Fast heuristics for the Steiner tree problem with revenues, budget and hop constraints

This article describes and compares three heuristics for a variant of the Steiner tree problem with revenues, which includes budget and hop constraints. First, a greedy method which obtains good approximations in short computational times is proposed. This initial solution is then improved by means of a destroy-and-repair method or a tabu search algorithm. Computational results compare the three methods in terms of accuracy and speed. (C) 2007 Elsevier B.V. All rights reserved.

Towards universal and statistical-driven heuristics for automatic classification of sports video events

Researchers worldwide have been actively seeking for the most robust and powerful solutions to detect and classify key events (or highlights) in various sports domains. Most approaches have employed manual heuristics that model the typical pattern of audio-visual features within particular sport events To avoid manual observation and knowledge, machine-learning can be used as an alternative approach. To bridge the gaps between these two alternatives, an attempt is made to integrate statistics into heuristic models during highlight detection in our investigation. The models can be designed with a modest amount of domain-knowledge, making them less subjective and more robust for different sports. We have also successfully used a universal scope of detection and a standard set of features that can be applied for different sports that include soccer, basketball and Australian football. An experiment on a large dataset of sport videos, with a total of around 15 hours, has demonstrated the effectiveness and robustness of our
aIlgorithms.

Using decision-tree to automatically construct learned-heuristics for events classification in sports video

Automatic events classification is an essential requirement for constructing an effective sports video summary. It has become a well-known theory that the high-level semantics in sport video can be “computationally interpreted” based on the occurrences of specific audio and visual features which can be extracted automatically. State-of-the-art solutions for features-based event classification have only relied on either manual-knowledge based heuristics or machine learning. To bridge the gaps, we have successfully combined the two approaches by using learning-based heuristics. The heuristics are constructed automatically using decision tree while manual supervision is only required to check the features and highlight contained in each training segment. Thus, fully automated construction of classification system for sports video events has been achieved. A comprehensive experiment on 10 hours video dataset, with five full-match soccer and five full-match basketball videos, has demonstrated the effectiveness/robustness of our algorithms.

Agent-based heuristics to solve NP-hard scheduling problems

A novel agent-driven heuristic approach was developed to control the operational scheduling for a local manufacturer. This approach outperformed the traditional kanban control mechanism under numerous simulated benchmarking tests. Using this approach, the individual machine loading was reduced by, on average, 28%, with the loading spread reduced by 85%

The use of heuristics in valuation practice : implications in a changing market

Despite the existence of prescribed frameworks, valuation remains a cause of much controversy and variety of opinion. It does not matter whether procedures are undertaken in exactly the same way, the conclusion of ‘value’ will vary from valuer to valuer – sometimes considerably. This uncertainty within valuation is founded on property’s heterogeneous nature and the imperfect market that is the property market; in addition to the unpredictability of human behaviour in making judgements (French and Gabrielli 2004). Uncertainty, in valuation is found in the amalgam of locational, physical and legal characteristics and innumerable other forces which control and energise the property market (Whipple 1995). Particular irregular occurrences, or drastic changes in property markets, from either within market evolution or external forces, for example the creation of global financial markets, cause further uncertainty for valuers and provides challenges in identifying ‘market value’ in valuation practice. The praxis of valuation in a commercial sense navigates this complexity using a combination of algorithms and heuristics to identify the value of a property. The application of theoretical mathematical algorithms based on economic theory (Brown 1995), is augmented by valuers’ ability to apply appropriate adjustment based on their knowledge of the market, their ability to analyse, assess and compare the attributes of a property in comparison to its market, and their practical experience (Sliogeriene 2008). Despite the necessity of algorithms, the application of appropriate adjustments and assumptions are important in arriving at a value. This paper is a critical reflection on the basis of valuation practice as guided by standards, methods, and ethics (algorithms), and the use of heuristics in practice. This is important because changes within property markets challenge the inter-relationship between these two aspects of valuation practice. Through the authors’ industry experience and a review of previous research and statements of practice norms this paper provides an analysis of the ability of valuers to address market change in their valuation practices.

Modelling interplay in normative social systems: towards a heuristics formalism of agents and networks

The need for guiding model formulation of normative social systems in support of a digital ecosystem is introduced. Normative social systems improve the understanding of computational social processes in simulation and experimentation, and provide support for digital ecosystem developments. However, a successful simulation requires the appropriate implementation of a conceptual model. It is proposed that an heuristic formalism of agents, networks and environments, complements the conventional creative approach to model formulation by guiding the formulation of conceptual models via abstract components and facilitate interface with other components in a digital environment.