Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods

Lecchini‐Visintini, Andrea; Lygeros, John; Maciejowski, Jan M.

doi:10.1109/tac.2010.2078170

Cited by 24 publications

(12 citation statements)

References 44 publications

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“…Simulation-based optimization algorithms for Markov-chains help to maximize the average reward of a parameterized Markov-chain [67,68]. Markov-chain models can be combined with Monte Carlo algorithms to solve global stochastic optimization problems defined over continuous domains [69]. An important scientific issue is the control of Markovian processes.…”

Section: Markov-chain Modellingmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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Manufacturing and service processes are composed of several elements: Technical, financial, logistics, information and human resources. Staff deployment and staffing is an essential problem in the human resource management domain because the structure of employees would be continuously in an optimal relationship to the jobs to be performed. This paper proposes a conceptual model for the analysis of human resource deployment processes. After a systematic literature review, it was found that algorithms are important tools for the design and control of human resource problems since a wide range of models determines an optimization problem. According to that, the main focus of this research is the modelling and analysis of human resource deployment processes of manufacturing companies using Markov-chain mathematics, also taking into account the absorbing phenomena of employees’ promotion. The main contribution of this article includes the model framework of Markov-chain simulation of a human resource deployment problem; the mathematical description of different human resource deployment strategies with subdiagonal and superdiagonal promotion matrices; the computational results of the described model with different datasets and scenarios. In the case of a given human resource strategy, the Markovian human resource deployment process of a company was analyzed. The analyzed model was the HR deployment of assembly line operators in a multinational company, including six levels of promotion. The results of the scenario analysis show that promotion and recruitment rates have a great impact on the future employees’ structure.

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Section: Markov-chain Modellingmentioning

confidence: 99%

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

2018

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“…Many studies are devoted to the models and methods of detection based on the use of Markov chains [20][21][22][23]. The typical disadvantage of most SRCA that are proposed in these studies is lack of an opportunity to quickly replenish the repository of cyberattacks' patterns, as they almost always use only one methodology of recognition.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

A model developed for teaching an adaptive system of recognising cyberattacks among non-uniform queries in information systems

Lakhno

Mohylnyi

Donchenko

et al. 2016

EEJET

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“…In our case, it is exponential of the form π ∞ (x) = 1 M e −βV (x) [14], where M is the (unknown) normalization constant. For the first part, recently, a result on finite-time guarantees for SA optimization over continuous domains was obtained in [28]. Informally, given a desired precision of the optimization, it provides a number of samples after which we are guaranteed that the minimum so far is within the desired precision of the global minimum.…”

Section: Practical Considerationsmentioning

confidence: 99%

Convergence proofs for Simulated Annealing falsification of safety properties

Abbas

Fainekos

2012

2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Abstract-The problem of falsifying temporal logic properties of hybrid automata can be posed as a minimization problem by utilizing quantitative semantics for temporal logics. Previous work has used a variation of Simulated Annealing (SA) to solve the problem. While SA is known to converge to the global minimum of a continuous objective function over a closed and bounded search space, or when the search space is discrete, there do not exist convergence proofs for the cases addressed in that previous work. Namely, when the objective function is discontinuous, and when the objective is a vector-valued function. In this paper, we derive conditions and we prove convergence of SA to a global minimum in both scenarios. We also consider matters affecting the practical performance of SA.

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Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods

Cited by 24 publications

References 44 publications

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

Markov-Chain Simulation-Based Analysis of Human Resource Structure: How Staff Deployment and Staffing Affect Sustainable Human Resource Strategy

A model developed for teaching an adaptive system of recognising cyberattacks among non-uniform queries in information systems

Convergence proofs for Simulated Annealing falsification of safety properties

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