Parallel Implementation of an Artificial Neural Network Integrated Feature and Architecture Selection Algorithm

The selection of salient features and an appropriate hidden layer architecture contributes significantly to the performance of a neural network. A number of metrics and methodologies exist for estimating these parameters. This research builds on recent efforts to integrate feature and architecture selection for the multilayer perceptron. In the first stage of work a current algorithm is developed in a parallel environment, significantly improving its efficiency and utility. In the second stage, improvements to the algorithm are proposed. With regards to feature selection, a common random number (CRN) addition is proposed. Two new methods of architecture selection are examined, to include an information criterion and a signal to noise based procedure. These methodologies are shown to improve algorithm performance

A bi-objective model for scheduling green investments in two-stage supply chains

Investing in green technologies to increase sustainability in supply chains has become a common practice for two reasons: the first is directly related to the defense of the environment and people’s health to smooth the emissions of pollutants; the second is the increasing consumer awareness of green products. Despite the higher costs of producing with green technologies and processes, there is also a higher markup on the price of products which rewards the former costs. This study proposes a mathematical model for scheduling green investments over time in a two-stage supply chain to minimize the impact of production on the environment and the economic costs deriving from the investment. The resulting bi-objective model has nonlinear constraints and is solved using a commercial solver. Given its complexity, we propose an upper-bound heuristic and a lower-bound model to reduce the optimality gap attained at a given time limit. Tests on synthetic instances have been conducted, and an example demonstrates the applicability and efficacy of the proposed model

Optimization Heuristics for the Combinatorial Auction Problem

This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the context of a price controlled resource allocation process designed for the control and provision of distributed information services. Comparing the SG and SA method shows that depending on the problem structure the performance of the SA is up to 20% higher than the performance of the simple greedy allocation method. The proposed GA approach, using a random key encoding, results in a further improvement of the solution quality. Although the metaheuristic approaches result in higher search performance, the computational effort in terms of used CPU time is higher in comparison to the simple greedy mechanism. However, the absolute overall computation time is low enough to enable real-time execution in the considered IS application domain

Why simheuristics? : Benefits, limitations, and best practices when combining metaheuristics with simulation

Many decision-making processes in our society involve NP-hard optimization problems. The largescale, dynamism, and uncertainty of these problems constrain the potential use of stand-alone optimization methods. The same applies for isolated simulation models, which do not have the potential to find optimal solutions in a combinatorial environment. This paper discusses the utilization of modelling and solving approaches based on the integration of simulation with metaheuristics. These 'simheuristic' algorithms, which constitute a natural extension of both metaheuristics and simulation techniques, should be used as a 'first-resort' method when addressing large-scale and NP-hard optimization problems under uncertainty -which is a frequent case in real-life applications. We outline the benefits and limitations of simheuristic algorithms, provide numerical experiments that validate our arguments, review some recent publications, and outline the best practices to consider during their design and implementation stages

Models and metaphors: complexity theory and through-life management in the built environment

Complexity thinking may have both modelling and metaphorical applications in the through-life management of the built environment. These two distinct approaches are examined and compared. In the first instance, some of the sources of complexity in the design, construction and maintenance of the built environment are identified. The metaphorical use of complexity in management thinking and its application in the built environment are briefly examined. This is followed by an exploration of modelling techniques relevant to built environment concerns. Non-linear and complex mathematical techniques such as fuzzy logic, cellular automata and attractors, may be applicable to their analysis. Existing software tools are identified and examples of successful built environment applications of complexity modelling are given. Some issues that arise include the definition of phenomena in a mathematically usable way, the functionality of available software and the possibility of going beyond representational modelling. Further questions arising from the application of complexity thinking are discussed, including the possibilities for confusion that arise from the use of metaphor. The metaphor of a 'commentary machine' is suggested as a possible way forward and it is suggested that an appropriate linguistic analysis can in certain situations reduce perceived complexity

The Role of Social Context in Modulating Gene Expression, Neural Activity, and Neuroendocrine Response in Individuals of Varying Social Status

Social context, which includes both the direct social experience of individuals as well as the characteristics of their social network as a whole, has been shown to be an important modulator of behavior across species. However, relatively little is known about the role of social context in regulating the complex relationships between neurobiology, neuroendocrine response, and behavior in mammals. Historically, the neurobiology of social behavior has been studied at the dyadic level, looking at brief social interactions between pairs of individuals. Given that all social species live in groups, rather than pairs, it is essential that we begin to understand the role social context at the group level plays in regulating physiology. Throughout this thesis, I use a novel behavioral housing system to study how the characteristics of stable social groups and how instances of social opportunity, when individuals are ascending up a social hierarchy, are associated with differential brain gene expression, neuroendocrine output, and behavior. I first extensively analyze the social dynamics of male dominance hierarchies, showing that they are both consistent, in that males reliably form significantly linear dominance hierarchies, and unique, in that the characteristics of these hierarchies vary from group to group. I further prove that mice living in these social hierarchies are extremely socially competent, displaying the ability to respond appropriately to individuals of varying social status. I demonstrate that females are capable of forming dominance hierarchies as well, but that their hierarchies differ from those of males. I then use this foundational knowledge to investigate how these different hierarchy characteristics can lead to differences in physiology, how one’s social status is associated with brain gene expression and neuroendocrine response, and how disruption of a hierarchy through removal of the alpha male leads to robust behavioral as well as physiological consequences. Finally, I use the insights gained from this immediate early gene work to demonstrate the crucial role of the infralimbic/prelimbic region of the medial prefrontal cortex in regulating socially competent response to changing social contexts. Taken together, this work establishes the broad role social context plays in regulating the complex relationships between behavior, brain gene expression, neural activation, and neuroendocrine output

Development of a hybrid metaheuristic for the efficient solution of strategic supply chain management problems: application to the energy sector

Supply chain management (SCM) addresses the strategic, tactical, and operational decision making that optimizes the supply chain performance. The strategic level defines the supply chain configuration: the selection of suppliers, transportation routes, manufacturing facilities, production levels, technologies. The tactical level plans and schedules the supply chain to meet actual demand. The operational level executes plans. Tactical and operational level decision-making functions are distributed across the supply chain. To increase or optimize performance, supply-chain functions must be perfectly coordinated. But the cycles of the enterprise and the market make this difficult: raw material does not arrive on time, production facilities fail, workers are ill, customers change or cancel orders, therefore, causing deviations from the plan. In some cases, these situations may be dealt with locally. In other cases, the problem cannot be ”locally contained” and modifications across many functions are required. Consequently, the supply chain management system must coordinate the revision of plans or schedules. The ability to better understand an algorithm is important to focus on the following variables: tactical and operational levels of the supply chain so that the timely dissemination of information, accurate coordination of decisions, and management of actions among people and systems is achieved ultimately determines the efficient, coordinated achievement of enterprise goal

A study of facility management knowledge classification for the effective stewardship of existing buildings

The aim of the study was to establish the Facility Management knowledge categories within the life cycle of a building context. The significance of the study stemmed from research undertaken into the compliance to Australian Standards 1851-17:2005 Maintenance of Fire and Smoke Doors within West Australian nursing homes, which demonstrated 87 per cent non-compliance. The level of non-compliance appeared to identify a lack of knowledge, and appropriately qualified and experienced personnel involved within the management of nursing homes (Doleman, 2008). The issues identified prompted the question on how facility management knowledge categories evolves and develops throughout the life cycle of a building. The research used a three Phase, Grounded Theory interpretive analysis of the Facility Management knowledge construct. Phase One involved the examination of 21 international tertiary undergraduate Facility Managers courses. The course content was analysed and assessed through linguistic analysis to extract the knowledge categories and subordinate concepts. The findings identified 14 primary knowledge categories which were presented to 10 Facility Management experts for validation. Phase Two presented the findings of Phase One in a Multi Dimensional Scaling (MDS) survey instrument to Facility Management experts for dissimilarity assessments. The results from the 56 completed surveys were embedded within MDS software to present spatial knowledge proximity cluster analysis. The final phase was the validation of the research findings through semi-structured interviews of 10 industry experts, selected with consideration of heterogeneity in order to validate the findings of the previous phase. The outcome of this study was to develop an understanding of the Facility Management knowledge categories within the life cycle of a building context and the identification of 14 core knowledge base, required as a Facility Manager practitioner. Core knowledge categories included Finance as a central theme within the Facility Management domain with Building Services and Business providing an indication as to the broad nature of Facility Management knowledge construct. Also identified within the research was the lack of legislative harmonisation between different states and territories within the Facility Management domain and the disparity between Facility Management practitioners with regards to knowledge context and application. The role of Facility Management and their involvement within the lifecycle of a building was also identified within the research as being little or none during the design and construction phases of the building. The handover and management of the buildings to Facility Managers occurs within the occupancy phase of the buildings life cycle meaning that the building was inherited without due consideration of continued operational efficiencies or functionality affecting the overall cost effectiveness of the building. Such outcomes lead to a number of recommendations such as a the introduction of central knowledge standard in order to provide context of definitions and well as the continued development and drive of Facility Management practitioners and associations to establish the Facility Management profession as a respected body

Deep Space Network information system architecture study

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control