A note on a generalized network flow model for manufacturing process

Manufacturing network flow （MNF） is a generalized network model that overcomes the limitation of an ordinary network flow in modeling more complicated manufacturing scenarios, in particular the synthesis of different materials into one product and/or the distilling of one type of material into many different products. Though a network simplex method for solving a simplified version of MNF has been outlined in the literature, more research work is still needed to give a complete answer whether some classical duality and optimality results of the classical network flow problem can be extended in MNF. In this paper, we propose an algorithmic method for obtaining an initial basic feasible solution to start the existing network simplex algorithm, and present a network-based approach to checking the dual feasibility conditions. These results are an extension of those of the ordinary network flow problem.     

A generalized nonautonomous SIRVS model

A nonautonomous SIRVS model with time‐dependent parameters is considered. The global dynamics is investigated, and conditions for the permanence, extinction, and disease‐free equilibrium are studied. Substitute quantities that replace the basic reproduction number are presented. Numerical simulations illustrate the dynamic behavior of the proposed model. Copyright © 2012 John Wiley & Sons, Ltd.     

A generalized collapsing sandpile model

In this paper, we introduce a new model for the collapsing sandpile and we prove existence and uniqueness of a solution for the corresponding initial value problem. Moreover, we prove the convergence of the time-stepping approximation of the solution. We use subgradient flows for variational problems with time dependent gradient constraints. These gradient constraints are interpreted as the critical angles of the sandpile. In particular, our model produces an evolution in time of avalanches in a drying of a sandpile, rather than instantaneous collapse.     

A generalized linear exchange model

The linear exchange model is studied in which alongside the consumers there are firms minimizing expenses to guarantee some minimal level of the total cost of production. Some finite algorithm for finding an equilibrium is proposed and justified in the case of the fixed budgets of consumers. This algorithm develops the original approach of polyhedral complementarity which was proposed by the author for the classical exchange model.     

A generalized linear production model: A unifying model

We introduce a generalized linear production model whose attractive feature being that the resources held by any subset of producersS is not restricted to be the vector sum of the resources held by the members ofS. We provide sufficient conditions for the non-emptiness of the core of the associated generalized linear production game, and show that if the core of the game is not empty then a solution in it can be produced from a dual optimal solution to the associated linear programming problem. Our generalized linear production model is a proper generalization of the linear production model introduced by Owen, and it can be used to analyze cooperative games which cannot be studied in the ordinary linear production model framework. We use the generalized model to show that the cooperative game induced by a network optimization problem in which players are the nodes of the network has a non-empty core. We further employ our model to prove the non-emptiness of the core of two other classes of cooperative games, which were not previously studied in the literature, and we also use our generalized model to provide an alternative proof for the non-emptiness of the core of the class of minimum cost spanning tree games. Thus, it appears that the generalized linear production model is a unifying model which can be used to explain the non-emptiness of the core of cooperative games generated by various, seemingly different, optimization models.This research was partially done while the author was visiting the Graduate School of Business Administration at Tel-Aviv University. The research was partially supported by Natural Sciences and Engineering Research Council Canada Grant A4181 and by SSHRC leave fellowship 451-83-0030.Dedicated to George B. Dantzig.     

A discrete dynamical system as a model of a neural network with generalized Hebbian synapses

We study the dynamical behavior of a discrete time dynamical system which can serve as a model of a learning process. We determine fixed points of this system and basins of attraction of attracting points. This system was studied by Fernanda Botelho and James J. Jamison in [A learning rule with generalized Hebbian synapses, J. Math. Anal. Appl. 273 (2002) 529-547] but authors used its continuous counterpart to describe basins of attraction.     

A generalized stochastic goal programming model

In this paper we show how one can get stochastic solutions of Stochastic Multi-objective Problem (SMOP) using goal programming models. In literature it is well known that one can reduce a SMOP to deterministic equivalent problems and reduce the analysis of a stochastic problem to a collection of deterministic problems. The first sections of this paper will be devoted to the introduction of deterministic equivalent problems when the feasible set is a random set and we show how to solve them using goal programming technique. In the second part we try to go more in depth on notion of SMOP solution and we suppose that it has to be a random variable. We will present stochastic goal programming model for finding stochastic solutions of SMOP. Our approach requires more computational time than the one based on deterministic equivalent problems due to the fact that several optimization programs (which depend on the number of experiments to be run) needed to be solved. On the other hand, since in our approach we suppose that a SMOP solution is a random variable, according to the Central Limit Theorem the larger will be the sample size and the more precise will be the estimation of the statistical moments of a SMOP solution. The developed model will be illustrated through numerical examples.     

A multivariate generalized long memory model

In this Note, we propose a new flexible multivariate long memory process which is a self-similar model with the ability to capture short-range dependence, seasonality and long-range dependence characteristics. Specifically, we extend the multivariate ARFIMA model proposed by Sowell (1989) [8], and investigate some of its statistical properties.     

A generalized discrete-time order replacement model

In this article, we consider a discrete-time order replacementproblem. More precisely, we treat a generalized model with morecomplex cost structure than Kaio & Osaki (1979, IEEE Trans.Reliab., R-29, 405–406) and with two decision variables:allowable inventory time and ordering time. Based on the discreteprobabilistic argument, we derive the optimal ordering policyto deliver a spare unit preventively by a regular order, soas to minimize the expected cost per unit time in the steadystate. Numerical examples are devoted to carrying out the sensitivityanalysis of model parameters on the optimal ordering policyand its associated expected cost value.     

一种Tandem网络模型

本文考虑的是一个MAP输入的tandem队列，运用一个新颖的方法把它模拟成一个带爆炸块依赖阶数的拟生灭过程，这样使得我们可以很好地分析它的联合时间分布和平稳状态的停留时间分布布。     

A primal-dual approximation algorithm for generalized steiner network problems

We present the first polynomial-time approximation algorithm for finding a minimum-cost subgraph having at least a specified number of edges in each cut. This class of problems includes, among others, the generalized Steiner network problem, also called the survivable network design problem. Ifk is the maximum cut requirement of the problem, our solution comes within a factor of 2k of optimal. Our algorithm is primal-dual and shows the importance of this technique in designing approximation algorithms.Research supported by an NSF Graduate Fellowship, DARPA contracts N00014-91-J-1698 and N00014-92-J-1799, and AT&T Bell Laboratories.Research supported in part by Air Force contract F49620-92-J-0125 and DARPA contract N00014-92-J-1799.Part of this work was done while the author was visiting AT&T Bell Laboratories and Bellcore.     

A generalized polyconvex hyperelastic model for anisotropic solids

A generalized polyconvex hyperelastic model for anisotropic solids is presented. The strain energy function is formulated in terms of convex functions of generalized invariants and is given by a series with an arbitrary number of terms. The model addresses solids with orthotropic or transversely isotropic material symmetry as well as fiber-reinforced materials. Special cases of the strain energy function suitable for anisotropic elastomers and soft biological tissues are proposed. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A generalized linear programming model for nurse scheduling

This paper presents a 0–1 column generation model with a resource constrained shortest path auxiliary problem for nurse scheduling. The master problem finds a configuration of individual schedules to satisfy the demand coverage constraints while minimizing salary costs and maximizing both employee preferences and team balance. A feasible solution of the auxiliary problem is an acceptable schedule for a given nurse, with respect to collective agreement requirements such as seniority, workload, rotations and days off. We define a new resource structure in the auxiliary problem in order to take into account the complex collective agreement rules specific to the nurse scheduling problem. This model generalizes further the previous formulations discussed in the literature and can be viewed as a general scheme for complex personnel scheduling problems, especially in the context of organizations which operate around the clock. Solution methods and preliminary test results are discussed.     

A generalized DEA model for centralized resource allocation

In this paper, we introduced a new generalized centralized resource allocation model which extends Lozano and Villa’s and Asmild et al.’s models to a more general case. In order to uncover the sources of such total input contraction in the generalized centralized resource allocation model, we applied the structural efficiency to further decompose it into three components: the aggregate technical efficiency, the aggregate allocative efficiency and re-transferable efficiency components. The proposed models are not only flexible enough for the central decision-maker to adjust the inputs and outputs to achieve the total input contraction but also identify the sources of such total input contraction, thereby giving rise to an important interpretation and understanding of the generalized centralized resource allocation model. Finally, an empirical example is used to illustrate the approach.     

A computationally efficient model selection in the generalized linear mixed model

This paper is concerned with model selection in spline-based generalized linear mixed model. Exploiting the fact that smoothing parameters can be expressed as the reciprocal ratio of the variances of random effect under the setting of estimation by regularization, we propose a computationally efficient model selection procedure. Applications to some real data sets reveal that the proposed method selects reasonable models and is very fast to implement.     

New algorithms for generalized network flows

This paper, of which a preliminary version appeared in ISTCS'92, is concerned with generalized network flow problems. In a generalized network, each edgee = (u, v) has a positive flow multipliera _e associated with it. The interpretation is that if a flow ofx _e enters the edge at nodeu, then a flow ofa _e x _e exits the edge atv. The uncapacitated generalized transshipment problem (UGT) is defined on a generalized network where demands and supplies (real numbers) are associated with the vertices and costs (real numbers) are associated with the edges. The goal is to find a flow such that the excess or deficit at each vertex equals the desired value of the supply or demand, and the sum over the edges of the product of the cost and the flow is minimized. Adler and Cosares [Operations Research 39 (1991) 955–960] reduced the restricted uncapacitated generalized transshipment problem, where only demand nodes are present, to a system of linear inequalities with two variables per inequality. The algorithms presented by the authors in [SIAM Journal on Computing, to appear result in a faster algorithm for restricted UGT.Generalized circulation is defined on a generalized network with demands at the nodes and capacity constraints on the edges (i.e., upper bounds on the amount of flow). The goal is to find a flow such that the excesses at the nodes are proportional to the demands and maximized. We present a new algorithm that solves the capacitated generalized flow problem by iteratively solving instances of UGT. The algorithm can be used to find an optimal flow or an approximation thereof. When used to find a constant factor approximation, the algorithm is not only more efficient than previous algorithms but also strongly polynomial. It is believed to be the first strongly polynomial approximation algorithm for generalized circulation. The existence of such an approximation algorithm is interesting since it is not known whether the exact problem has a strongly polynomial algorithm.Corresponding author. Research was done while the first author was attending Stanford University and IBM Almaden Research Center. Research partially supported by ONR-N00014-91-C-0026 and by NSF PYI Grant CCR-8858097, matching funds from AT&T and DEC.Research partially supported by ONR-N00014-91-C-0026.     

Robustness analysis of generalized Jackson network

Computational Management Science - Queuing networks are a well-established approach to modeling and analysis of complex systems. This paper develops an approach to risk-analysis of queuing network...