29,112 research outputs found

    Collaborative information systems and business process design using simulation

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    The Information Systems (IS) community promotes the idea that IS analyst should have a clear understanding of the way the organization operates before attempting to propose an IS solution. It is argued that to take a complete advantage of the underlying Information Technology (IT), organizations should first identify any process flaw and then propose a suitable IT solution. Similarly, many process design approaches claim that Business Process (BP) design should be done considering the advantages provided and the limitations imposed by the underlying (IT). Despite this fact research in these domains provides little indication of which mechanisms or tools can help BP and IS analyst to understand the complex relationships amongst these two areas. This paper describes the insights gained during a UK funded research project, namely ASSESS-IT, that aimed to depict the dynamic relationships between IT and BP using simulation. One of the major limitations of the ASSESS-IT project is that it looked at relationship between BP and IT as a three layered structure, namely BP, IS and Computer Networks (CN), and did not explore in detail the relationships between BP and IS alone. This paper uses the outcomes derived from this project and suggests that, is some cases, the relationship between BP and IT could be analyzed by looking at the relationship between BP and IS alone. It then proposes an alternative simulation framework, namely BPISS, that provides the guideline to develop simulation models that portray BP and IS behavior performance measurements, offering in this way an alternative mechanism that can help BP and IS analyst to understand in more detail the dynamic interactions between BP and IS domains

    Panel on future challenges in modeling methodology

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    This panel paper presents the views of six researchers and practitioners of simulation modeling. Collectively we attempt to address a range of key future challenges to modeling methodology. It is hoped that the views of this paper, and the presentations made by the panelists at the 2004 Winter Simulation Conference will raise awareness and stimulate further discussion on the future of modeling methodology in areas such as modeling problems in business applications, human factors and geographically dispersed networks; rapid model development and maintenance; legacy modeling approaches; markup languages; virtual interactive process design and simulation; standards; and Grid computing

    Human-Machine Collaborative Optimization via Apprenticeship Scheduling

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    Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

    Overview on agent-based social modelling and the use of formal languages

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    Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

    Development Process for Multi-Disciplinary Embedded Control Systems

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    This report contains the progress report for the qualification exam for Industrial PhD student Sune Wolff. Initial work on describing a development process for multi-disciplinary systems using collaborative modelling and co-simulation is described
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