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Demand Data (demand + data)

Distribution by Scientific Domains

Engineering	50%

Selected Abstracts

An operational algorithm for residential cogeneration systems based on the monitored daily-basis energy demand

ELECTRICAL ENGINEERING IN JAPAN, Issue 2 2010
Yuka Yamagishi
Abstract Residential cogeneration systems with PEFC are promising as distributed power system resources with the ability to improve energy system efficiency. However, it is important to develop an efficient algorithm for operation because the energy demand at each house differs greatly from day to day. In this paper, we propose an operational algorithm and evaluate it from the viewpoint of energy conservation and economic effectiveness based on the energy demand characteristics. In the algorithm, the hot water and electricity demand on the next day are estimated based on the average of past data. The results of simulations using actually monitored energy demand data indicate that (1) the greater the electrical demand of a household, the more effective this algorithm becomes with respect to energy conservation; (2) the greater the hot water demand of a household, the more effective this algorithm becomes with respect to economic effectiveness. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 170(2): 37,45, 2010; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/eej.20892 [source]

A rational rank four demand system

JOURNAL OF APPLIED ECONOMETRICS, Issue 2 2003
Arthur Lewbel
Past parametric tests of demand system rank employed polynomial Engel curve systems. However, by Gorman's (1981) theorem, the maximum possible rank of a utility-derived polynomial demand system is three. The present paper proposes a class of demand systems that are utility derived, are close to polynomial, and have rank four. These systems nest rational polynomial demands, and so can be used to test ranks up to four. These systems are suitable for applications where high rank is likely, such as demand systems involving a large number of goods. A test of rank using this new class of systems is applied to UK consumer demand data. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Exploiting self-canceling demand point aggregation error for some planar rectilinear median location problems

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2003
R.L. Francis
When solving location problems in practice it is quite common to aggregate demand points into centroids. Solving a location problem with aggregated demand data is computationally easier, but the aggregation process introduces error. We develop theory and algorithms for certain types of centroid aggregations for rectilinear 1-median problems. The objective is to construct an aggregation that minimizes the maximum aggregation error. We focus on row-column aggregations, and make use of aggregation results for 1-median problems on the line to do aggregation for 1-median problems in the plane. The aggregations developed for the 1-median problem are then used to construct approximate n -median problems. We test the theory computationally on n -median problems (n , 1) using both randomly generated, as well as real, data. Every error measure we consider can be well approximated by some power function in the number of aggregate demand points. Each such function exhibits decreasing returns to scale. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 614,637, 2003. [source]

The cost impact of using simple forecasting techniques in a supply chain

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 5 2003
Heung-Kyu Kim
In this paper we consider an inventory model in which the retailer does not know the exact distribution of demand and thus must use some observed demand data to forecast demand. We present an extension of the basic newsvendor model that allows us to quantify the value of the observed demand data and the impact of suboptimal forecasting on the expected costs at the retailer. We demonstrate the approach through an example in which the retailer employs a commonly used forecasting technique, exponential smoothing. The model is also used to quantify the value of information and information sharing for a decoupled supply chain in which both the retailer and the manufacturer must forecast demand. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 388,411, 2003 [source]

THE VALUE OF SKU RATIONALIZATION IN PRACTICE (THE POOLING EFFECT UNDER SUBOPTIMAL INVENTORY POLICIES AND NONNORMAL DEMAND)

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 1 2003
JOSÉ A. ALFARO
Several approaches to the widely recognized challenge of managing product variety rely on the pooling effect. Pooling can be accomplished through the reduction of the number of products or stock-keeping units (SKUs), through postponement of differentiation, or in other ways. These approaches are well known and becoming widely applied in practice. However, theoretical analyses of the pooling effect assume an optimal inventory policy before pooling and after pooling, and, in most cases, that demand is normally distributed. In this article, we address the effect of nonoptimal inventory policies and the effect of nonnormally distributed demand on the value of pooling. First, we show that there is always a range of current inventory levels within which pooling is better and beyond which optimizing inventory policy is better. We also find that the value of pooling may be negative when the inventory policy in use is suboptimal. Second, we use extensive Monte Carlo simulation to examine the value of pooling for nonnormal demand distributions. We find that the value of pooling varies relatively little across the distributions we used, but that it varies considerably with the concentration of uncertainty. We also find that the ranges within which pooling is preferred over optimizing inventory policy generally are quite wide but vary considerably across distributions. Together, this indicates that the value of pooling under an optimal inventory policy is robust across distributions, but that its sensitivity to suboptimal policies is not. Third, we use a set of real (and highly erratic) demand data to analyze the benefits of pooling under optimal and suboptimal policies and nonnormal demand with a high number of SKUs. With our specific but highly nonnormal demand data, we find that pooling is beneficial and robust to suboptimal policies. Altogether, this study provides deeper theoretical, numerical, and empirical understanding of the value of pooling. [source]