Genetic algorithm for non-linear mixed integer programming problems and its applications

Takao, Yoshihiro; Gen, Mitsuo; Li, Yinxiu

doi:10.1016/0360-8352(96)00041-1

Cited by 187 publications

(95 citation statements)

References 11 publications

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“…In the first case, when the uncertainty is to be minimised, if the test right designer has no sensors in stock, the constraints becomes linear and some efforts can be done into obtaining a better solving algorithm, as seen in [24]. Nevertheless, any NMIP algorithm may be used, such as a genetic algorithms [25] or branch and bound methods as described in [26] or, more recently, in [27] or [12]. The proposed algorithm to solve the problem is as follows:…”

Section: Optimisation Methodologymentioning

confidence: 99%

Uncertainties in power computations in a turbocharger test bench

et al. 2015

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A specific study of the uncertainties of turbine power output measured in turbocharger test benches is presented using the law of uncertainty propagation and the influence of the different terms that contribute to it is shown. Then, non-linear mixed integer mathematical programming algorithms used with the turbine power uncertainty expression become an essential tool to overcome the problem of selection new sensors to improve an existing test rig or to contribute to a new one. A method of optimisation is presented for two different scenarios: first, where the maximum cost is a constraint; second where the maximum uncertainty is a constraint and the total cost is minimised. When using a large transducers database, computational efforts may be reduced by solving the relaxed non-integer problem by means of sequential quadratic programming and then probing the ceilings and floors of the parameters to get an optimum approximation with low costs. A comparison between the linear uncertainty propagation model and Monte Carlo simulations is also presented, only showing benefits of the later method when computing high order statistical moments of the turbine power output probability distribution.

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Section: Optimisation Methodologymentioning

confidence: 99%

Uncertainties in power computations in a turbocharger test bench

et al. 2015

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“…LINGO optimization solver can be used to solve such MNIP problem of smaller in size. Yokota et al (1996) presented the usefulness of GA for MNIP problems to provide optimal or near optimal solutions. Costa & Oliveira (2001) addressed that evolution strategies such as GA, SAA and ES are emerging as the best algorithm for MNIP problems.…”

Section: Ga Based Heuristics For Mv_mbomentioning

confidence: 99%

“…A metaheuristic is a framework for producing heuristics, such as Simulated Annealing (SAA), Genetic Algorithm (GA), Tabu Search (TS), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), etc. (Yokota et al, 1996;Costa & Oliveira 2001;Wu, 2001). Metaheuristics have many desirable features for becoming an excellent method: in general they are simple, easy to implement, robust and have been proven highly effective to solve hard problems.…”

Section: Heuristicsmentioning

confidence: 99%

Pricing in Supply Chain under Vendor Managed Inventory

Subramanian¹,

Jawahar²

2008

Supply Chain

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“…The well known operators for genetic algorithm, namely, crossover and mutation, as explained in the literature on genetic algorithm theory (J. H. Holland, 1992), (D. E. Goldberg, 1989)( A S. and ( T. Yokota, M. Gen, and Y. X. Li, 1996) are used in this paper, too.…”

Section: Step1 Enter Network Datamentioning

confidence: 99%

“…GA has also proved powerful in the optimization process in various power engineering applications e.g., (D. E. Goldberg, 1989)( A S. Chung, and F. Wu, 2000)( T. Yokota, M. Gen, and Y. X. Li, 1996). The genetic optimization algorithm, as applied to find optimal membership functions, observes the following steps:…”

Section: Step1 Enter Network Datamentioning

confidence: 99%