15768911
The Bottleneck Problem with Minimum Quantity Commitments
Given an edge-distance graph of a set of suppliers and clients, the bottleneck problem is to assign each client to a selected supplier minimizing their maximum distance. We introduce minimum quantity commitments to balance workloads of suppliers, provide it a 3-approximation algorithm, and study its generalizations.
16158729
Transshipment Through Crossdocks with Inventory and Time Windows
The supply chain between manufacturers and retailers always includes transshipments through a network of locations. A major challenge in making demand meet supply has been to coordinate transshipment activities across the chain aimed at reducing costs and increasing service levels in the face of a range of factors, including demand fluctuations, short lead times, warehouse limitations and transportation and inventory costs. The success in implementing push-pull strategies, when firms change from one strategy to another in managing the chain and where time lines are crucial, is dependent on adaptive transshipment scheduling. Yet again, in transshipment through crossdocks, where just-in-time objectives prevail, precise scheduling between suppliers, crossdocks and customers is required to avoid inventory backups or delays.
8170241
TPBOSCourier: A Transportation Procurement System (for the Procurement of Courier Services)
TPBOSCourier is the Transportation Procurement and Bid Optimization System (TPBOS) for Philips Electronics to automate and optimize its procurement of courier services. It was jointly developed by Red Jasper Limited and the Hong Kong University of Science and Technology, and has been successfully deployed in 2005. Philips typically procures courier services for more than 2500 shipping lanes annually and the use of the software has resulted in significant cost and time savings in analyzing and optimizing procurement decisions. This paper explains the development and design of the TPBOSCourier.
15852916
Induced uncertain linguistic OWA operators applied to group decision making
The ordered weighted averaging (OWA) operator was developed by Yager [IEEE Trans. Syst., Man, Cybernet. 18 (1998) 183]. Later, Yager and Filev [IEEE Trans. Syst., Man, Cybernet.--Part B 29 (1999) 141] introduced a more general class of OWA operators called the induced ordered weighted averaging (IOWA) operators, which take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are exact numerical values and then aggregated. The aim of this paper is to develop some induced uncertain linguistic OWA (IULOWA) operators, in which the second components are uncertain linguistic variables. Some desirable properties of the IULOWA operators are studied, and then, the IULOWA operators are applied to group decision making with uncertain linguistic information.
4926227
An Approach Based on Similarity Measure to Multiple Attribute Decision Making with Trapezoid Fuzzy Linguistic Variables
In this paper, we investigate the multiple attribute decision making problems under fuzzy linguistic environment. We introduce the concept of trapezoid fuzzy linguistic variable and some operational laws of trapezoid fuzzy linguistic variables. We develop a similarity measure between two trapezoid fuzzy linguistic variables. Based on the similarity measure and the ideal point of attribute values, we develop an approach to ranking the decision alternatives in multiple attribute decision making with trapezoid fuzzy linguistic variables. We finally illustrate the developed approach with a practical example.
7566163
Dirichlet enhanced relational learning
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned by entities or relationships, and are coupled via a common prior distribution. Flexibility is added in a nonparametric hierarchical Bayesian approach, such that the learned knowledge can be truthfully represented. We apply our approach to a medical domain where we form a nonparametric hierarchical Bayesian model for relations involving hospitals, patients, procedures and diagnosis. The experiments show that the additional flexibility in a nonparametric hierarchical Bayes approach results in a more accurate model of the dependencies between procedures and diagnosis and gives significantly improved estimates of the probabilities of future procedures.
5894989
A Hybrid Relevance-Feedback Approach to Text Retrieval
Relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. The conventional RF algorithms either converge slowly or cost a user's additional efforts in reading irrelevant documents. This paper surveys several RF algorithms and introduces a novel hybrid RF approach using a support vector machine (HRFSVM), which actively selects the uncertain documents as well as the most relevant ones on which to ask users for feedback. It can efficiently rank documents in a natural way for user browsing. We conduct experiments on Reuters-21578 dataset and track the precision as a function of feedback iterations. Experimental results have shown that HRFSVM significantly outperforms two other RF algorithms.
16217697
Large-sample performance of blind and Group-blind multiuser detectors: a perturbation perspective
In blind and group-blind multiuser detection, different detectors can be designed using either the sample data covariance matrix directly or its eigencomponents. Due to finite-sample effect in practice, their performance deviates from the corresponding optimum. A perturbation technique is developed rigorously and systematically to analyze those detectors in this work. Subject to the assumption that the first-order perturbation dominates, corresponding results can be applied to a practical system of a given sample size. In particular, performance of the following typical detectors is studied for either flat or estimated multipath channels: direct-matrix-inversion (DMI) blind minimum mean-square error (MMSE) detector, subspace blind MMSE detector, direct zero-forcing (ZF) detector, subspace ZF detector, and group-blind hybrid detector. Simulation examples further verify various analytical results.