Supervised classification is an important part of corporate data mining to support decision making in customer-centric planning tasks. The paper proposes a hierarchical reference model for support vector machine based classification within this discipline. The approach balances the conflicting...

The redundancy allocation problem is the problem of finding an optimal allocation of redundant components subject to a set of resource constraints. The problem studied in this paper refers to a series-parallel system configuration and allows for component mixing. We propose a new...

Road pricing or road user charging may be understood as an economic concept regarding direct charges applied for using roads. Different pricing paradigms may be distinguished mainly refering to pricing of congested, non-expandable urban networks as well as pricing of expandable, uncongested...

Items with short lifetimes that are subject to deterioration are important in the business world. Research has a long tradition in integrating deterioration and value loss effects into mathematical models for inventory planning and control where such effects are understood as a general loss or...

We present a novel integer programming model for analyzing inter-terminal transportation (ITT) in new and expanding sea ports. ITT is the movement of containers between terminals (sea, rail or otherwise) within a port. ITT represents a significant source of delay for containers being...

Transductive learning involves the construction and application of prediction models to classify a fixed set of decision objects into discrete groups. It is a special case of classification analysis with important applications in web-mining, corporate planning and other areas. This paper...

The blocks relocation problem (BRP) may be defined as follows: given a set of homogeneous blocks stored in a two-dimensional stock, which relocations are necessary to retrieve the blocks from the stock in a predefined order while minimizing the number of those relocations? In this paper, we...