Facility Location with Spatially Interactive Travel Behavior

This paper sets out a spatially interactive facility location model that specifies client travel behavior according to a "gravity" formula. The well-known uncapacitated facility location model is a limiting case of this model. Analytical partial optimization yields a condensed formulation that can be solved by a nonlinear branch-and-bound approach. Computational results are presented for several problems having as many as 69 potential facility locations

A Dual-Based Procedure for Dynamic Facility Location

In dynamic facility location problems, one desires to specify the time-staged establishment of facilities at different locations so as to minimize the total discounted costs for meeting demands specified over time at various customer locations. We formulate a particular dynamic facility location problem as a combinatorial optimization problem. The formulation permits both the opening of new facilities and the closing of existing ones. A branch-and-bound procedure incorporating a dual ascent method is presented and shown, in computational tests, to be superior to previously developed methods. The procedure is comparable to the most efficient methods for solving static (single-period) location problems. Problems with as many as 25 potential facility locations, 50 customer locations, and 10 time periods have been solved within one second of CPU time on an IBM 3033 computer. Extensions of the dynamic facility location problem that allow price-sensitive demands, linearized concave costs, interdependent projects, and multiple commodities can also be solved by the dual ascent method. The method can serve as a component of a solution process for more difficult capacitated problems

On the Choice of Models for Public Facility Location

Public sector facility location models have been defined as those that minimize client costs for a given level of service subject to a public budget constraint, whereas private sector models are those that minimize the total costs for meeting fixed client demands. We show that a slight reformulation of a typical public sector location model is both superior to the original model and equivalent to a typical private sector formulation. Thus, for the class of problems considered, a standard model type is appropriate regardless of the institutional context

Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease

Plant Networks for Processing Recyclable Materials

10.1287/msom.2013.0437Manufacturing and Service Operations Management154670-688MSOM

Ford Whitman Harris's economical lot size model

Here we celebrate the centennial of Ford Whitman Harris's model for determining economical lot sizes, which was published in the A.W. Shaw Company's magazine Factory, The Magazine of Management in February 1913. The square-root formula derived by Harris has become one of the most cited and applied results in production and operations management. We examine the circumstances under which this result was derived, and explore the probable causes for the later obscurity of Harris's paper, which was forgotten for many years. © 2013 Elsevier B.V. All rights reserved