Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

An Introduction to MultiAgent Systems.

The current decade sees a considerable growth in worldwide container transportation and with it an indispensable need for optimization. Also the interest in and availability of academic literatures as well as case reports are almost exploding. With this paper an earlier survey which proved to be of utmost importance for the community is updated and extended to provide the current state of the art in container terminal operations and operations research.

Operations research at container terminals: a literature update

On the truck dispatching problem

To improve the productivities of an automated container terminal, it is important to schedule different types of handling equipment in an integrated way. A mixed-integer programming model, which considers various constraints related to the integrated operations between different types of handling equipment, is formulated. This study proposes a heuristic method, called multi-layer genetic algorithm (MLGA) to obtain the near-optimal solution of the integrated scheduling problem and an improved heuristic algorithm, called genetic algorithm plus maximum matching (GAPM), to reduce the computation complexity of the MLGA method. The performance of GAPM is also compared with that of the MLGA method.

https://rd.springer.com/content/pdf/10.1007%2Fs10479-007-0262-5.pdf

Integrated scheduling of handling equipment at automated container terminals

Faced with increasing population density, rising traffic congestion, and the resulting upsurge in carbon emissions, several urban metropolitan areas have instituted public bicycle sharing system as a viable alternative mode of transportation to complement existing long-distance bus- and metro- transit systems. A pressing issue that needs to be addressed in bike sharing systems is the accrued imbalance of bicycles between commuter demands and inventory levels at stations. To overcome this issue, a commonly employed strategy is to reposition bicycles during off-peak periods (typically at night) when no new user arrivals are expected. However, when such an imbalance occurs during day-time peak hours, such a passive strategy would result in lower resource utilization rates. To overcome this drawback, in this study, we propose a dynamic bicycle repositioning methodology that considers inventory level forecasting, user arrivals forecasting, bicycle repositioning, and vehicle routing in a unified manner. A multi-commodity time-space network flow model is presented, which results in an underlying complex nonlinear optimization problem. This problem is then reformulated into an equivalent mixed-integer problem using a model transformation approach and a novel heuristic algorithm is proposed to efficiently solve this model. Specifically, the first stage involves solving the linear relaxation of the MIP model, and a set covering problem is subsequently solved in the second stage to assign routes to the repositioning vehicles. The proposed methodology is evaluated using standard test-bed instances from the literature, and our numerical results reveal that the heuristic algorithm can achieve a significant reduction in rejected user requests when compared to existing methods, while yet expending only minimal computational effort.

A time-space network flow approach to dynamic repositioning in bicycle sharing systems

The unprecedented number and scales of natural and human-induced disasters in the past decade has urged the emergency search and rescue community around the world to seek for newer, more effective equipment to enhance their efficiency. Search and rescue technology to-date still rely on old technologies such as search dogs, camera mounted probes, and technology that has been in service for decades. Intelligent robots equipped with advanced sensors are attracting more and more attentions from researchers and rescuers. This paper presents the design and application of a distributed wireless sensor network prototyping system for tracking mobile search and rescue robots. The robotic system can navigate autonomously into rubbles and to search for living human body heat using its thermal array sensor. The wireless sensor network helps to track the location of the robot by analyzing signal strength. Design and development of the network and the physical robot prototype are described in this paper.

Henry Y. K. Lau

Papers

Integrated scheduling of handling equipment at automated container terminals

A time-space network flow approach to dynamic repositioning in bicycle sharing systems

Robot Assisted Emergency Search and Rescue System With a Wireless Sensor Network

A virtual design platform for interactive product design and visualization

Computers & Industrial Engineering