37711447
Searching Optimal Resequencing and Feature Assignment on an Automated Assembly Line
In this paper, we have solved the resequencing and feature assignment problem (RFAP) by an iterative search scheme, which can obtain optimum solutions for instances sized as large as that in reality. The search scheme is based on a beam search heuristic, which outperform other heuristics in previous literature. The algorithms proposed can therefore be utilized to improve the vehicle manufacturing and to benchmark the optimum or near-optimum solutions for future research
29379871
A Fixed-Length Subset Genetic Algorithm for the p-Median Problem
In this paper, we review some classical recombination operations and devise new heuristic recombinations for the fixed-length subset. Our experimental results on the classical p-median problem indicate that our method is superior and very close to the optimal solution.
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.
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.
11036293
Virtual reality for neuropsychological diagnosis and rehabilitation: a survey
Recently the considerable potential of virtual reality has been recognized for the scientific study, diagnosis and treatment in the field of mental healthcare. It provides a unique opportunity to provide a natural interface and to mimic actual challenges faced by impaired subjects in their lives. In spite of certain limitations, some work has emerged which provides guidelines for future research efforts. A shortlist of such applications includes assessment and treatment of phobias; obsessive compulsive disorders, posttraumatic stress disorders and hyperactive attention deficit syndromes. We highlight these recent and ongoing research projects and compare this unique paradigm with conventional nonVR techniques by discussion of potential benefits and possible shortcomings. Due to the nature of research, ethical and human factors cannot be over emphasized. We also describe the limitations of VR in the field of practical psychology as media hype has over sold its potential. With recent advances in technology, it is not unrealistic to hope that VR will soon be a part of a clinician's armoury.
20162029
Effective Feature Preprocessing for Time Series Forecasting
Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting, there is so far no systematic research to study and compare their performance. How to select effective techniques of feature preprocessing in a forecasting model remains a problem. In this paper, the authors conduct a comprehensive study of existing feature preprocessing techniques to evaluate their empirical performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time series forecasting models.
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.
1744668
Feature Selection with Particle Swarms
Feature selection is widely used to reduce dimension and remove irrelevant features. In this paper, particle swarm optimization is employed to select feature subset for classification task and train RBF neural network simultaneously. One advantage is that both the number of features and neural network configuration are encoded into particles, and in each iteration of PSO there is no iterative neural network training sub-algorithm. Another is that the fitness function considers three factors: mean squared error between neural network outputs and desired outputs, the complexity of network and the number of features, which guarantees strong generalization ability of RBF network. Furthermore, our approach could select as small-sized feature subset as possible to satisfy high accuracy requirement with rational training time. Experimental results on four datasets show that this method is attractive.