Ebook: Intelligent Decision Technologies

The current volume includes the research results presented at the Fifth International Conference on Intelligent Decision Technologies (KES-IDT 2013) which took place in June 26-28, 2013, in Sesimbra, Portugal.

KES-IDT is a well established international annual conference, interdisciplinary conference in nature, and this edition consisted of keynote talks, oral and poster presentations, invited sessions and workshops, on the applications and theory of intelligent decision systems and related areas. It provided excellent opportunities for the presentation of interesting new research results and discussion about them, leading to knowledge transfer and generation of new ideas.

Sesimbra is a municipality lying at the foothills of the Serra da Arrábida, a mountain range between 40 km to the South of Portugal's capital, Lisbon. To the East of Sesimbra lies Arrábida Natural Park with natural caves, beaches and beautiful trails. To the West you'll find more beaches as well as Cabo Espichel with its scenic hiking trails, dinosaur footprints and ancient monastery. To the South lies Praia California and the Atlantic Ocean. Sesimbra is sheltered and the climate here is typically warmer than in most areas along the coast.

KES-IDT 2013 received many high quality submissions and all papers have been reviewed by at least two reviewers. Following a rigorous reviewing process, not all submissions could be accommodated for presentation at the conference. From these, 45 papers were accepted for presentation and included in this Proceedings. We are very satisfied with the quality of the program and would like to thank the authors for choosing KES-IDT as the forum for presentation of their work. Also, we gratefully acknowledge the hard work of KES-IDT international program committee members and of the additional reviewers for taking the time to review the submitted papers rigorously and select the best among them for presentation at the conference and inclusion in its proceedings.

We are also grateful to the KES personnel for their wonderful work in maintaining the KES-IDT 2013 website. Finally, we would like to thank the IOS Press personnel for their wonderful job in producing this volume.

The General and Program Co-Chairs:

Rui Neves Silva, Universidade Nova de Lisboa, Portugal

Gloria Wren-Phillips, Loyola University, USA

Junzo Watada, Waseda University, Japan

Lakhmi C. Jain, University of South Australia, Australia

The Executive Chair:

Robert Howlett, KES International & Bournemouth University, UK

The Local Arrangements Chair:

Ana Rita Campos, UNINOVA, Portugal

In this paper, we present a prototype of a clinical decision-support system. This prototype relies on a two-phase algorithm that is based on the differential diagnosis method from medical diagnostics and predictive models for disease occurrence in a subpopulation. The algorithm requires a data set containing information about diseases and their corresponding symptoms, and a data set with registered disease cases. The main output of this algorithm is a ranked list of diagnoses that might explain the manifested symptoms. The ranking is influenced by the patient's context, i.e., disease trends within a subpopulation to which the patient belongs. In the context of medical diagnosis discovery based on symptom matching, we present a short rationale for developing such a system, brief review of similar systems, algorithm for diagnoses ranking, and ideas for future research. Furthermore, we elaborate on the required data sets and illustrate the application of the proposed solution with a typical use scenario.

Farm-to-door online retail for fruit and vegetable is new business mode in China, which could connect farmers and customers directly. This mode is a promising business field as it could offer fresh and convenient agri-food to citizens, who barely have time to go to food markets on weekdays. However, with the growing of customer's amount, the efficiency of warehouse and logistics operations has been the block of its expansion. And currently the most time-consuming procedures in Delivery Centre (DC) are order picking and goods delivery, which are conducted one after another. In order to improve the operation efficiency of DC, we proposed a group-based order batching method to organize the warehouse and delivery operations as a pipeline. Firstly, the order picking process is modelled, which refers to vehicle routing plan. Then, we employ the group technology and put forward a group-based order batching method. Finally, we apply the proposed methodology on the farm-to-door fruit and vegetable online retail company in China. The result shows that the group-based order batching method could greatly reduce the operation time. This is a new order batching method, which could promote the development of farm-to-door business mode for fresh agri-food in China. And it is also offer a new idea for order picking process of large B2C online retailers.

In this paper, a new variant of the Radial Basis Function Network with the Dynamic Decay Adjustment algorithm (i.e., RBFNDDA) to undertake data classification problems is proposed. The new network is formed by integrating the learning algorithm of the Fuzzy ARTMAP (FAM) neural network into RBFNDDA. The proposed RBFNDDA-FAM network inherits the salient features of FAM and overcomes the shortcomings of the original RBFNDDA network. The effectiveness of RBFNDDA-FAM is demonstrated using two benchmark problems. The first involves an artificial data set whereas the second uses a medical data set related to thyroid diagnosis. The results from these studies are compared, analyzed, and discussed. The outcomes positively reveal the potentials of RBFNDDA-FAM in learning information with a compact network architecture, in addition to high classification performances.

In order to implement extended producer responsibility (EPR) and improve the efficiency of recycling as well as benefits, producers (demand-side of logistics) select the third-party to recycle and process waste electrical and electronic equipment (WEEE). The relationship between production enterprises and third-party reverse logistics providers is a principal-agent. Under the constraint that the third-party reverse logistics providers' environmental protection ability and effort level are asymmetric information, the principal-agent models between the production enterprises and third-party reverse logistics providers are established, and then the models are solved as well as influencing factors are analyzed. It is found that the more effort the third party reverse logistics providers (TPRLP) make to recycle waste, the greater the strength factor of incentive pay is and the bigger the cost coefficient of TPRLP is, the smaller the strength factor of incentive pay is.

Decision making processes are becoming increasingly collaborative and multi-disciplinary in nature. Aiming to augment the quality of decision making in contemporary complex settings, this paper reports on an innovative approach that offers a range of alternative - but meaningfully interrelated - visualizations of the context under consideration. These visualizations incorporate suitable reasoning mechanisms that exploit human and machine understandable knowledge to facilitate the underlying what-if analysis and aid stakeholders towards reaching consensus and, ultimately, making a collective decision.

This paper provides a literature review of three complementary research areas: Business Models, Business Applications of Agent Based Modeling and Digital Business Characteristics. Based on these reviews, a Dynamic Agent Based Modelling Framework (DYNAMOD) is developed for performing Digital Business Simulations. This Framework is customisable and computationally implements key digital business characteristics including network effects, online and offline word of mouth, pricing strategies, amongst other features of the Digital Business Environment. DYNAMOD can be a generic framework for developing a variety of forcasting and simulation models that can provide a new computational approach to Digital Business Modeling and Analysis.

In this paper, we describe the absolute measurement method and its Kinoshita's implementation in Section 1. The dominant standard method is described in Section 2. We discuss equivalences and differences among these methods, and we show conditions of agreeing scores or disagreeing them in Section 3. Conclusions and future works are described in Section 4.

Knowledge extraction from data in the form of rules is a widespread direction in data mining area, which allows to obtain interesting relationships in data from large databases in for a human easily understandable form. This paper deals with one of the methods for extraction of rules from data which extract rules in form of a formula in considered fuzzy logic by means of artificial neural networks with special architecture. Using artificial neural networks in extraction process, above mentioned methods gain good approximation of analyzed data and thanks to special architecture allows to extract human-understandable knowledge. The method described in this paper was, however, missing any module, that is a standard part of the most of methods used for rules extraction from data, that would allow to the user subjective selection of the best ratio between accuracy and comprehensibility of the model. This is especially important feature for solving data mining tasks called searching of concepts descriptions, which allow to the user to get a good insight and understanding of the analyzed data. Thus, the main contribution of this paper is a design of such a module inspired by a similar module in methods for extraction of the so-called decision trees. Performance of this new module is illustrated on a standard dataset in two experiments.

This paper proposes a decision support approach for energy savings and emissions trading based on the requirements collected through a set of industrial users. These requirements served as guideline for identification of needs that should be addressed with respect to the decision support approach, constituting a fundamental step for future platform development. The decision support approach proposes two different perspectives: (i) support for immediate reaction: based on the paradigm of intelligent decision support implemented through the use of Case-based Reasoning together with probabilistic analysis; and (ii) support for process reconfiguration and Emission Trading System (ETS) is implemented through the use of multi-criteria decision analysis using MACBETH. The paper proposes categorization of approaches and main criteria involved in the process.

Fuzzy set theory has been applied to build various portfolio selection models in the past decades. Based on the knowledge of previous studies, this paper proposes a new portfolio selection model with technical pattern-based fuzzy birandom variables. There are two innovations in the work: The concept of technical pattern is combined with fuzzy set theory to use the fuzzy birandom variables as security returns; The fuzzy birandom Value-at-Risk (VaR) is introduced to build the mathematical model, named the fuzzy birandom VaR-based portfolio selection model (FBR-PSM). Then, fuzzy simulation is extended to the fuzzy birandom case to obtain a general solution to the FBR-PSM, which is called as fuzzy birandom simulation-based particle swarm optimization algorithm (FBS-PSO). To illustrate the performances of the FBR-PSM and the FBS-PSO, two numerical examples are introduced based on investors' different risk attitudes. Finally, we analyze the experimental results and provide further discussions.

Much work has been done on making and perfecting agent-based simulations on child safety measures in cars. These simulations, using different algorithms, try and predict what factors are responsible for propagation of knowledge about child safety measures in a society. One of the biggest factors being over-looked in these simulations is validity. In absence of validation, these models may not be a true representation of a real world scenario and the trends predicted are questionable. This paper proposes a system design using regression analysis and predictive data mining on a survey done in the field of child safety measure. Using the result of this data mining process in form of a decision tree, we can initialize our agent-based model with data from the survey and later validate our model comparing the results to the survey.

We elucidate correlations among stock price movements in S&P 500 and Tokyo Stock Exchange (TSE) taking advantage of the concept of community in networks. The correlation matrix, purified by random matrix theory, is regarded as the adjacency matrix for a stock correlation network. The network thus constructed has links with weights of either sign depending on whether stocks are correlated (positive) or anticorrelated (negative). Community is defined here as a group of stocks related to each other with positive correlation coefficients. The community detection allows us to find that the stocks in S&P 500 are split up into four communities with two conflicting triangular relations. In TSE, there exists three communities of stocks forming a conflicting triangle. We thus see that the frustrated correlation structure is common to the well-developed financial markets.

Integration of principal component analysis (PCA) with random matrix theory (RMT) has been successful in analyzing cross correlations between stock price movements in financial markets. RMT is used as a null hypothesis to distinguish between genuine cross correlations and noises. In this paper, we develop a RMT-aided complex PCA method based on the Hilbert transformation of time series. The complex data thus generated carry dynamic information in a form of instantaneous phase; the conventional PCA is entirely dependent on simultaneous correlations in time. Accordingly RMT is generalized to be adaptable to complex PCA. The data set analyzed here is daily returns in Tokyo Stock Exchange (TSE) spanning from 1996 to 2006. Diagonalization of the complex correlation matrix enables us to find that a small number of the eigenvalues certainly deviate from the RMT prediction. The largest eigenvalue represents a market mode in which all of the stock prices move in a collective way. The eigenvectors of the other remaining large eigenvalues clearly show formation of stock groups as characterized by business sectors and also indicates existence of dynamical correlations between some sectors.

This paper presents a concept of an analytical engine for real time energy consumption analysis on the utility and consumer level which should enable smooth integration of new energy technologies and services in the complex environment, industrial or urban. Proposed concept aims at supporting energy utilities in optimizing energy performance on both supply and demand sides of their operations. After the validation and in order to achieve significant energy savings proposed concept should be realized in platform which should provide its users with: (i) real time energy consumption data gathered on one minute interval using smart meters, (ii) analysis and contextualization of energy consumption (including benchmarking) and (iii) knowledge sharing system for communicating results and enabling active cooperation between energy utilities and their consumers. Early detection of unnecessary energy consumption and contextualization of energy consumption should enable elimination of unnecessary leakages and should be the first visible result which would boost the confidence between partners. With the support of the proposed concept, energy utilities should become the key promoters of new energy technologies and support final consumers in their exploitation. The proposed concept should be realized as a platform with the modular architecture, allowing future expansion of user's portfolio and inventory management (new measures, technologies, different industries, urban districts and regions).

The main objective of the paper is to create context sensitive decision support services within flexible Quality Assurance (QA) of software development projects and their resulting products. The new QA process is supported by an Internet solution composed of several knowledge, context sensitive services based on open standards that is able to detect changes in the scope and requirements of an application (or changes in its development process) and provide the adequate set of assessments as a basis for an accurate measurement of the quality of the process and product at any time and allow for effective decision making within QA. The Internet Services monitor the different stages of the software development process, interoperating with the existing applications and systems to provide quantitative information about the quality of each phase (i.e. project management, requirements gathering, functional and technical design, development and testing), the project as a whole and the resulting product. They also monitor context under which the SW is developed and decisions on QA have to be made. Data obtained in real-time by the monitoring services are used in an indistinctive way by software engineers, designers, developers, testers and managers alike for different collaborative decision making. The paper is one of the first attempts to apply context sensitivity to support decision making in QA for SW development. The approach is assessed in 2 different business cases in order to validate the results under different conditions. The first business case belongs to a large software company developing large Internet projects based on Rational Unified Process methodology. The second business case belongs to a SME developing complex projects based on agile methodologies.

The paper presents a new approach to applying context awareness in order to create context-sensitive decision support services in an eco-process engineering systems setting. Optimizing the life-cycle of industrial products is subject to the options of continuously updating them by incorporating cutting edge technologies, replacing worn out pieces by new improved ones, and conceptually changing components of the product itself. While new products benefit from cutting edge technologies, the highest impact is achieved by upgrading existing products in operation, leading to the “long life eco-products” concept. The proposed approach uses context awareness to enable evaluating the performance of engineered products based on the whole life-cycle, so that product engineering teams can exploit this information to adapt the design, operation, and disposal strategies of products. One of the key assumptions of this approach is that the results of analysing contextual information and previously taken decisions can be used to reconfigure application-specific services positioned anywhere along the life-cycle of a product; by monitoring the development life-cycle and the product enriched with context from ambient intelligence, the services supporting the life-cycle can be configured in order to have faster update iterations. The presented approach will be validated against three different business cases.

Trams are reviving in many regions of the world due to its low carbon emission and better utilization of resources. However, this above-ground rail system is prone to unexpected disruptions such as vehicle breakdowns, track misalignment, power outages, and natural disasters. The fast response to disruptions is important for continuous operations of tram systems and provision of high-quality passenger service. This research is inspired by the tram system operations in cities of China, Germany and the United States with respect to their strategies for dealing with disruptions, particularly the collaboration strategy with taxi companies to provide quick replacement service, as practiced in Germany. Comparisons between the tram systems from different regions have been made to find indicators for adopting different strategies. The case study of disruption handling methods in public transport system will provide managerial insights for both academic and practical directions.

This paper considers how to optimize the specification of new built-to-order technology system which takes into user's preference for the system for a manufacturing company. This paper addresses this issue by combining cost-benefit analysis evaluating potential treatment system, and the AHP quantifying subjective judgment in evaluations. A case study is carried out to demonstrate the applicability of the proposed approach. The results show some evidence that the diagnosis procedure succeeded in quantifying user's preference for alternative systems, and that the specification was optimized successfully.

The recent rapid growth of the services industry has led to an increase in the number of service quality improvement research studies. However, analyzing service quality and determining the factors influencing consumers' perceptions of service quality are a challenging problem. The objective of this paper was to apply a data mining method to the current problems of Customer Relationship Management (CRM), analyze corporate communications systems and then identify possible data mining applications. We apply simple statistical and machine-learning techniques to study the dynamics of occurrence frequencies of events by scrutinizing user comments and corresponding customer satisfaction scores. Our analysis revealed that in the context of customer support centers, the service experience of customers strongly influences the satisfaction and service quality that the customers experienced. As a result of this study, we have identified a method of capturing the hearts of faithful customers.

This paper describes an innovation platform and focuses on a novel approach aiming to develop a robust and flexible Central Knowledge Repository (CKR) for collecting and managing innovation knowledge, from all the members of extended enterprise for new and existing product and process developments, within the platform. The proposed repository with help of external innovation tools and services will support the further development of the captured ideas and knowledge, and foster the industrial innovation. The main objective of the CKR is to stimulate the generation of innovative ideas for new and existing product/process improvement, solving company's problems by re-using the past experiences, and sow the spirit of learning organization in their DNA. In the paper an overview of the Central Knowledge Repository (CKR) is presented. It will, in addition, discuss the research methods used in gathering requirements for CKR. The paper has applied a mixed research methodology and presents the research issues and CRK requirements from the seven manufacturing SMEs. References are made to the Ideal Repository which serves to promote good ideas in the SMEs using the CKR. The overall view of the innovation platform is presented.

Cloud computing is undoubtedly one of the most widely discussed innovations of the last decade. The governance and enterprise architecture to obtain repeatable, scalable and secure business outcomes from cloud computing is still greatly undefined. In this paper we present a Cloud Computing Tipping Point (C2TP) framework that not only considers financial motivations, but also business initiatives, IT governance structures, IT operational control structures and technical architecture requirements to evaluate the benefits regarding cloud investment. This model can be leveraged by an ICT organization to evaluate the “tipping point” where the organization can make an informative decision to embrace cloud computing at the expense of on-premise hosting options. The model is a service centric framework created by mapping cloud computing attributes with industry best practices such as ValIT, Control Objectives for Information and related Technology (COBIT) and Information Technology Infrastructure Library (ITIL). This paper discusses the development of the C2TP model in detail with its findings.

This paper considers the use of a TRIZ interface to access internet embedded knowledge within the creative design process. Access to the knowledge available on the internet can massively enhance the relevance of TRIZ in the creative design process. As the internet continuously evolves it is clear that the know-how it makes available becomes ever more important. It is considered that, in the context of the creative design process, TRIZ can impose some order on what is a currently random knowledge acquisition process. The paper concludes that knowledge management process needed to configure and control the continuous addition of new (free) knowledge to the World Wide Web resources can be supported by the TRIZ process and that increasing the effectiveness of TRIZ as a creative design tool can best be accomplished in this way.

In this paper, we provide evaluation principles of decision making processes: Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) which is an extension of AHP for typical network structure, Dominant AHP, and its Concurrent Convergence Method (CCM). We also list differences and similarities among them in discussions.

We search for better strategies in a multi-agent model of the iterated prisoners' dilemma with evolvable strategies, originally proposed by Lindgren that allows elongation of genes represented by one-dimensional binary arrays, by means of three kinds of mutations: the duplication, the fission, and the point mutation, and the strong strategies are set to survive according to their performance at every generation change. Inorder to reduce comptational time, we treat each startegy as an agent and let them evolve. We also avoid fixing the number of games and let them end the iteration by means of throwing dice. The actions that the players can choose are assumed to be either cooperation (represented by C) or defection (represented by D). We conveniently use {0,1} instead of {D,C}. Each player has a strategy that determines the player's action based on the history of actions chosen by both players. Corresponding to the history of actions, represented by a binary tree of depth m, a strategy is represented by the leaves of that tree, an one-dimensional array of length 2^m. We have performed extentive simulations until many long genes are generated by mutations, and by evaluating those genes we have discovered that the genes of high scores are constructed by 3 common quartet elements, [1001], [0001], and [0101]. Furthermore, we have found that the strong genes commonly have the element [1001 0001 0001 0001] that have the following four features: (1) never defects under the cooperative situation, represented by having ‘1’ in the fourth element of the quartet such as [***1], (2) retaliates immediately if defected, represented by having ‘0’ in the first element and the third element in the quartet such as [0*0*], (3) volunteers a cooperative action after repeated defections, represented by ‘1’ in the first element of the genes, (4) exploits the benefit whenever possible, represented by having ‘0’ in the quartet such as [*0**].