A Generic library of problem-solving methods for scheduling applications

In this paper we describe a generic library of problem-solving methods (PSMs) for scheduling applications. Although, some attempts have been made in the past at developing libraries of scheduling methods, these only provide limited coverage: in some cases they are specific to a particular scheduling domain; in other cases they simply implement a particular scheduling technique; in other cases they fail to provide the required degree of depth and precision. Our library is based on a structured approach, whereby we first develop a scheduling task ontology, and then construct a task-specific but domain independent model of scheduling problem-solving, which generalises from specific approaches to scheduling problem-solving. Different PSMs are then constructed uniformly by specialising the generic model of scheduling problem-solving. Our library has been evaluated on a number of real-life and benchmark applications to demonstrate its generic and comprehensive nature

Modelling,simulation, and analysis of HAL Bangalore13; international airport

Air traffic density in India and the world at large is growing fast and posing challenging13; problems. The problems encountered can be parameterized as flight delay, workload of air traffic13; controllers and noise levels in and around aerodromes. Prediction and quantification of these13; parameters aid in developing strategies for efficient air traffic management. In this study, the13; method used for quantifying is by simulation and analysis of the selected aerodrome and air13; space. This paper presents the results of simulation of HAL Bangalore International Airport,13; which is used by civil as well as military aircraft. With the test flying of unscheduled military13; aircraft and the increase in the civil air traffic, this airport is hitting the limit of acceptable delay.13; The workload on air traffic controllers is pushed to high during peak times. The noise contour13; prediction, especially for the test flying military aircraft is sounding a wake up call to the13; communities living in the vicinity of the Airport.13

An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601