Stochastic resource allocation using a predictor-based heuristic for optimization via simulation

Melouk, Sharif H.; Fontem, Belleh A.; Waymire, Emily; Hall, Shane N.

doi:10.1016/j.cor.2013.12.010

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…The focus here lies on mathematical models [13] or very specific areas in production, such as the assembly [14]. Moreover, there are different approaches in the field of simulation (discrete event as well as agent based) in the context of production [15][16][17][18][19]. Comparing the relevant literature references shows that researchbased approaches to simulation-based decision support are often limited to certain use cases or cannot be transferred to other industries.…”

Section: Literature In the Field Of Disruption Management Reaction St...mentioning

confidence: 99%

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

Luetkehoff¹,

Stich²,

Schroeter³

et al. 2019

Collaborative Networks and Digital Transformation

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Due to Digital Transformation, also called Industry 4.0 or the Industrial Internet of Things, the barrier for implementing data collecting technology on the shop floor has decreased dramatically in the past yearsleading to an increasingly growing amount of data from a multitude of IT systems in production companies worldwide. Despite that, the production controller still relies heavily on intrinsic knowledge and intuition for the management of disruptions in production. Thanks to advances in the fields of production control and artificial intelligence, potentials for the collected data for disruption management arise. However, in order to transform data into usable information and allow drawing conclusions for disruption management in production, the relevant data-objects, disturbances and alternative actions must be known. Thus, the decision-making can be supported, reducing the decision latency and increasing benefit of alternative actions. Therefore, the goal of this paper is to discuss the prerequisites necessary to perform a data based disruption management and the methodology itself, serving as an approach to allow companies to build a data basis, classify disruptions and alternative actions in order to improve decision making in the future.

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Section: Literature In the Field Of Disruption Management Reaction St...mentioning

confidence: 99%

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

Luetkehoff¹,

Stich²,

Schroeter³

et al. 2019

Collaborative Networks and Digital Transformation

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“…), random search, gradient surface methods, metamodel-based methods, sample-path optimization, statistical selection methods, etc. (Bianchi et al, 2009;Chan and Schruben, 2008;Figueira and Almada-Lobo, 2014;Melouk et al, 2014). It is interesting to note the newly introduced term of simheuristic by Juan et al 2015that designates any algorithm, which integrates simulation (in any of its variants) into a meta-heuristic driven framework for solving complex stochastic combinatorial optimization problems.…”

Section: Simulation Optimization: a Unified Macroscopic Classificationmentioning

confidence: 99%

COTS software integration for simulation optimization coupling: case of ARENA and CPLEX products

Borodin

Bourtembourg²,

Hnaien

et al. 2018

International Journal of Modelling and Simulation

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…Simulating means duplicating the features, shapes, and characteristics of the proper system. The basic idea of simulation is to use multiple devices to imitate proper systems to study and interpret the properties, behavior, and operations characteristics (Melouk et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…We can use simulations besides operational research methods, especially in financial operations used to solve stochastic problems (Chen & Raggi & Bordignon, 2006;Tompkins & D'Ecclesia, 2006;Ng, 2007;Casari, 2008;Castañeda & Reus, 2009;Vagnani, 2009;Chen & Huang, 2008;Rossi & Spazzini, 2010;Chou, 2011;Huang, 2012;Yu & Huang, 2013;Melouk et al, 2014;Acebes et al, 2015;Buchner, 2015;Nadarajah & Secomandi, 2017;Wee et al, 2020). Simulation can represent dynamic behavior (Tompkins & D'Ecclesia, 2006;Casari, 2008;Yu & Huang, 2013;Ghodrati & Zahiri, 2014;Neaime, 2015;Ferrer et al, 2016;Lai, 2018) from a system into a model, this simulation aims to evaluate a system model numerically (Yang, 2005;Sanford & Martin, 2005;Raggi & Bordignon, 2006;Casari, 2008;Chen & Huang, 2008;Rossi & Spazzini, 2010;Chen & Huang, 2012;Driessen et al, 2012;Kang et al, 2012;Yu & Huang, 2013;Ghodrati & Zahiri, 2014;Melouk et al, 2014;Denault & Simonato, 2017;Nadarajah & Secomandi, 2017;…”

Section: Introductionmentioning

confidence: 99%

“…Simulation can represent dynamic behavior (Tompkins & D'Ecclesia, 2006;Casari, 2008;Yu & Huang, 2013;Ghodrati & Zahiri, 2014;Neaime, 2015;Ferrer et al, 2016;Lai, 2018) from a system into a model, this simulation aims to evaluate a system model numerically (Yang, 2005;Sanford & Martin, 2005;Raggi & Bordignon, 2006;Casari, 2008;Chen & Huang, 2008;Rossi & Spazzini, 2010;Chen & Huang, 2012;Driessen et al, 2012;Kang et al, 2012;Yu & Huang, 2013;Ghodrati & Zahiri, 2014;Melouk et al, 2014;Denault & Simonato, 2017;Nadarajah & Secomandi, 2017;Lai, 2018;Wi et al, 2020) and data is collected to estimate the true model characteristics and the simulation approach supports sensitive analysis by allowing rapid changes to the model logic and data. Simulation is designing a logical model (Lai, 2018) mathematics (Vagnani, 2009;Rossi & Spazzini, 2010;Chou, 2011;Melouk et al, 2014;Nadarajah & Secomandi, 2017) of an actual system and experimenting with models built on computers (Goda, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

‘Monte Carlo’ Simulation Predicting on the Movement of Investments – During the Covid Pandemic in Indonesia

Azis,

Ilmi,

Hakim

et al. 2021

JDM

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The movement of the net asset value (NAV) of mutual fund products (MFP) whether high or low in the Covid-19 pandemic conditions in 2020. With the support of the Monte Carlo Simulation (MCS), this study intends to predict the rate of return on mutual fund investment (MFI) providing a choice of the average demand for return on mutual funds (MFR) that investors need. Analysis of the prediction of NAV movements and the rate of return on MFI in 55 MFP with a trial frequency of 48 times, we get an estimate of the average demand for MFR of 37 out of 100 cumulative numbers of probability distributions. The result is 77.08 percent, and an estimated average MFR in Indonesia during the 2020 Covid-19, the simulation got was IDR 421,954. The contribution resulted in a vital discovery of NAV in Indonesia in response to the economic recession affected by Covid-19..

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Stochastic resource allocation using a predictor-based heuristic for optimization via simulation

Cited by 7 publications

References 25 publications

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

Development of a Methodology for the Analysis and Evaluation of Alternative Actions in Disruption Management in Production

COTS software integration for simulation optimization coupling: case of ARENA and CPLEX products

‘Monte Carlo’ Simulation Predicting on the Movement of Investments – During the Covid Pandemic in Indonesia

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