基于技术创新的资金约束供应链融资策略分析

新兴技术进入竞争市场时,供应链成员往往存在资金约束,为了尽早占据市场份额、获得更高的利润流,上下游企业有意愿进行供应链的内外部融资。文章对上游企业存在资金约束、下游核心企业资金充裕的供应链融资策略进行决策分析。研究表明:上下游的融资策略会使上游企业得到帕累托改进,且随上游企业自有资金的增大,下游企业的利率阈值逐渐减小;存在最优融资利率使得下游企业的期望利润达到最大。在上下游企业财务不透明的前提下,存在下游企业的融资利率区间使上游企业有隐藏真实资金状况以期获得更低融资利率的动机,文章基于博弈论的信息甄别模型给出了相应的激励合同对上游企业的期望利润进行修正,诱使上游企业提供真实的资金状况。     

Modifying the forrest-tomlin and saunders updates for linear programming problems with variable upper bounds

The author previously described a modification of the simplex method to handle variable upper bounds implicitly. This method can easily be used when the representation of the basis inverse (e.g. a triangular decomposition of the basis) is maintained as a dense matrix in core, but appears to cause difficulties for large problems where secondary storage and packed matrices may be employed. Here we show how the Forrest-Tomlin and Saunders updating schemes, which are designed for such large problems, can be adapted.Research supported in part by a fellowship from the Alfred P. Sloan Foundation and by NSF Grant ECS82-15361.     

Diagonal discrete gradient bundle method for derivative free nonsmooth optimization

Typically, practical nonsmooth optimization problems involve functions with hundreds of variables. Moreover, there are many practical problems where the computation of even one subgradient is either a difficult or an impossible task. In such cases, the usual subgradient-based optimization methods cannot be used. However, the derivative free methods are applicable since they do not use explicit computation of subgradients. In this paper, we propose an efficient diagonal discrete gradient bundle method for derivative-free, possibly nonconvex, nonsmooth minimization. The convergence of the proposed method is proved for semismooth functions, which are not necessarily differentiable or convex. The method is implemented using Fortran 95, and the numerical experiments confirm the usability and efficiency of the method especially in case of large-scale problems.     

Using imperfect advance demand information in forecasting

Email: t.tan{at}tue.nl Received on 4 January 2007. Accepted on 11 January 2008. In this paper, we consider the demand-forecasting problem ofa make-to-stock system operating in a business-to-business environmentwhere some customers provide information on their future orders,which are subject to changes in time, hence constituting imperfectadvance demand information (ADI). The demand is highly volatileand non-stationary not only because it is subject to seasonalityand changing trends but also because some individual clientdemands have significant influence on the total demand. In suchan environment, traditional forecasting methods may result inhighly inaccurate forecasts, since they are mostly developedfor the total demand based only on the demand history, not makinguse of demand information and ignoring the effects of individualorder patterns of the customers. We propose a forecasting methodologythat makes use of individual ordering pattern histories of theproduct–customer combinations and the current build upof orders. Moreover, we propose making use of limited judgementalupdates on the statistical forecasts prior to the use of ADI.     

A Modified Rank One Update Which Converges Q-Superlinearly

Quasi-Newton methods are generally held to be the most efficient minimization methods for small to medium sized problems. From these the symmetric rank one update of Broyden (Math. Comp., vol. 21, pp. 368–381, 1967) has been disregarded for a long time because of its potential failure. The work of Conn, Gould and Toint (Math. Prog., vol. 50, pp. 177–195, 1991), Kelley and Sachs (COAP, vol. 9, pp. 43–64, 1998) and Khalfan, Byrd and Schnabel (SIOPT, vol. 3, pp. 1–24, 1993; SIOPT, vol. 6, pp. 1025–1039, 1996) has renewed the interest in this method. However the question of boundedness of the generated matrix sequence has not been resolved by this work. In the present paper it is shown that a slightly modified version of this update generates bounded updates and converges superlinearly for uniformly convex functions. Numerical results support these theoretical considerations.     

Preconditioner updates for solving sequences of linear systems in matrix‐free environment

We present two new ways of preconditioning sequences of nonsymmetric linear systems in the special case where the implementation is matrix free. Both approaches are fully algebraic, they are based on the general updates of incomplete LU decompositions recently introduced in (SIAM J. Sci. Comput. 2007; 29 (5):1918–1941), and they may be directly embedded into nonlinear algebraic solvers. The first of the approaches uses a new model of partial matrix estimation to compute the updates. The second approach exploits separability of function components to apply the updated factorized preconditioner via function evaluations with the discretized operator. Experiments with matrix‐free implementations of test problems show that both new techniques offer useful, robust and black‐box solution strategies. In addition, they show that the new techniques are often more efficient in matrix‐free environment than either recomputing the preconditioner from scratch for every linear system of the sequence or than freezing the preconditioner throughout the whole sequence. Copyright © 2010 John Wiley & Sons, Ltd.     

Family of optimally conditioned quasi-Newton updates for unconstrained optimization

In this note, a general optimal conditioning problem for updates which satisfy the quasi-Newton equation is solved. The new solution is a family of updates which contains other known optimally conditioned updates but also includes new formulas of increased rank. A new factorization formula for the Broyden family and some preliminary numerical results are also given.     

Variational quasi-Newton methods for unconstrained optimization

In this paper, we propose new members of the Broyden family of quasi-Newton methods. We develop, on the basis of well-known least-change results for the BFGS and DFP updates, a measure for the Broyden family which seeks to take into account the change in both the Hessian approximation and its inverse. The proposal is then to choose the formula which gives the least value of this measure in terms of the two parameters available, and hence to produce an update which is optimal in the sense of the given measure. Several approaches to the problem of minimizing the measure are considered, from which new updates are obtained. In particular, one approach yields a new variational result for the Davidon optimally conditioned method and another yields a reasonable modification to this method. The paper is also concerned with the possibility of estimating, in a certain sense, the size of the eigenvalues of the Hessian approximation on the basis of two available scalars. This allows one to derive further modifications to the above-mentioned methods. Comparisons with the BFGS and Davidson methods are made on a set of standard test problems that show promising results for certain new methods.Part of this work was done during the author's visits at International Centre for Theoretical Physics, Trieste, Italy, at Systems Department, University of Calabria, Cosenza, Italy, and at Ajman University College of Science and Technology, Ajman, United Arab Emirates.The author expresses his gratitude to Professor L. Grandinetti for his encouragement and thanks the anonymous referees for their careful reading of an earlier draft of the paper and valuable comments, which led to a substantial improvement of the original paper.     

Modifying the forrest-Tomlin and saunders updates for linear programming problems with variable upper bounds

The author previously described a modification of the simplex method to handle variable upper bounds implicitly. This method can easily be used when the representation of the basis inverse (e.g. a triangular decomposition of the basis) is maintained as a dense matrix in core, but appears to cause difficulties for large problems where secondary storage and packed matrices may be employed. Here we show how the Fonest—Tomlin and Saunders updating schemes, which are designed for such large problems, can be adapted. Research supported in part by a fellowship from the Alfred P. Sloan Foundation and by NSF Grant ECS82-15361.     

A standard form variant,and safeguarded linesearch,for the modified Karmarkar algorithm

In his original analysis of the projective algorithm for linear programming, Karmarkar proposed a modified method which improved the worst-case arithmetic complexity of the original algorithm by a factor of . Karmarkar's analysis of the improvement is based on a primal-dual formulation, and requires that small steps be taken on each iteration. However, in practice the original algorithm can be easily applied to standard form problems, and is considerably improved by performing a linesearch on each iteration, generally leading to much larger steps. We show here that by incorporating a simple safeguard, a linesearch may be performed in the modified algorithm while retaining the complexity improvement over the original algorithm. We then show that the modified algorithm, with safeguarded linesearch, can be applied directly to a standard form linear program with unknown optimal objective value. The resulting algorithm enjoys a complexity advantage over standard form variants of the original algorithm.