Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Business performance management models based on the digital corporation’s paradigm

Digital development of the global economy has increasingly severe implications for business, society and State. The so-called digital transformation (DX) has already turned from a scientific paradigm to reality, adjusting the development strategies of entire states, changing the face of social infrastructure and reformatting business processes. The market participants now face serious challenges: how to build their own business model and how to find their place in the digital ecosystem of the nearest future, drawing on digital technologies. That is precisely why the research and approbation of approaches to building an information model of a digital corporation are not only topical, but also very timely. The article provides an overview of several important studies in the field of DX, along with a comparative analysis of classical and digital models of corporate governance; it also shows the potential for the development of the CPM concept (Corporate performance management) considering the DX requirements and the advantages of the evolutionary planning approach. The authors present the paradigm of building information and analytical systems for digital corporation management with the use of advanced business intelligence based on dynamic intellectual models. The article describes examples of real projects on the development of support systems for decision-making in terms of marketing and financial management, including business effects from the use of similar systems. The authors have summarized project experience in the field of building a digital system of corporate management based on the Academic Competence Center of IBM "Reasonable Commerce" (located in Plekhanov Russian University of Economics) and outlined the prospects for further research.peer-reviewe

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Developing retail performance measurement and financial distress prediction systems by using credit scoring techniques

The current research develops a theoretical framework based on the ResourceAdvantage Theory of Competition (Hunt, 2000) for the selection of appropriate variables. Using a review of the literature as well as to interviews and a survey, 170 potential retail performance variables were identified as possible for inclusion in the model. To produce a relative simple model with the aim of avoiding over-fitting, a limited number of key variables or principal components were selected to predict default. Five credit-scoring techniques: Naive Bayes, Logistic Regression, Recursive Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO) were employed on a sample of 195 healthy and 51 distressed businesses from the USA market over five time periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001 and 1998-2002.Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability in the year before financial distress. Moreover, they still remained sound even five years prior to financial distress. However, it is difficult to conclude which modelling technique has the highest classification ability uniformly, since model performance varied in terms of different time scales. The analysis also showed that external environment influences do impact on default assessment for all five credit-scoring techniques, but these influences are weak. These findings indicate that the developed models are theoretically sound. There is however a need to compare their performance to other approaches.To explore the issue of the model's performance two approaches are taken. First, rankings from the study were compared with those from a standard rating system—in this case the well-established Moody's Credit Rating. It is assumed that the higher the degree of similarity between the two sets of rankings, the greater the credibility of the prediction model. The results indicated that the logistic regression model and the SMO model were most comparable with Moody's. Secondly, the model's performance was assessed by applying it to different geographical areas. The original USA model was therefore applied to a new US data set as well as the European and Japanese markets. Results indicated that all market models displayed similar discriminating ability one year prior to financial distress. However, the USA model performed relatively better than European and Japanese models five years before financial distress. This implied that a financial distress model has potentially better prediction ability when based on a single market.Following this result it was decided to explore the performance of a generic global model, since model construction is time-consuming and costly. A composite model was constructed by combining data from USA, European and Japanese markets. This composite model had sound prediction performance, even up to five years before financial distress, as the accuracy rate was above 85.15% and AUROC value was above 0.7202. Comparing with the original USA model, the composite model has similar prediction performance in terms of the accuracy rate. However, the composite model presented a worse prediction utility based on the AUROC value. A future research direction might be to include more world retailing markets in order to ensure the model's prediction utility and practical applicability

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Abordagem preditiva e adaptativa de gestão operacional aplicada à cadeia de suprimentos do varejo Omni-channel

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2020.A evolução tecnológica e a digitalização possibilitam a comercialização de produtos através de múltiplos canais e plataformas de forma integrada, propiciando a gestão de varejo omnichannel. Esse processo contínuo de integração das tecnologias digitais/virtuais aos processos gerenciais físicos dos diversos canais influencia na interação das organizações com os clientes. O comportamento de consumo dos clientes é influenciado em decorrência do aumento da conveniência, tornando, contudo, a gestão operacional das cadeias de suprimentos do varejo mais complexa. Para a gestão da cadeia de suprimentos de varejo omni-channel a complexidade reside na incerteza, oscilações no volume de vendas e incompatibilidade entre oferta e demanda. Para lidar com essa complexidade é necessária a adoção de abordagens inovadoras relacionadas a tecnologias de informação e métodos de decisão inteligentes, destacados pela indústria 4.0. No entanto, ainda faltam pesquisas sobre a conexão entre os mundos digital e real, principalmente quando se trata de cadeias de suprimentos de varejo omni-channel, que se baseiam na integração de fluxos e atividades multicanais para melhor atender ao consumidor. Neste contexto, esta pesquisa tem como objetivo propor uma abordagem preditiva e adaptativa para a gestão operacional combinando aprendizado de máquina para minimizar a incerteza, e otimização baseada em simulação para lidar com a sincronização entre oferta e demanda, aplicada à cadeia de suprimentos do varejo omni-channel. Para isso foram identificados os métodos de aprendizado de máquina, de simulação e de otimização aplicados à cadeia de suprimentos e a indústria 4.0 com o intuito de apoiar a escolha do método de redes neurais e da otimização baseada em simulação por meio do algoritmo genético. O método de redes neurais e a otimização baseada em simulação foram analisados por meio de aplicação de um caso teste, visando identificar a aplicabilidade do método levantado na literatura, na gestão operacional da cadeia de suprimentos varejista omni-channel. Em seguida, a abordagem preditiva e adaptativa é aplicada a uma empresa varejista brasileira e como resultado um modelo de gerenciamento operacional de demanda e suprimentos é proposto para a cadeia de suprimentos varejista omnichannel. Os resultados da aplicação do modelo evidenciaram uma redução dos custos da cadeia de suprimentos, do tempo de entrega dos produtos e da quantidade de pedidos provenientes da incompatibilidade de oferta-demanda. Dessa forma, a tese possibilitou a redução das incertezas proveniente da previsão de demanda, redução da falta de produtos na cadeia, e consequentemente um melhor gerenciamento da distribuição da cadeia de suprimentos.Abstract: Technological evolution and digitalization enable the commercialization of products through multiple channels and platforms in an integrated way, providing omni-channel retail management. This ongoing process of integrating digital / virtual technologies into the physical management processes of the various channels influences the interaction of organizations with customers. Customer consumption behavior is influenced by the increase in convenience, however, making the operational management of retail supply chains more complex. For the management of the omni-channel retail supply chain the complexity lies in uncertainty, fluctuations in sales volume and incompatibility between supply and demand. To address this complexity, it is necessary to adopt innovative approaches related to information technologies and intelligent decision methods, highlighted by industry 4.0. However, there is still a lack of research on the connection between the digital and real worlds, especially when it comes to omni-channel retail supply chains, which are based on the integration of multi-channel flows and activities to better serve the consumer. In this context, this research aims to propose a predictive and adaptive approach to operational management combining machine learning to minimize uncertainty, and simulation-based optimization to deal with synchronization between supply and demand, applied to the omni-channel retail supply chain. For this, the machine learning, simulation and optimization methods applied to the supply chain and industry 4.0 were identified in order to support the choice of neural networks method and simulation-based optimization through the genetic algorithm. The neural networks method and the simulationbased optimization were analyzed by applying a test case, aiming to identify the applicability of the method raised in the literature, in the operational management of the omni-channel retail supply chain. The predictive and adaptive approach is then applied to a Brazilian retail company and as a result an operational demand and supply management model is proposed for the omnichannel retail supply chain. The results of the model application showed a reduction in the supply chain costs, in the products fulfillment time and in the quantity of orders resulting from the incompatibility of supply and demand. In this way, the thesis allowed reduce uncertainties arising from demand forecasting, reduce product shortages in the chain, and thereby better manage supply chain distribution