Permeability correction factor for fractures with permeable walls

Enhanced Geothermal Systems (EGS) are based on the premise that heat can be extracted from hot dry rocks located at significant depths by circulating fluid through fracture networks in the system. Heated fluid is recovered through production wells and the energy is extracted in a heat exchange chamber. There is much published research on flow through fractures, and many models have been developed to describe an effective permeability of a fracture or a fracture network. In these cases however, the walls of the fracture were modelled as being impermeable. In this paper, we have extended our previous work on fractures with permeable walls, and we introduce a correction factor to the equation that governs fracture permeability. The solution shows that the effective fracture permeability for fractures with permeable walls depends not only on the height of the channel, but also on the wall permeability and the wall Reynolds number of the fluid. We show that our solution reduces to the established solution when the fracture walls become impermeable. We also extend the discussion to cover the effective permeability of a system of fractures with permeable walls.R. Mohais, C. Xu, P. A. Dowd, and M. Han

Earthquake classifying neural networks trained with random dynamic neighborhood PSOs

This paper investigates the use of Random Dynamic Neighborhoods in Particle Swarm Optimization (PSO) for the purpose of training fixed-architecture neural networks to classify a real-world data set of seismological data. Instead of the ring or fully-connected neighborhoods that are typically used with PSOs, or even more complex graph structures, this work uses directed graphs that are randomly generated using size and uniform out-degree as parameters. Furthermore, the graphs are subjected to dynamism during the course of a run, thereby allowing for varying information exchange patterns. Neighborhood re-structuring is applied with a linearly decreasing probability at each iteration. Several experimental configurations are tested on a training portion of the data set, and are ranked according to their abilities to generalize over the entire set. Comparisons are performed with standard PSOs as well as several static non-random neighborhoods.Arvind S. Mohais, Rosemarie Mohais, Christopher Ward and Christian Posthof

Applications of evolutionary methods for complex industrial problems

Adaptive Business Intelligence systems combine the methods and techniques that enable prediction, optimisation and adaptation. Adaptive Business Intelligence software solutions powered by Computational Intelligence tools are invaluable for making better predictions and decisions. This paper discusses applicability of Adaptive Business Intelligence software systems for Demand Planning, Advanced Planning and Scheduling, and Supply Chain Network Optimisation, developed by SolveIT Software Pty Ltd.Zbigniew Michalewicz, Maksud Ibrahimov, Arvind Mohais, Sven Schellenberg and Neal Wagnerhttp://www.eurogen2009.polsl.pl/index.htm

Combining vehicle routing and packing for optimal delivery schedules of water tanks

This article describes a decision-support system that was developed in 2011 and is currently in production use. The purpose of the system is to assist planners in constructing delivery schedules of water tanks to often remote areas in Australia. A delivery schedule consists of a number of delivery trips by trucks. An optimal delivery schedule minimises cost to deliver a given total sales value of delivered products. To construct an optimal delivery schedule, trucks need to be optimally packed with water tanks and accessories to be delivered to a set of delivery locations. This packing problem, which involves many packing and loading constraints, is intertwined with the transport problem of minimising distance travelled by road. Such a decision-support system that optimises multi-component operational problems is of great importance for an organisation; it supports what-if analysis for operational and strategic decisions and trade-off analysis to handle multi-objective optimisation problems; it is capable of handling and analysing variances; it is easy to modify – constraints, business rules, and various assumptions can be re-configured by a client. Construction of such decision-support systems requires the use of heuristic methods rather than linear/integer programming.Jacob Stolk, Isaac Mann, Arvind Mohais and Zbigniew Michalewic

Comparison of different evolutionary algorithms for global supply chain optimisation and parameter analysis

This paper discusses global optimisation from a business perspective in the context of the supply chain operations. A two-silo supply chain was built for experimentation and three approaches were used for global optimisation: a classical evolutionary approach, a cooperative coevolutionary approach and a coevolutionary approach with on the fly partner generation where the solution from the second component of the supply chain is generated deterministically based on the first one. The second approach produced higher quality solutions due to its use of communication between silos. Additional experiment was conducted to choose optimal species sizes.Maksud Ibrahimov, Arvind Mohais, Sven Schellenberg, Zbigniew Michalewiczhttp://cec2011.org

Global optimization in supply chain operations

This chapter discusses some optimization issues from a business perspective in the context of the supply chain operations. We note that the term “global optimization” may have different meanings in different communities and we look at it from the business and classical optimization points of view. We present two real-world optimization problems which differ in scope and use them for our discussion on global optimization issues. The differences between these two problems, experimental results, the main challenges they present and the algorithms used are discussed. Here, we claim neither uniqueness nor superiority of the algorithms used, rather the main goal of this chapter is to emphasize the importance of the global optimization concept.Maksud Ibrahimov, Arvind Mohais and Zbigniew Michalewic

Evolutionary approaches for supply chain optimisation: Part I: single and two-component supply chains

PURPOSE – The purpose of this paper and its companion (Part II: multi-silo supply chains) is to investigate methods to tackle complexities, constraints (including time-varying constraints) and other challenges. In tis part, the paper aims to devote attention to single silo and two-silo supply chains. It also aims to discuss three models. The first model is based on the winebottling real-world system and exposes complexities of a single operational component of the supply chain. The second model extends it to two components: production and distribution. The last system is a real-world implementation of the two-component supply chain. DESIGN/METHODOLOGY/APPROACH – Evolutionary approach is proposed for a single component problem. The two-component experimental supply chain is addressed by the algorithm based on cooperative coevolution. The final problem of steel sheet production is tackled with the evolutionary algorithm. FINDINGS – The proposed systems produce solutions better than solutions proposed by human experts and in a much shorter time. ORIGINALITY/VALUE – The paper discusses various algorithms to provide the decision support for the real-world problems. The proposed systems are in the production use.Maksud Ibrahimov, Arvind Mohais and Sven Schellenberg, Zbigniew Michalewic

Time-varying constraints and other practical problems in real-world scheduling applications

When an evolutionary algorithm is used as an optimizer in a scheduling software application that is destined for use in a real-world commercial setting, a number of timevariability issues are encountered. This paper explores several such issues and other practical problems that arose during the solution of a scheduling application in the area of wine bottling. Each hurdle was addressed by appropriately adjusting the candidate individual representation, the procedure used to decode an individual, or the objective function itself. Addressing these issues is critical when designing and constructing the evolutionary algorithm, in order to ensure that the resulting system is robust enough to meet the demands of day-to-day use. The approach described in this paper has been proven by implementation and vigorous sustained use in a complex business environment.Arvind Mohais, Maksud Ibrahimov, Sven Schellenberg, Neal Wagner, and Zbigniew Michalewic

Advanced planning in vertically integrated supply chains

During the last few years most production-based businesses have been under enormous pressure to improve their top-line growth and bottom-line savings. As a result, many companies are turning to systems and technologies that can help optimise their supply chain activities and improving short-and long-term demand forecasting. Given the inherent complexities of planning and scheduling in vertically integrated supply chains, many new methods (e.g., ant systems, evolutionary algorithms, fuzzy systems, genetic algorithms, neural networks, rough sets, swarm intelligence, simulated annealing, tabu search-collectively known as "Computational Intelligence" methods) have been introduced into software applications to help manage and and optimise this complexity. In this paper we discuss two realworld applications of advanced planning: one from wine industry and the other-from mining industry. © 2011 Springer-Verlag Berlin Heidelberg.Maksud Ibrahimov, Arvind Mohais, Sven Schellenberg, and Zbigniew Michalewiczhttp://nla.gov.au/anbd.bib-an4802882

Evolutionary approaches for supply chain optimisation. Part II: Multi-silo supply chains

PURPOSE – The purpose of this paper and its companion (Part I: single and two-component supply chains) is to investigate methods to tackle complexities, constraints (including time-varying constraints) and other challenges. In this part, attention is devoted to multi-silo supply chain and the relationships between the components. The first part of the paper aims to consider two types of experimental supply chains: with one-to-many and many-to-one relationships. The second half of the paper aims to present two approaches on optimising the material flow in the real-world supply chain network. DESIGN/METHODOLOGY/APPROACH – Cooperative coevolutionary and classical sequential approaches are taken to address the experimental multi-silo supply chains. Due to the nature and the complexity of the supply chain presented in the second half of the paper, evolutionary algorithm was not sufficient to tackle the problem. A fuzzy-evolutionary algorithm is proposed to address the problem. FINDINGS – The proposed systems produce solutions better than solutions proposed by human experts and in much shorter time. ORIGINALITY/VALUE – The paper discusses various algorithms to provide the decision support for the real-world problems. The system proposed for the real-world supply chain is in the process of integration to the production environment.Maksud Ibrahimov, Arvind Mohais and Sven Schellenberg, Zbigniew Michalewic