Intelligence, innovation and integrity— KD Tocher and the dawn of simulation

Hollocks, Brian

doi:10.1057/jos.2008.15

Cited by 31 publications

(10 citation statements)

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“…It is especially noteworthy that Tocher conceived and implemented an approach to combined simulation (discrete-event and continuous submodel interaction within a single model execution) well before its appearance in a US simulation language (Tocher and Splaine 1966). We are indebted to Hollocks (2008) for detailing the contributions of Tocher that led to the establishment of the K. D. Tocher Award by the Operational Research Society (United Kingdom) in 2008.…”

Section: The Formative Period (1945±1970)mentioning

confidence: 99%

“…Examples of the various perspectives and combinations can be readily found in the published histories; see Nance (1996), Nance and Sargent (2002), and Hollocks (2008). We offer this brief treatment from a very informal perspective; the objective is to highlight people, places, and events that have marked the development of discrete-event and Monte Carlo simulation.…”

Section: Introductionmentioning

confidence: 99%

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A brief history of simulation revisited

Goldsman¹,

Nance²,

Wilson³

2010

Proceedings of the 2010 Winter Simulation Conference

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In response to a request from the WSC Foundation and the WSC 2010 Program Committee, we review and slightly revise our survey of the history of simulation up to 1982, with special emphasis on some of the critical advances in the field and some of the individuals who played leading roles in those advances. Documenting the history of simulation remains a work in progress on our part, and we encourage individuals and organizations in the simulation community to bring significant historical data to our attention.

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Section: The Formative Period (1945±1970)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A brief history of simulation revisited

Goldsman¹,

Nance²,

Wilson³

2010

Proceedings of the 2010 Winter Simulation Conference

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“…adoption are unclear. One contributing factor might be that the rapid rise of simulation techniques in other disciplines (Gilbert and Troitzsch 2005: 6-9;Hollocks 2008;Heath and Hill 2010), coincided with the period when "postprocessual" critiques were introduced into the archaeological discipline, which led to a re-thinking of the epistemological foundations of the discipline and a criticism of the dependence on the scientific method. Furthermore, the process of simulation modelling differs from the common in archaeology research process consisting of data collection and analysis followed by proposing the most plausible conceptual model to explain the patterns detected in the data.…”

Section: Introductionmentioning

confidence: 99%

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Romanowska¹

2015

Human Biology

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With the current surge of simulation studies in archaeology there is a growing concern for the lack of engagement and feedback between modellers and domain specialists. To facilitate this dialogue I present a compact guide to the simulation modelling process applied to a common research topic and the focus of this special issue of Human Biology-human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. ThePre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version.conceptual phase consists of identifying research questions (step 1) and finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3) and then by filling in that framework with the modelled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterising the simulation (step 6) and running it (step 7). In the final phase the results of the simulation are analysed and re-contextualised (step 8) and the findings of the model are disseminated in publications and code repositories (step 9). Each step will be defined and characterised and then illustrated with examples of published human dispersals simulation studies. While not aiming to be a comprehensive textbookstyle guide to simulation, this overview of the process of modelling human dispersals should arm any non-modeller with enough understanding to evaluate the quality, strengths and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool.All archaeologists start modelling the moment they step out of the excavation trench. We interpret the individual finds (e.g., pots, skeletons, buildings etc.) within certain frameworks (e.g., pottery typologies, bone taxonomies, architectural types etc.) and analyse sets of finds to detect population level patterns (e.g., cultural similarities, age profiles of bone assemblages, urbanPre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version. development, etc.) with strict analytical rigour. However, the interpretations of these patterns in terms of human behaviour and causality are predominantly built using natural language, i.e. they are constructed in the form of conceptual models that hypothesise which causal mechanisms might have led from actions of individual actors (the owners of pots, users of skeletons and inhabitants of buildings) to the detected population-level patterns. These causal relationships are often described as, for example, "culture A influenced culture B," "the dispersal reached area C," or "population D outcompeted population E." Although inferences like these are made on the basis of rigorously collected and analysed data, and are commonly built upon extensive research and a good understanding of multiple strands of evidence, they nevertheless represent a thought experiment and are therefore limited ...

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“…The ACD is a timed Petri-net. Petri nets have been developed from the early work of Petri in 1962 as a means to communicate with automata [4], whereas the ACD was invented by Tocher in 1957 to solve the congestion control problem at a steel mill [5], , [6].…”

Section: Introductionmentioning

confidence: 99%

Parameterized ACD Modeling of Flexible Manufacturing Systems

Choi

Kim

Kang

et al. 2014

IEEE Trans. Automat. Sci. Eng.

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637We next derive the cdf of the failure time, i.e.,. From (7) and (9), we have (34) In (34), for the first expectation term, using Lemma 2 by setting , , , and then taking the expectation with respect to , we have (35) For the second expectation term, using Lemma 2 by setting , , , and taking the expectation with respect to , we have (36) This completes the proof. ACKNOWLEDGMENTThe authors sincerely thank the Editor, Associate Editor, and anonymous reviewers for their valuable suggestions and thorough review. Their constructive comments have considerably helped in the revision of this paper. REFERENCES[1] J. W. Sun, L. Li, and L. F. Xi, "Modified two-stage degradation model for dynamic maintenance threshold calculation considering uncertainty," A degradation path-dependent approach for remaining useful life estimation with an exact and closed-form solution," Eur. Abstract-Activity cycle diagram (ACD), which is essentially a timed Petri net, is one of the oldest formal modeling tools for discrete-event systems, and a flexible manufacturing system (FMS) is a highly automated job shop that is used widely in mechanical and electronics industries. Previous FMS modeling studies have indicated that formal modeling of real-life (or industrial) FMSs with classical ACDs (or Petri nets) is almostimpossible. This paper presents an incremental modeling procedure for building a formal simulation model of a real-life FMS with parameterized ACD (P-ACD) that was proposed recently. The incremental modeling procedure consists of job flow modeling, job routing modeling, dispatching rule modeling, and refixture operation modeling. In this paper, a P-ACD model of a real-life FMS was constructed and an FMS simulator was implemented from the proposed P-ACD model. A simulation experiment was conducted in order to demonstrate the usefulness of the FMS simulator. and maaabdullah@kau.edu.sa). This paper has supplementary downloadable material at http://ieeexplore.ieee.org, provided by the author. This includes videos for the dedicated FMS simulator and its animation. Clip 1 (demonstration.avi) shows the dedicated FMS simulator can be used to represent a class of a FMS and simulate an instance of the FMS described by the input data. Clip 2 (animation.avi) shows the animation of a FMS, which was constructed using Proof Animation of Wolverine Software and uses the trace file converted from the simulation log generated by the dedicated FMS simulator. This material is 41.4 MB in size.Note to Practitioners-FMSs are used widely in discrete part manufacturing. Recently, the authors developed P-ACD with which a formal model of a real-life industrial FMS can be built. There are three key deliverables in this paper: an FMS simulator executing the P-ACD model of the FMS, P-ACD models of a real-life FMS, and an incremental modeling procedure for building a P-ACD model of the FMS. Thus, as a practitioner, you may: 1) use the FMS simulator posted in the authors' URL as a dedicated simulator for your FMS, if the configuration of your FMS is similar to tha...

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Intelligence, innovation and integrity— KD Tocher and the dawn of simulation

Cited by 31 publications

References 19 publications

A brief history of simulation revisited

A brief history of simulation revisited

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Parameterized ACD Modeling of Flexible Manufacturing Systems

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