Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Udhayakumar, A.; Charles, Vincent; Kumar, Mukesh

doi:10.1016/j.omega.2010.09.002

Cited by 62 publications

(25 citation statements)

References 67 publications

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“…Some observations can be located on the efficient frontier in the deterministic DEA, while some stochastic inputs and outputs are by definition allowed to be around the efficient frontier can be allowed with the aim of conceptualizing the stochastic nature of the data into the model to adapt the measurement and specification errors. Stochastic input and output variations in DEA have been studied within various input-output DEA contexts by many scholars (see e.g., Olesen andPetersen, 2015, Olesen andPetersen, 1995;Huang and Li, 1996;Cooper et al, 1996Cooper et al, , 1998Cooper et al, , 2002Cooper et al, , 2004Land et al, 1993;Morita and Seiford, 1999;Sueyoshi, 2000;Talluri et al, 2006;Olesen, 2006;Bruni et al, 2009;Wu and Lee, 2010;Tsionas and Papadakis, 2010;Udhayakumar et al, 2011). Land et al (1993) were the first to extend the chance-constrained programming (CCP) DEA proposed by Charnes and Cooper (1959), in order to compute efficiency in the presence of uncertainty in which inputs are assumed to be deterministic and outputs are jointly normally distributed.…”

Section: Selective Literaturementioning

confidence: 99%

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

et al. 2018

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Data envelopment analysis (DEA) is a well-known non-parametric technique primarily used to estimate radial efficiency under a set of mild assumptions regarding the production possibility set and the production function. The technical efficiency measure can be complemented with a consistent radial metrics for cost, revenue and profit efficiency in DEA, but only for the setting with known input and output prices. In many real applications of performance measurement, such as the evaluation of utilities, banks and supply chain operations, the input and/or output data are often stochastic and linked to exogenous random variables. It is known from standard results in stochastic programming that rankings of stochastic functions are biased if expected values are used for key parameters. In this paper, we propose economic efficiency measures for stochastic data with known input and output prices. We transform the stochastic economic efficiency models into a deterministic equivalent non-linear form that can be simplified to a deterministic programming with quadratic constraints. An application for a cost minimizing planning problem of a state government in the US is presented to illustrate the applicability of the proposed framework.

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Section: Selective Literaturementioning

confidence: 99%

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

et al. 2018

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“…To name a few, Udhayakumar et al (2011) discussed the P-model of chance constrained data envelopment analysis, Poojari and Varghese (2008) used MC simulation technique for handling chance constraints in 'the news vendor problem' and Iwamura and Liu (1996) also used the same technique in their 'feed mixture problem'. But if the constraints fail to be regular or hard to be handled it is more convenient to deal with chance constraints by stochastic simulations.…”

Section: Stochastic Simulation For Chance Constraintsmentioning

confidence: 99%

Genetic algorithm for chance constrained reliability stochastic optimisation problems

Charles

Udhayakumar

2012

IJOR

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This paper addresses the chance constrained reliability stochastic optimisation problem, in which the objective is to maximise system reliability for the given chance constraints. A problem specific stochastic simulationbased genetic algorithm (GA) is developed for finding optimal redundancy to an n-stage series system with m-chance constraints of the redundancy allocation problem. As GA is a proven robust evolutionary optimisation search technique for solving various reliability optimisation problems and the Monte Carlo (MC) simulation, which is a flexible tool for checking feasibility of chance constraints, we have effectively combined GA and MC simulation in the proposed algorithm. The effectiveness of the proposed algorithm is illustrated for a four-stage series system with two chance constraints. . Currently, he is affiliated with CENTRUM Católica, Graduate School of Business, Pontificia Universidad Católica del Perú as a Professor and Principal Researcher. His research interests are stochastic and fractional programming theory and applications, stochastic data envelopment analysis (DEA), service quality gap analysis, six sigma and financial optimisation. He has published over 40 research papers in journals and proceedings of repute. Some of his contributions can be seen in

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“…Therefore, in this paper, chance constrained programming is applied to model the economy and safety operation of the distributed network. In previous literature, many optimization algorithms have been raised to solve chance constrained programming; for example, the genetic algorithm, the ant colony algorithm, and the particle swarm optimization [18][19][20]. Compared with other algorithms, the particle swarm optimization (PSO) algorithm has the obvious advantages of easy implementation, fewer parameters, and better optimization ability.…”

Section: Introductionmentioning

confidence: 99%

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

Zhang

Gong

et al. 2018

Applied Sciences

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Abstract:With the rapid growth of electricity demands, many traditional distributed networks cannot cover their peak demands, especially in the evening. Additionally, with the interconnection of distributed electrical and thermal grids, system operational flexibility and energy efficiency can be affected as well. Therefore, by adding a portable energy system and a heat storage tank to the traditional distributed system, this paper proposes a newly defined distributed network to deal with the aforementioned problems. Simulation results show that by adding a portable energy system, fossil fuel energy consumption and daily operation cost can be reduced by 8% and 28.29%, respectively. Moreover, system peak load regulating capacity can be significantly improved. However, by introducing the portable energy system to the grid, system uncertainty can be increased to some extent. Therefore, chance constrained programming is proposed to control the system while considering system uncertainty. By applying Particle Swarm Optimization-Monte Carlo to solve the chance constrained programming, results show that power system economy and uncertainty can be compromised by selecting appropriate confidence levels α and β. It is also reported that by installing an extra heat storage tank, combined heat and power energy efficiency can be significantly improved and the installation capacity of the battery can be reduced.

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Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems

Cited by 62 publications

References 67 publications

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs

Genetic algorithm for chance constrained reliability stochastic optimisation problems

Peak Load Regulation and Cost Optimization for Microgrids by Installing a Heat Storage Tank and a Portable Energy System

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