A note on analysing substancehood

Denby, David A.

doi:10.1080/00048400701572238

Cited by 17 publications

(2 citation statements)

References 3 publications

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“…Kataoka stated that the expected value optimisation is not always considered a good measure for the optimality criterion; 24 because of this, the authors set up the objective to maximisation of expected value of NPV (E function) and minimisation of its variance (V function) simultaneously, but as is clear from equation ( 17) and 20, these two objective functions have different units and dissimilar orders of magnitude; therefore, it is necessary to transform the objective functions such that they are unitless and have similar orders of magnitude. To do this, each objective can be divided by a norm of its multipliers vector; therefore, the transformed form of equations ( 25) and (26) are…”

Section: Objective Functionmentioning

confidence: 99%

“…The most popular method that has been used for optimising of open pit production scheduling is genetic algorithm. For example, Denby and Schofield 3 and Denby et al 26 used genetic algorithm to solve open pit design and production planning problem simultaneously. Denby and Schofield also extended their method to underground mines.…”

Section: Final Shape Of Open Pit Long Term Production Scheduling Modelmentioning

confidence: 99%

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Using chance constrained binary integer programming in optimising long term production scheduling for open pit mine design

Gholamnejad¹,

Osanloo²

2007

Mining Technology

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The present paper attempts to model long term production scheduling problems by chance constrained binary integer programming in a stochastic environment. This stochastic model is set up to account for ore block grade uncertainty. The probability distribution function of grade in each block is used as a stochastic input to the optimisation model. This distribution function in each block should be determined using geostatistical simulation approach. The deterministic equivalent of proposed chance constrained model is then achieved which is the form of non-linear quadratic in binary variables. A confidence level at which it is desire that the uncertain constraints holds, is specified in each scheduling period. Rather than previous risk based model, this formulation will yield schedules with high chance of achieving planned production targets while maximises the expectation of net present value and minimises the variance function simultaneously. Using this method the grade uncertainty is integrated explicitly into the optimisation process.Var(g i ) variance of g i x t i binary decision variable which is equal to one if block i is to be mined in period t and 0 otherwise a t the least probability of fulfilling the demand in period t (confidence level) 12a t acceptable risk level for not fulfilling the demands in period t VOL 116 NO 2

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Section: Objective Functionmentioning

confidence: 99%

Section: Final Shape Of Open Pit Long Term Production Scheduling Modelmentioning

confidence: 99%