A decision support system for fostering smart energy efficient districts

The role of ICT is becoming prominent in tackling some of the urban societal challenges such as energy wastage and increasing carbon emissions. In this context, the concept of DAREED aims to deliver an integrated decision support system (DSS) to drive energy efficiency and low carbon activities at both a building and district level. The main aim of this paper is to present the technical concept of the Best Practices recommendation component of the DAREED system. This component seeks to compare and identify existing best practices to recommend practical actions to various stakeholders (e.g. building managers, citizens) in order to improve energy performance considering the global needs of a building. This paper also discusses the context of the three field trial sites (based in UK, Spain and Italy) in which the DAREED platform along with the best practices tool is to be tested and validated.This work evolved in the context of the project DAREED (Decision support Advisor for innovative business models and useR engagement for smart Energy Efficient Districts), www.dareed.eu, a project cofunded by the EC within FP7, Grant agreement no: 609082

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

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

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

Advanced decision support systems for managers

Managers need to make timely decisions to solve the problems in the organization or to take advantage of the opportunities. But accessing the related information and knowledge is a necessity for the purpose of decision-making. Current transactional information systems in organizations easily manage the affairs of the organization, but they do not provide the necessary ability and intelligence to take a good decision and business performance management. Here, the importance of using such systems to support management decisions and business intelligence tools are highlighted. The purpose of this paper is to analyze the decision-making process, the structure, components, and features. Also a decision support system (DSS) is described. Then, the types of decision support systems will be defined and their abilities and functions will be expressed. The following performance management and business intelligence are explained. Finally, Intelligent Decision Support System will be examined and their functions and abilities to develop a decision support tool for decision-making will be analyzed

Algorithmic simulation in system design and innovation

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-63).This thesis explores the use of genetic programming as a tool in the system design and innovation process. Digital circuits are used as a proxy for complex technological designs. Circuit construction is simulated through a computer algorithm which assembles circuit designs in an attempt to reach specified design goals. Complex designs can be obtained by repeatedly combining simpler components, often called building blocks, which were created earlier in the algorithm's progression. This process is arguably a reflection of the traditional development path of systems engineering and technological innovation. The choice of algorithm used to guide this process is crucial. This thesis considers two general types of algorithms-a blind random search method, and a genetic programming search method-with variations applied to each. The research focused on comparing these algorithms in regard to: 1) the successful creation of multiple complex designs; 2) resources utilized in achieving a design of a given complexity; and 3) the inferred time dependence of technological improvement resulting from the process. Also of interest was whether these algorithms would exhibit exponential rates of improvement of the virtual technologies being created, as is seen in real-world innovation. The starting point was the hypothesis that the genetic programming approach might be superior to the random search method. The results found however that the genetic programming algorithm did not outperform the blind random search algorithm, and in fact failed to produce the desired circuit design goals. This unexpected outcome is believed to result from the structure of the circuit design process, and from certain shortcomings in the genetic programming algorithm used. This work also examines the relationship of issues and considerations (such as cost, complexity, performance, and efficiency) faced in these virtual design realms to managerial strategy and how insights from these experiments might be applied to real-world engineering and design challenges. Algorithmic simulation approaches, including genetic programming, are found to be powerful tools, having demonstrated impressive performance in bounded domains. However, their utility to systems engineering processes remains unproven. Therefore, use of these algorithmic tools and their integration into the human creative process is discussed as a challenge and an area needing further research.by Timothy Harsh.S.M.in Engineering and Managemen

Data Mining; A Conceptual Overview

This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners

The Management of Direct Material Cost During New Product Development: A Case Study on the Application of Big Data, Machine Learning, and Target Costing

This dissertation thesis investigates the application of big data, machine learning, and the target costing approach for managing costs during new product development in the context of high product complexity and uncertainty. A longitudinal case study at a German car manufacturer is conducted to examine the topic. First, we conduct a systematic literature review, which analyzes use cases, issues, and benefits of big data and machine learning technology for the application in management accounting. Our review contributes to the literature by providing an overview about the specific aspects of both technologies that can be applied in managerial accounting. Further, we identify the specific issues and benefits of both technologies in the context management accounting. Second, we present a case study on the applicability of machine learning and big data technology for product cost estimation, focusing on the material costs of passenger cars. Our case study contributes to the literature by providing a novel approach to increase the predictive accuracy of cost estimates of subsequent product generations, we show that the predictive accuracy is significantly larger when using big data sets, and we find that machine learning can outperform cost estimates from cost experts, or produce at least comparable results, even when dealing with highly complex products. Third, we conduct an experimental study to investigate the trade-off between accuracy (predictive performance) and explainability (transparency and interpretability) of machine learning models in the context of product cost estimation. We empirically confirm the oftenimplied inverse relationship between both attributes from the perspective of cost experts. Further, we show that the relative importance of explainability to accuracy perceived by cost experts is important when selecting between alternative machine learning models. Then, we present four factors that significantly determine the perceived relative importance of explainability to accuracy. Fourth, we present a proprietary archival study to investigate the target costing approach in a complex product development context, which is characterized by product design interdependence and uncertainty about target cost difficulty. We find that target cost difficulty is related to more cost reduction performance during product development based on archival company data, and thereby complement results from earlier studies, which are based on experimental studies. Further, we demonstrate that in a complex product development context, product design interdependence and uncertainty about target cost difficulty may both limit the effectiveness of target costing

Artificial Intelligence Applied to Supply Chain Management and Logistics: Systematic Literature Review

The growing impact of automation and artificial intelligence (AI) on supply chain management and logistics is remarkable. This technological advance has the potential to significantly transform the handling and transport of goods. The implementation of these technologies has boosted efficiency, predictive capabilities and the simplification of operations. However, it has also raised critical questions about AI-based decision-making. To this end, a systematic literature review was carried out, offering a comprehensive view of this phenomenon, with a specific focus on management. The aim is to provide insights that can guide future research and decision-making in the logistics and supply chain management sectors. Both the articles in this thesis and that form chapters present detailed methodologies and transparent results, reinforcing the credibility of the research for researchers and managers. This contributes to a deeper understanding of the impact of technology on logistics and supply chain management. This research offers valuable information for both academics and professionals in the logistics sector, revealing innovative solutions and strategies made possible by automation. However, continuous development requires vigilance, adaptation, foresight and a rapid problem-solving capacity. This research not only sheds light on the current panorama, but also offers a glimpse into the future of logistics in a world where artificial intelligence is set to prevail