Alexandra Brintrup

An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization

Consideration of qualitative factors is an integral and necessary part of design optimization. In this paper, we consider qualitative factors as design objectives to be optimized and attempt to optimize qualitative and quantitative criteria together. Interactive evolutionary computation (IEC) provides an ideal platform to include qualitative perspectives of the designer for the optimization of a design. This paper reviews the inclusion of qualitativeness in design, makes the case for its necessity, and classifies qualitative concepts in design optimization, before moving onto a comparison between sequential single objective and multi-objective optimization with regards to simultaneous handling of qualitative and quantitative criteria. The manufacturing plant layout design problem is used as an illustrative example. We further examine the enrichment of the optimization methods by allowing human interaction with the program within the framework of IEC. The multi-objective platform is found to be superior. Finally various challenges, such as the influence of human fatigue, are discussed.

RFID opportunity analysis for leaner manufacturing

Although RFID is seen by many as a revolutionary enabler of automated data capture, confusion still remains as to how manufacturing organisations can identify cost-effective opportunities for its use. Managers view promotional business case estimates as unjustified, simulation based analysis and analytical models as secondary modes of analysis, and case studies are scarce. Further, there is a lack of simple tools to understand how RFID can help to achieve a leaner manufacturing environment, after the use of which practitioners can be routed to grounded forms of analysis. The purpose of this paper is to provide and test such a toolset, which uses the seven Toyota Production System wastes as a template. In our approach, RFID technology is viewed as a vehicle to achieve leaner manufacturing through automated data collection, assurance of data dependencies, and improvements in production and inventory visibility. The toolset is tested on case examples from two push-based, multi-national fast moving consumer goods manufacturing companies. The opportunity analysis is shown to identify not only initially suspected areas of improvement, but also other areas of value.

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.

Behaviour adaptation in the multi-agent, multi-objective and multi-role supply chain

Researchers, practitioners and enterprise software providers are realising the potential of agent-based technology to automate supply chain procurement to achieve consistent, traceable decision making. As the complexity of supply chains grow, these systems will gain more attention. In this paper, we model and simulate a complex autonomous supply chain managed by computational agents that aim to minimise lead time and maximise revenue through evolutionary multi-objective optimisation. The agents are in a competitive environment where they take on the roles of both client and producer. In addition to optimising their production strategy, they also have the opportunity to dynamically fine-tune their decision parameters when it comes to selecting their own suppliers, using the Analytical Hierarchy Process. It is observed that computational agents are capable of functioning in such complex environments, effectively converging to policies in synergy with their market. Multi-objective, multi-role optimisation results in better overall supply chain performance than tests where agents have single-objectives and single-roles. Our study forms an exploratory step towards more realistic agent-based supply chains where analytical methods are unavailable.

Will Intelligent Assets Take Off? Toward Self-Serving Aircraft

The authors describe a self-serving asset built on an intelligent, self-aware agent platform that maximizes its service life by contacting, selecting, and procuring service providers autonomously.

Supply Networks as Complex Systems: A Network-Science-Based Characterization

Outsourcing, internationalization, and complexity characterize todays aerospace supply chains, making aircraft manufacturers structurally dependent on each other. Despite several complexity-related supply chain issues reported in the literature, aerospace supply chain structure has not been studied due to a lack of empirical data and suitable analytical toolsets for studying system structure. In this paper, we assemble a large-scale empirical data set on the supply network of Airbus and apply the new science of networks to analyze how the industry is structured. Our results show that the system under study is a network, formed by communities connected by hub firms. Hub firms also tend to connect to each other, providing cohesiveness, yet making the network vulnerable to disruptions in them. We also show how network science can be used to identify firms that are operationally critical and that are key to disseminating information.

Optimising Home Automation Systems: A comparative study on Tabu Search and Evolutionary Algorithms

We use the Multi Agent System paradigm to model and analyse Home Automation System performance in exploiting limited resources such as electricity and hot water. In this paper we evaluate several approaches to the optimisation of Home Automation System performance using Tabu Search, and Single and Multi-objective Genetic Algorithms. The results show that the Genetic Algorithms achieve faster convergence than Tabu Search. Multi-objective Genetic Algorithm provides a diverse set of solutions for the decision maker.

Integrated qualitativeness in design by multi-objective optimization and interactive evolutionary computation

The concept of qualitativeness in design is an important one, and needs to be incorporated in the optimization process for a number of reasons outlined in this paper. interactive evolutionary computation and fuzzy systems are two of the widely used approaches for handling quanlitativeness in design optimization. This paper classifies the types of quantitativeness observed in design optimization, makes the case for their necessity, and proposes a novel framework for handling them, combining the two approaches in an evolutionary multi-objective optimization platform. Two components of the framework are tested using the floor-planning problem, and observations are reported. Future work is defined on the development of the framework.

Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents

The objects of the Internet of Things will be empowered by embedded devices whose constrained resources will need to be managed efficiently. It is envisioned that these devices will be able to form ad-hoc networks, and that the connection from these networks to the Internet of Things infrastructure will not always be possible. In this chapter we propose the use of clustering, software agents and synchronisation techniques in order to overcome the challenges of managing the resources of the Internet of Things objects. We argue that clustering will be beneficial to reduce the energy expenditure and improve the scalability and robustness of the object networks. Software agents will aide in the automation of task, both for the objects and the Internet of Things users. Finally, synchronisations techniques will be necessary to address the various challenges of harmonising plenty of copies of object data with potentially partially disconnected Internet of Things architecture components.

Systemic Risk Assessment in Complex Supply Networks

The growth in size and complexity of supply chains has led to compounded risk exposure, which is hard to measure with existing risk management approaches. In this study, we apply the concept of systemic risk to show that centrality metrics can be used for complex supply network risk assessment. We review and select metrics, and set up an exemplary case applied to the material flow and contractual networks of Honda Acura. In the exemplary case study, geographical risk information is incorporated to selected systemic risk assessment metrics and results are compared to assessment without risk indicators in order to draw conclusions on how additional information can enhance systemic risk assessment in supply networks. Katz centrality is used to measure the nodes risk spread using the World Risk Index. Authority and hub centralities are applied to measure the link risk spread using distances between geographical locations. Closeness is used to measure speed of disruption spread. Betweenness centrality is used to identify high-risk middlemen. Our results indicate that these metrics are successful in identifying vulnerabilities in network structure even in simplified cases, which risk practitioners can use to extend with historical data to gain more accurate insights into systemic risk exposure.