria are numerous and inter-related. The method is demonstrated using data from a Taiwanese airline.     

A variational model with hybrid Hyper-Laplacian priors for Retinex

Retinex aims at estimating real reflectance images by removing the effect of illumination. We propose a nonconvex variational model for Retinex with novel priors for reflectance and illumination. Based on the statistics of the gradients of reflectance and illumination, we use the hyper-Laplacian prior to characterize the gradients of reflectance, and the hybrid hyper-Laplacian and Tikhonov prior to characterize the gradients of illumination. An efficient alternating direction method of multipliers (ADMM) is developed to solve the proposed model. Extensive numerical experiments show that the proposed method is comparable to the state-of-the-art methods quantitatively and qualitatively.     

A hybrid model of the Akamai adaptive streaming control system

Video streaming is the application generating the largest fraction of the Internet traffic. Adaptive video streaming adds to classic video streaming the possibility of dynamically adapting the video bitrate to track the time-varying network available bandwidth, avoid playback interruptions and ensure the delivery of the best video quality. This work focuses on the adaptive video streaming control system employed by Akamai, a major Content Delivery Network operator whose video delivery system is used by several video streaming platforms, including Livestream. Differently from the typical client-side control, Akamai employs an interesting and unique hybrid client/server control architecture. In this paper we propose and experimentally validate a closed loop mathematical model of the control system in the form of a hybrid automaton. The model is analyzed to derive key properties which can be used to properly tune the controller parameters.     

A stick-slip/Rouse hybrid model for viscoelasticity in polymers

A Rouse model for polymer chains is incorporated into the linear continuous stick-slip molecular-based tube reptation ideas of Doi–Edwards and Johnson–Stacer. This treats the physically constrained (PC) molecular stretches as internal strain variables for the overall PC/chemically cross-linked (CC) system. It yields an explicit system of stress–strain equations for the system permitting simple calculations of complex stress–strain relations. The model that is developed here treats PC molecule as entrapped within a constraining tube, which is comprised of both CC and PC molecules. The model is compared with experimental data sets from the literature.     

A hybrid genetic model for the prediction of corporate failure

This study examines the potential of a neural network (NN) model, whose inputs and structure are automatically selected by means of a genetic algorithm (GA), for the prediction of corporate failure using information drawn from financial statements. The results of this model are compared with those of a linear discriminant analysis (LDA) model. Data from a matched sample of 178 publicly quoted, failed and non-failed, US firms, drawn from the period 1991 to 2000 is used to train and test the models. The best evolved neural network correctly classified 86.7 (76.6)% of the firms in the training set, one (three) year(s) prior to failure, and 80.7 (66.0)% in the out-of-sample validation set. The LDA model correctly categorised 81.7 (75.0)% and 76.0 (64.7)% respectively. The results provide support for a hypothesis that corporate failure can be anticipated, and that a hybrid GA/NN model can outperform an LDA model in this domain.MSC codes: 62M45, 68W10, 90B50, 91C20     

A hybrid evolutionary learning algorithm for TSK-type fuzzy model design

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods.     

A hybrid macroscopic-based model for traffic flow in road networks

Recently, the unquestionable growth of interest to increase the operational efficiency and capability of transportation systems led to the development of a large number of traffic modeling theories. One of the major operational issues when developing a transportation system management model lies in the selection of the appropriate methodological approach with respect to several decisions, such as the selection of the type of input and output data as well as the qualitative representation and the computational power of the model. Despite the considerable effort in the area, there is still not an approach which per se models effectively the various dynamically evolving features of traffic in road networks. The present paper addresses this issue by introducing a new hybrid approach which combines the complementary features and capabilities of both continuum mathematical models e.g. 1, 6, 23 and 26 and knowledge-based models e.g. 7, 22 and 28 in order to describe effectively traffic flow in road networks.     

A hybrid numerical method for the shallow water equations with source term

In coastal oceanography there is interest in problems modeled by the shallow water equations, where variations in channel depth are accounted for by the presence of source terms. A numerical treatment for the solution of such problems is presented here, in terms of a hybrid approach, which combines a second-order TVD scheme for conservation law equations (assuming no source terms) with an eigenvector projection scheme that incorporates the effects of nonzero source terms (in regions where the bottom is not flat). For the case where an initially sharp wave profile is assumed, the progress of a wave as it traverses an estuary whose channel depth varies is calculated. Excellent numerical results are obtained. © 1996 John Wiley & Sons, Inc.     

A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management

This paper presents a hybrid neuro-fuzzy methodology to identify appropriate global logistics (GL) operational modes used for global supply chain management. The proposed methodological framework includes three main developmental phases: (1) establishment of a GL strategic hierarchy, (2) formulation of GL-mode identification rules, and (3) development of a GL-mode choice model. By integrating advanced multi-criteria decision-making (MCDM) techniques including fuzzy analytical hierarchy process (Fuzzy-AHP), Fuzzy-MCDM, and the technique for order preference by similarity to an ideal solution (TOPSIS), six types of global logistics and operational modes coupled with corresponding fuzzy-based multi-criteria decision-making rules are specified in the second phase. Using the specified fuzzy decision-making rules as the input database, an adaptive neuro-fuzzy inference system (ANFIS) is then developed in the third phase to identify proper GL modes for the implementation of global supply chain management. A numerical study with a questionnaire survey database aimed at the information technology (IT) industries of Taiwan is conducted to illustrate the applicability of the proposed method.     

A model for stochastic hybrid systems with application to communication networks

We propose a model for stochastic hybrid systems (SHSs) where transitions between discrete modes are triggered by stochastic events much like transitions between states of a continuous-time Markov chains. However, the rate at which transitions occur is allowed to depend both on the continuous and the discrete states of the SHS. Based on results available for piecewise-deterministic Markov process (PDPs), we provide a formula for the extended generator of the SHS, which can be used to compute expectations and the overall distribution of the state.As an application, we construct a stochastic model for on-off TCP flows that considers both the congestion-avoidance and slow-start modes and takes directly into account the distribution of the number of bytes transmitted. Using the tools derived for SHSs, we model the dynamics of the moments of the sending rate by an infinite system of ODEs, which can be truncated to obtain an approximate finite-dimensional model. This model shows that, for transfer-size distributions reported in the literature, the standard deviation of the sending rate is much larger than its average. Moreover, the later seems to vary little with the probability of packet drop. This has significant implications for the design of congestion control mechanisms.     

An interval-parameter fuzzy nonlinear optimization model for stream water quality management under uncertainty

Planning for water quality management systems is complicated by a variety of uncertainties and nonlinearities, where difficulties in formulating and solving the resulting inexact nonlinear optimization problems exist. With the purpose of tackling such difficulties, this paper presents the development of an interval-fuzzy nonlinear programming (IFNP) model for water quality management under uncertainty. Methods of interval and fuzzy programming were integrated within a general framework to address uncertainties in the left- and right-hand sides of the nonlinear constraints. Uncertainties in water quality, pollutant loading, and the system objective were reflected through the developed IFNP model. The method of piecewise linearization was developed for dealing with the nonlinearity of the objective function. A case study for water quality management planning in the Changsha section of the Xiangjiang River was then conducted for demonstrating applicability of the developed IFNP model. The results demonstrated that the accuracy of solutions through linearized method normally rises positively with the increase of linearization levels. It was also indicated that the proposed linearization method was effective in dealing with IFNP problems; uncertainties can be communicated into optimization process and generate reliable solutions for decision variables and objectives; the decision alternatives can be obtained by adjusting different combinations of the decision variables within their solution intervals. It also suggested that the linearized method should be used under detailed error analysis in tackling IFNP problems.     

A stochastic programming model for water resource planning

A framework is offered for the evaluation of electricity generation and water supply for agricultural irrigation. This assessment is conducted through the construction of an appropriate stochastic optimization model. A recursive least squares algorithm is incorporated in the model which enables more accurate estimation of model parameters.     

A multiobjective optimization model for water resources planning

A technique is developed for solving multiple objective optimization programs. The approach decomposes the system into groups of objectives according to their priority in the model. A lexicographic ordering (goal programming) approach is used to analyse this system of groups, while the solution structure of each individual group is developed using the method of constraints. The technique is applied to a planning model for river basins.