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on Computational Economics |
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Issue of 2007‒07‒20
five papers chosen by |
| By: | Francesco Audrino; Fabio Trojani |
| Abstract: | We propose a multivariate nonparametric technique for generating reliable shortterm historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for non-linearities in the dependence of interest rates at all available maturities. Based on FGD we apply filtered historical simulation to compute reliable out-of-sample yield curve scenarios and confidence intervals. We back-test our methodology on daily USD bond data for forecasting horizons from 1 to 10 days. Based on several statistical performance measures we find significant evidence of a higher predictive power of our method when compared to scenarios generating techniques based on (i) factor analysis, (ii) a multivariate CCC-GARCH model, or (iii) an exponential smoothing covariances estimator as in the RiskMetricsTM approach. |
| Keywords: | Conditional mean and variance estimation, Filtered Historical Simulation, Functional Gradient Descent, Term structure; Multivariate CCC-GARCH models |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2007:2007-24&r=cmp |
| By: | Yugang, Y.; Koster, M.B.M. de (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | Compact, multi-deep (3D), Automated Storage and Retrieval Systems (AS/RS) are becoming more common, due to new technologies, lower investment costs, time efficiency and compact size. Decision-making research on these systems is still in its infancy. We study a particular compact system with rotating conveyors for the depth movement and a Storage/Retrieval (S/R) machine for the horizontal and vertical movement of unit loads. We determine the optimal storage zone boundaries for such systems with two product classes: high and low turnover, by minimizing the expected Storage/Retrieval (S/R) machine travel time. We propose a mixed-integer nonlinear programming model to determine the zone boundaries. A decomposition algorithm and a one dimensional search scheme are developed to solve the model. The algorithm is complex, but the results are appealing since most of them are in closed-form and easy to apply to optimally layout the 3D AS/RS rack. The results are compared with those under random storage, and show that a significant reduction of the machine travel time can be obtained. Finally, a practical example is studied to demonstrate the use and validate our findings. |
| Keywords: | Order picking;Storage rack design;AS/RS;Travel time model;Class-based storage; |
| Date: | 2007–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:300011309&r=cmp |
| By: | Eliashberg, J.; Hegie, Q.; Ho, J.; Huisman, D.; Miller, S.J.; Swami, S.; Weinberg, C.B.; Wierenga, B. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | This paper describes a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies within each day of the week, on which screen(s) different movies will be played, and at which time(s). The model consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time; and (ii) an optimization procedure that quickly finds an almost optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule we formulate the so-called movie scheduling problem as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We have applied this combined demand forecasting /schedule optimization procedure to a multiplex in Amsterdam where we supported the scheduling of fourteen movie weeks. The proposed model not 2 only makes movie scheduling easier and less time consuming, but also generates schedules that would attract more visitors than the current ?intuition-based? schedules. |
| Keywords: | Optimization of movie schedules;Integer programming;Column generation;Demand forecasting; |
| Date: | 2007–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:300011308&r=cmp |
| By: | Hachicha, Wafik; Masmoudi, Faouzi; Haddar, Mohamed |
| Abstract: | The important step in the design of a cellular manufacturing (CM) system is to identify the part families and machine groups and consequently to form manufacturing cells. The scope of this article is to formulate a multivariate approach based on a correlation analysis for solving cell formation problem. The proposed approach is carried out in three phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make simultaneously machine groups and part families while maximizing correlation between elements. In the third stage, an algorithm is improved to assign exceptional machines and exceptional parts using respectively angle measure and Euclidian distance. The proposed approach is also applied to the general Group Technology (GT) problem in which exceptional machines and part are considered. Furthermore, the proposed approach has the flexibility to consider the number of cells as a dependent or independent variable. Two numerical examples for the design of cell structures are provided in order to illustrate the three phases of proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical. |
| Keywords: | cellular manufacturing; cell formation; correlation matrix; Principal Component Analysis; exceptional machines and parts |
| JEL: | L60 |
| Date: | 2006–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3975&r=cmp |
| By: | McDonald, Stuart |
| Abstract: | A stochastic partial differential equation, or SPDE, describes the dynamics of a stochastic process defined on a space-time continuum. This paper provides a new method for solving SPDEs based on the method of lines (MOL). MOL is a technique that has largely been used for numerically solving deterministic partial differential equations (PDEs). MOL works by transforming the PDE into a system of ordinary differential equations (ODEs) by discretizing the spatial dimension of the PDE. The resulting system of ODEs is then solved by application of either a finite difference or a finite element method. This paper provides a proof that the MOL can be used to provide a finite difference approximation of the boundary value solutions for two broad classes of linear SPDEs, the linear elliptic and parabolic SPDEs. |
| Keywords: | Finite difference approximation; linear stochastic partial differential equations (SPDEs); the method of lines (MOL). |
| JEL: | C63 |
| Date: | 2006–10–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3983&r=cmp |