in this study, a cross-docking system is designed at strategic and tactical levels. for making the strategic decisions, a multi-objective nonlinear location allocation model for cross-docks is presented based on a distri-bution location allocation model by andreas klose and andreas drexl. the model is further developed to in-clude the whole supply chain members and the objective functions are w...

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in...

Necessary and sufficient relations between measurement objectivity and psychometric models for data in more than two categories are reviewed and extended. Rasch (1960, 1961) and others prove the sufficiency of Rasch models for objectivity. Douglas and Wright (1986) derive the model necessary for objectivity for data in two categories. Rasch (1968) outlines a proof of the model necessary for obs...

This paper is concerned with transductive learning. We study a recent transductive learning approach based on clustering. In this approach one constructs a diversity of unsupervised models of the unlabeled data using clustering algorithms. These models are then exploited to construct a number of hypotheses using the labeled data and the learner selects an hypothesis that minimizes a transductiv...

The Federal Emergency Management Agency (FEMA) has identified the four phases of disaster related planning as mitigation, preparation, response, and recovery. The recovery phase is characterized by activity to return life to normal or improved levels. Very little research considers the recovery phase, which encompasses restoring services and rebuilding disaster stricken areas of the highway tra...

Central to several objective approaches to Bayesian model selection is the use of training samples (subsets of the data), so as to allow utilization of improper objective priors. The most common prescription for choosing training samples is to choose them to be as small as possible, subject to yielding proper posteriors; these are called minimal training samples. When data can vary widely in te...