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Готові списки джерел за темами / Modelling and simulation in information systems / Статті в журналах

Статті в журналах з теми "Modelling and simulation in information systems"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Ontuzheva, G. A., E. R. Bruchanova, I. N. Rudov, N. O. Pikov, and O. A. Antamoshkin. "Simulation modelling of the heterogeneous distributed information processing systems." IOP Conference Series: Materials Science and Engineering 450 (November 30, 2018): 052018. http://dx.doi.org/10.1088/1757-899x/450/5/052018.

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2

Hilmola, Olli Pekka, and Lauri Lättilä. "Simulation modelling challenge of transportation logistics systems." International Journal of Business Information Systems 14, no. 4 (2013): 429. http://dx.doi.org/10.1504/ijbis.2013.057498.

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3

Dada, Maria. "Queering Geographic Information information Systems." A Peer-Reviewed Journal About 8, no. 1 (August 15, 2019): 58–68. http://dx.doi.org/10.7146/aprja.v8i1.115415.

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What’s the relationship between GIS and the political subject? In an effort to address this question, this paper traces the movement from the map to GIS. The map is shown to be the performative utterance of the state, one that supports its national discourse and narrative. GIS, on the other hand, is shown to be a device of neoliberal governmentality, its non-representational economic practices, divided discourse and subjectivities. Despite the seemingly hopeless situation surrounding GIS, however, certain simulation and modelling practices are attempting to construct subjectivities out of economic neoliberalism’s fractured narratives. They do this by reading meaning into otherwise mathematical datasets and models. These practices could form a basis for queering GIS.

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4

Sakalauskas, Leonidas. "Modelling and simulation of business systems." European Journal of Operational Research 175, no. 3 (December 2006): 1339. http://dx.doi.org/10.1016/j.ejor.2005.02.010.

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5

Bawden, David. "Information Systems and Databases as Alternatives." Alternatives to Laboratory Animals 18, no. 1_part_1 (November 1990): 83–89. http://dx.doi.org/10.1177/026119299001800112.1.

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The influence of information technology on the development of computerised information systems which can contribute to reduction and replacement of animal experimentation is considered. Particular emphasis is placed on access to information from computer databases as an alternative in itself. The role of databases in support of modelling (molecular and statistical), simulation and risk assessment is also described.

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6

Ogunbiyi, Niyi, Artie Basukoski, and Thierry Chaussalet. "Investigating the Diffusion of Workload-Induced Stress—A Simulation Approach." Information 12, no. 1 (December 26, 2020): 11. http://dx.doi.org/10.3390/info12010011.

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Work-induced stress is widely acknowledged as harming physical and psychosocial health and has been linked with adverse outcomes such as a decrease in productivity. Recently, workplace stressors have increased due to the COVID-19 pandemic. This study aims to contribute to the literature base in a couple of areas. First, it extends the current knowledge base by utilising generative additive modelling (GAMs) to uncover the nature of the relationship between workload (a key workplace stressor) and productivity based on real-world event logs. Additionally, it uses recursive partitioning modelling to shed light on the factors that drive the relationship between these variables. Secondly, it utilises a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. Simulation is a valuable tool for exploring the effect of changes in a risk-free manner as it provides the ability to run multiple scenarios in a safe and virtual environment with a view to making recommendations to stakeholders. However, there are several recognised issues with traditional simulation approaches, such as inadequate resource modelling and the limited use of simulations for operational decision making. In this study, we propose an approach which extracts the required parameters from an event log and subsequently utilises them to initialise a workload-induced stress diffusion simulation model accurately. We also explore the effects of varying the parameters to control the spread of workload-induced stress within the network. With suitable amendments, this approach can be extended to model the spread of disease (e.g., COVID-19), diffusion of ideas, among other things, in the workplace.

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7

Kluska, Kamila. "Automatic simulation modelling of warehouses." Logforum 17, no. 1 (March 30, 2021): 59–69. http://dx.doi.org/10.17270/j.log.2021.547.

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8

Plinta, Dariusz, Martin Krajčovič, Radovan Svitek, and Patrik Grznár. "Computer aided design of logistics systems." Multidisciplinary Aspects of Production Engineering 2, no. 1 (September 1, 2019): 416–24. http://dx.doi.org/10.2478/mape-2019-0042.

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Abstract This paper presents a possibility of applying modelling and simulation in logistic processes management and design in production enterprises. New functionalities of the modelling software, especially those connected with analysing material and information flow, are already necessary for designing and evaluating complex logistic systems. The possibilities of modelling and simulation with the use of the CET Table system are illustrated by practical example.

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9

Subramanian, Shreyas Vathul. "Modelling and simulation of system-of-systems using P-systems theory." International Journal of System of Systems Engineering 8, no. 3 (2018): 231. http://dx.doi.org/10.1504/ijsse.2018.093893.

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10

Subramanian, Shreyas Vathul. "Modelling and simulation of system-of-systems using P-systems theory." International Journal of System of Systems Engineering 8, no. 3 (2018): 231. http://dx.doi.org/10.1504/ijsse.2018.10014857.

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11

Clemen, Thomas. "The use of scale information for integrating simulation models into environmental information systems." Ecological Modelling 108, no. 1-3 (May 1998): 107–13. http://dx.doi.org/10.1016/s0304-3800(98)00022-2.

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12

Arsham, Hossein. "Algorithms for sensitivity information in discrete-event systems simulation." Simulation Practice and Theory 6, no. 1 (January 1998): 1–22. http://dx.doi.org/10.1016/s0928-4869(97)00011-6.

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13

Lektauers, Arnis. "Multi-Agent Geosimulation of Urban Dynamics within the V-Devs Framework." Scientific Journal of Riga Technical University. Computer Sciences 39, no. 1 (January 1, 2009): 52–58. http://dx.doi.org/10.2478/v10143-010-0007-9.

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Multi-Agent Geosimulation of Urban Dynamics within the V-Devs FrameworkIn recent years, a number of research efforts have focused on presenting solutions for modelling and simulation of urban systems using automata-based models allowing a transition from large scale, aggregate spatial representation in a static equilibrium to much finer scale disaggregate forms where dynamic processes are the primary focus of the simulation. In this paper a conceptual framework for integrating of agent-based models with cellular automata, geographic automata systems and geographic information systems in the context of modelling and simulation of urban systems is proposed. The proposed framework uses Visual Discrete Event System Specification (V-DEVS) that provides integrated modelling and simulation in a virtual interactive simulation environment, in a such way solving the existing problem that multi-agent systems and geographic information systems separately do not provide truly integrative modelling and simulation capabilities.

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14

Wang, Q., C. R. Chatwin, A. Geha, R. C. D. Young, and D. M. Budgett. "Modelling and Simulation of Integrated Operations and Information Systems in Manufacturing." International Journal of Advanced Manufacturing Technology 19, no. 2 (February 1, 2002): 142–50. http://dx.doi.org/10.1007/s001700200007.

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15

Burrage, K., J. Hancock, A. Leier, and D. V. Nicolau. "Modelling and simulation techniques for membrane biology." Briefings in Bioinformatics 8, no. 4 (March 29, 2007): 234–44. http://dx.doi.org/10.1093/bib/bbm033.

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16

Paranjape, Raman, Simerjit Gill, Sara Ghoreishi Nejad, and Robert Martens. "An agent-based simulation system for modelling a diabetic patient." International Journal of Intelligent Information and Database Systems 4, no. 3 (2010): 264. http://dx.doi.org/10.1504/ijiids.2010.034083.

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17

Evans, Neil, Helen Treharne, Régine Laleau, and Marc Frappier. "Applying CSP || B to information systems." Software & Systems Modeling 7, no. 1 (April 11, 2007): 85–102. http://dx.doi.org/10.1007/s10270-007-0048-x.

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18

Perzyk, M., A. Kochański, P. Mazurek, and K. Karczewski. "Selected Principles of Feeding Systems Design: Simulation vs Industrial Experience." Archives of Foundry Engineering 14, no. 4 (December 1, 2014): 77–82. http://dx.doi.org/10.2478/afe-2014-0090.

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Abstract Simulation software dedicated for design of casting processes is usually tested and calibrated by comparisons of shrinkage defects distribution predicted by the modelling with that observed in real castings produced in a given foundry. However, a large amount of expertise obtained from different foundries, including especially made experiments, is available from literature, in the form of recommendations for design of the rigging systems. This kind of information can be also used for assessment of the simulation predictions. In the present work two parameters used in the design of feeding systems are considered: feeding ranges in horizontal and vertical plates as well as efficiency (yield) of feeders of various shapes. The simulation tests were conducted using especially designed steel and aluminium castings with risers and a commercial FDM based software. It was found that the simulations cannot predict appearance of shrinkage porosity in horizontal and vertical plates of even cross-sections which would mean, that the feeding ranges are practically unlimited. The yield of all types of feeders obtained from the simulations appeared to be much higher than that reported in the literature. It can be concluded that the feeding flow modelling included in the tested software does not reflect phenomena responsible for the feeding processes in real castings properly. Further tests, with different types of software and more fundamental studies on the feeding process modelling would be desirable.

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19

Katina, Polinpapilinho F., Andreas Tolk, Charles B. Keating, and Keith F. Joiner. "Modelling and simulation in complex system governance." International Journal of System of Systems Engineering 10, no. 3 (2020): 262. http://dx.doi.org/10.1504/ijsse.2020.109739.

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20

Chuffart, Florent, Nicolas Dumoulin, Thierry Faure, and Guillaume Deffuant. "SimExplorer." International Journal of Agricultural and Environmental Information Systems 1, no. 1 (January 2010): 55–68. http://dx.doi.org/10.4018/jaeis.2010101304.

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This article describes Simexplorer, a computer framework for managing simulation experiments and, to some extent, the scientific quality of the modelling process. An information system, included in the framework, insures the traceability of the experiments and their reproducibility and thus contributes to the modelling process quality management. Moreover, this information system provides facilities for sharing and exchanging components of experiment scenarios. The authors illustrate the use of the framework on a simple example of modelling process.

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21

Bonomolo, Marina, Simone Di Lisi, and Giuliana Leone. "Building Information Modelling and Energy Simulation for Architecture Design." Applied Sciences 11, no. 5 (March 4, 2021): 2252. http://dx.doi.org/10.3390/app11052252.

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Over the years, building information modelling (BIM) has undergone a significant increase, both in terms of functions and use. This tool can almost completely manage the entire process of design, construction, and management of a building internally. However, it is not able to fully integrate the functions and especially the information needed to conduct a complex energy analysis. Indeed, even if the energy analysis has been integrated into the BIM environment, it still fails to make the most of all the potential offered by building information modelling. The main goals of this study are the analysis of the interaction between BIM and energy simulation, through a review of the main existing commercial tools (available and user-friendly), and the identification and the application of a methodology in a BIM environment by using Graphisoft’s BIM software Archicad and the plug-in for dynamic energy simulation EcoDesigner STAR. The application on a case study gave the possibility to explore the advantages and the limits of these commercial tools and, consequently, to provide some possible improvements. The results of the analysis, satisfactory from a quantitative and qualitative point of view, validated the methodology proposed in this study and highlighted some limitations of the tools used, in particular for the aspects concerning the personalization of heating systems.

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22

Moskon, Miha, and Miha Mraz. "MODELLING AND ANALYSING THE INFORMATION PROCESSING CAPABILITIES OF SIMPLE BIOLOGICAL SYSTEMS." Mathematical Modelling and Analysis 17, no. 4 (September 1, 2012): 467–84. http://dx.doi.org/10.3846/13926292.2012.706651.

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Biological systems that present basic logic primitives for information processing have already been realized. Models for simulating their dynamics have also been implemented. However there is a lack of metrics that would objectively evaluate the information processing capabilities of these primitives and possibilities of their interconnectivity. With the introduction of such processing and performance descriptive quantities complex biological systems capable of information processing could be built more straightforwardly. That would bring us closer to the realization of a biological computer.

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23

Sandblad, B., and H. P. Meinzer. "Modelling and Simulation of Complex Control Structures in Cell Biology." Methods of Information in Medicine 31, no. 01 (1992): 36–43. http://dx.doi.org/10.1055/s-0038-1634855.

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Abstract:Biological and biomedical tissues and organs represent very complex and highly dynamic systems. The problem to model such complex systems is twofold. First, it is necessary to identify and describe the biological properties and the connecting control mechanisms of the studied system and its subparts. Here, biological knowledge is often missing. Secondly, we are confronted with the problem to formulate the model of the control structure in a form that allows model analysis. Here, a normal mathematical formalism is often inadequate. To solve these modelling problems and to develop methods for building models of complex control structures that will enable the formulation of dynamic systems in biology, a framework for building such models is presented. The model framework is here mainly used for studies of dynamic cell structures, but the possible application areas are more general. An example is given for modelling cell structures of epithelial tissues in the intestine.

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24

Skipper, N. T. "Computer simulation of aqueous pore fluids in 2:1 clay minerals." Mineralogical Magazine 62, no. 5 (October 1998): 657–67. http://dx.doi.org/10.1180/002646198548043.

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AbstractMonte Carlo and molecular dynamics computer simulations are now able to provide detailed information concerning the structure, dynamics, and thermodynamics of pore fluids in 2:1 clays. This article will discuss interparticle interaction potentials currently available for atomistic simulations of clay-water systems, and will describe how computational techniques can be applied to modelling of clay systems. Some recent simulation studies of 2:1 clay hydration will then be reviewed. Comparison with experimental data promotes confidence in the molecular models and simulation techniques, and points to exciting future prospects.

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25

Gabalin, A. V. "Teaching modelling of distributed information and control systems to students using the method of simulation modelling." Open Education, no. 3 (January 1, 2017): 13–21. http://dx.doi.org/10.21686/1818-4243-2017-3-13-21.

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26

Paul, Ray J., and Francis Neelamkavil. "Computer Simulation and Modelling." Journal of the Operational Research Society 38, no. 11 (November 1987): 1092. http://dx.doi.org/10.2307/2582236.

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27

Dave, U., and L. Oakshott. "Business Modelling and Simulation." Journal of the Operational Research Society 49, no. 2 (February 1998): 180. http://dx.doi.org/10.2307/3009986.

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28

Paul, Ray J. "Computer Simulation and Modelling." Journal of the Operational Research Society 38, no. 11 (November 1987): 1092–93. http://dx.doi.org/10.1057/jors.1987.181.

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29

Dave, U. "Business Modelling and Simulation." Journal of the Operational Research Society 49, no. 2 (February 1998): 180–81. http://dx.doi.org/10.1057/palgrave.jors.2600005.

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30

Crookes, J. G. "Computer simulation and modelling." European Journal of Operational Research 36, no. 3 (September 1988): 422–23. http://dx.doi.org/10.1016/0377-2217(88)90141-5.

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31

Pidd, M. "Simulation modelling with pascal." European Journal of Operational Research 47, no. 3 (August 1990): 397–98. http://dx.doi.org/10.1016/0377-2217(90)90228-4.

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32

Tang, Zhao-Jun, Ting-Zhu Huang, and Jin-Liang Shao. "Consensus of Multiagent Systems with Sampled Information and Noisy Measurements." Discrete Dynamics in Nature and Society 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/206408.

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Анотація:

We consider consensus problems of first-order multiagent systems with sampled information and noisy measurements. A distributed stochastic approximation type algorithm is employed to attenuate the measurement noises. We provide conditions under which almost sure strong consensus is guaranteed for fixed and switching directed network topologies. Simulation results are provided to illustrate the theoretical results.

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33

Donzelli, Paolo, and Giuseppe Iazeolla. "Hybrid simulation modelling of the software process." Journal of Systems and Software 59, no. 3 (December 2001): 227–35. http://dx.doi.org/10.1016/s0164-1212(01)00064-4.

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34

Gurjanov, A. V., D. A. Zakoldaev, I. O. Zharinov, and O. O. Zharinov. "The Industry 4.0 technological and information processes cyber-modelling." Journal of Physics: Conference Series 2094, no. 4 (November 1, 2021): 042062. http://dx.doi.org/10.1088/1742-6596/2094/4/042062.

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Abstract Cyber-modelling is the information models simulation process describing in a mathematical and formal logic languages (phenomenon models) how cyber-physical systems interaction mechanisms are united with different control laws and parameter values. The equation complexity represented in different levels of cyber-physical production systems hierarchy and non-equations of algebra, logic, end-subtraction, vector and matrices form in a discreet and uninterrupted times are defined with an aggregated number in the industrial automatics element control loop. The cyber-modelling is done for statistic and dynamic processes and equipment states being monitored in a virtual environment fixating actual in a time interval technological data. The cyber-modelling is done with integrated calculation equipment systems with parallel physical production processes of item manufacturing. The model time faster than physical processes let prognosticate the corrections modifying control signals and phase variables of cyber-physical systems united in an assembly conveyor. The cyber-modelling advantage is an expanded number of cycles to optimize the technological processes, which are calculated with integrated calculation systems using consecutive approximation method. They describe the cyber-modelling technology and propose the information models based on phenomenon cyber-physical production processes descriptions with general control theory terms, calculations and connection for hierarchy controlling structures.

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35

Wang, Yingxu, and Shushma Patel. "Guest editorial: On Modeling Object-Oriented Information Systems." Software and Systems Modeling -1, no. 1 (July 2, 2003): 1. http://dx.doi.org/10.1007/s10270-004-0053-2.

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36

Soshko, Oksana. "Model-Based Supply Chain Management." Scientific Journal of Riga Technical University. Computer Sciences 45, no. 1 (January 1, 2011): 116–22. http://dx.doi.org/10.2478/v10143-011-0052-z.

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Model-Based Supply Chain Management The paper is focused on application of modelling and simulation approaches in supply chain management. First, essentials of multi echelon supply chain management are discussed including the functionality of supply chain information systems. Then, a variety of modelling and simulation approaches are analyzed in the context of supply chain management, for example, simulation, stochastic programming, business process modelling and reference model. Finally, some illustrative examples are provided on application of simulation and modelling approaches to supply chain management.

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37

Bajgoric, Nijaz. "Organizational Systems Integration: Management Information Systems Perspective." Concurrent Engineering 5, no. 2 (June 1997): 113–21. http://dx.doi.org/10.1177/1063293x9700500203.

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38

Schopfer, G., A. Yang, L. von Wedel, and W. Marquardt. "CHEOPS: A tool-integration platform for chemical process modelling and simulation." International Journal on Software Tools for Technology Transfer 6, no. 3 (August 2004): 186–202. http://dx.doi.org/10.1007/s10009-004-0157-6.

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39

Markus, Andras, Mate Biro, Gabor Kecskemeti, and Attila Kertesz. "Actuator behaviour modelling in IoT-Fog-Cloud simulation." PeerJ Computer Science 7 (July 30, 2021): e651. http://dx.doi.org/10.7717/peerj-cs.651.

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The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.

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40

Sakas, Damianos, Dimitris Vlachos, and Dimitris Nasiopoulos. "Modelling strategic management for the development of competitive advantage, based on technology." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 187–209. http://dx.doi.org/10.1108/jsit-01-2014-0005.

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Анотація:

Purpose – The purpose of this research work is to find a methodology for the strategic development of competitive advantage for information technology (IT) companies (Mezger and Violani, 2011). The ultimate aim of this project is to develop a methodological approach on this issue, based on dynamic simulation models (DSM) (Wirahadikusumah and Abraham, 2003). With the aid of DSM, senior managements of organizations will have the opportunity to make decisions of assured success. This success shall be guaranteed by the realization of entrepreneurial activity in a safe and inexpensive computing environment before actual investment. Design/methodology/approach – This paper highlights the advantages of the dynamic modelling of systems aiming at developing competitive advantage for IT companies (Ordóñez de Pablos, 2006). In this research, we have used the science of design and the research methodology for testing the concept of modelling as well as the process of modelling. The models have been completed through a series of alternations and iterations in the design, development, simulation, testing and evaluation. Findings – This paper examines the interface among several dimensions for the development of dynamic models. The validity and usefulness of those models in the process of decision-making has been confirmed by the usage of dynamic models in various sectors. Originality/value – This paper applies the system and the concepts of dynamic modelling, which are pioneering elements as to their nature and evolution. Although the sector, where the modelling was applied, is an IT company, the concepts and principles investigated, developed and validated can be applied to most enterprises.

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41

Su, Xing, Weihua Li, and Quan Bai. "Editorial Special Issue on AI-enabled System Simulation and Modelling." Journal of Systems Science and Systems Engineering 30, no. 4 (August 2021): 383–85. http://dx.doi.org/10.1007/s11518-021-5491-7.

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42

Barrera, Renato, Abraham Alcántara, Carlos Alegría, Ana L. Ávila, and David Esparza. "A Mediator for Biospatial Information Systems." International Journal of Decision Support System Technology 1, no. 2 (April 2009): 20–35. http://dx.doi.org/10.4018/jdsst.2009040102.

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43

Jánošíková, Michaela, Jozef Ristvej, and Maroš Lacinák. "POSSIBLE USE OF A GEOGRAPHICAL INFORMATION SYSTEM WITHIN CRISIS MANAGEMENT SIMULATIONS." CBU International Conference Proceedings 6 (September 24, 2018): 216–20. http://dx.doi.org/10.12955/cbup.v6.1159.

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In this paper we deal with Global Information Systems (GIS) used within simulations of crisis management through the phases of the Crisis Management Cycle. In the first part of this paper we point out the short description of the phases and the possible use of GIS within these phases and in simulations within crisis management. In the second part we introduce the GIS in the Slovak Republic with focuses on use in crisis management. The third part is giving possibilities of the interconnections among simulation technologies and GIS within crisis management with a focus on the newly built Laboratory of Modelling and Simulation of Crisis Phenomena in Transport and in different environments. Within conclusions we would like to point out the possibilities for further discussions and directions within this interesting and important topic.

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Hüllermeier, Eyke. "An Approach to Modelling and Simulation of Uncertain Dynamical Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, no. 02 (April 1997): 117–37. http://dx.doi.org/10.1142/s0218488597000117.

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Coping with uncertainty in dynamical systems has recently received some attention in artificial intelligence (AI), particularly in the fields of qualitative and model-based reasoning. In this paper, we propose an approach to modelling and simulation of uncertain dynamics which is based on the following ideas: We consider (linguistic) descriptions of uncertain functional relationships characterizing the behavior of some dynamical system. Based on a certain interpretation of such rule-based models, we derive a fuzzy function [Formula: see text]. It will be shown that all (reasonable) fuzzy functions can be approximated to any degree of accuracy in this way. The function [Formula: see text] is then used as the "fuzzy" right hand side of a set of differential equations, which leads us to consider fuzzy initial value problems. We are going to propose an interpretation of such problems. Moreover, several aspects of simulation methods for characterizing the set of all system behaviors compatible with this interpretation will be discussed.

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45

Gilbert, D., H. Fuss, X. Gu, R. Orton, S. Robinson, V. Vyshemirsky, M. J. Kurth, C. S. Downes, and W. Dubitzky. "Computational methodologies for modelling, analysis and simulation of signalling networks." Briefings in Bioinformatics 7, no. 4 (September 26, 2006): 339–53. http://dx.doi.org/10.1093/bib/bbl043.

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46

Paul, Ray J., and Sew Tee Chew. "Simulation Modelling Using an Interactive Simulation Program Generator." Journal of the Operational Research Society 38, no. 8 (August 1987): 735. http://dx.doi.org/10.2307/2582845.

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47

Paul, Ray J., and Sew Tee Chew. "Simulation Modelling Using an Interactive Simulation Program Generator." Journal of the Operational Research Society 38, no. 8 (August 1987): 735–52. http://dx.doi.org/10.1057/jors.1987.122.

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48

Gien, Denis, and Sylvie Jacqmart. "Design and simulation of manufacturing systems facing imperfectly defined information." Simulation Modelling Practice and Theory 13, no. 6 (September 2005): 465–85. http://dx.doi.org/10.1016/j.simpat.2005.01.006.

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He, Chansheng. "Integration of geographic information systems and simulation model for watershed management." Environmental Modelling & Software 18, no. 8-9 (October 2003): 809–13. http://dx.doi.org/10.1016/s1364-8152(03)00080-x.

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Luh, Cheng-Jye. "Modelling and simulation in an object-oriented environment." Information and Software Technology 36, no. 6 (June 1994): 343–52. http://dx.doi.org/10.1016/0950-5849(94)90034-5.

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