Minmax robustness for multi-objective optimization problems

Ehrgott, Matthias; Ide, Jonas; Schöbel, Anita

doi:10.1016/j.ejor.2014.03.013

Cited by 237 publications

(212 citation statements)

References 13 publications

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“…Calculating minmax robust efficient solution was done using the weightedsum-scalarization-method presented in (Ehrgott, et al, 2013). Here, the two objective functions are weighted with a scalar λ ∈ R 2 + and added and…”

Section: Computational Resultsmentioning

confidence: 99%

“…In this paper we follow the concept of minmax robustness which has been extended to the concept of minmax robust efficiency for multiobjective optimization problems in (Ehrgott, et al, 2013). We apply this concept to a real-world problem in the veneer cutting industry.…”

Section: Ide/tiedemannmentioning

confidence: 99%

“…This concept was originally designed for single-objective functions and has later been extended to multi-objective optimization problems in (Ehrgott, et al, 2013). Here, the following definition is presented: Definition 1.3 (Minmax Robust Efficiency).…”

Section: Minmax Robust Efficiencymentioning

confidence: 99%

“…Several algorithms for calculating minmax robust efficient solutions have been provided in (Ehrgott, et al, 2013). …”

Section: Ide/tiedemannmentioning

confidence: 99%

“…Objective functions: We re-formulate the objective function as proposed in (Ehrgott, et al, 2013) in the following way:…”

Section: Formulating the Robust Version Of The Problemmentioning

confidence: 99%

See 4 more Smart Citations

First International Conference on Resource Efficiency in Interorganizational Networks – ResEff 2013 –

Geldermann¹,

Schümann²

2013

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Section: Computational Resultsmentioning

confidence: 99%

Section: Ide/tiedemannmentioning

confidence: 99%

Section: Minmax Robust Efficiencymentioning

confidence: 99%

“…Several algorithms for calculating minmax robust efficient solutions have been provided in (Ehrgott, et al, 2013). …”

Section: Ide/tiedemannmentioning

confidence: 99%

“…Objective functions: We re-formulate the objective function as proposed in (Ehrgott, et al, 2013) in the following way:…”

Section: Formulating the Robust Version Of The Problemmentioning

confidence: 99%

See 3 more Smart Citations

First International Conference on Resource Efficiency in Interorganizational Networks – ResEff 2013 –

Geldermann¹,

Schümann²

2013

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Simulation and Multi‐criteria Optimization under Uncertain Model Parameters of a Cumene Process

Asprion

Blagov

Böttcher

et al. 2016

Chemie Ingenieur Technik

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Since experimentation is expensive with increasing computation power the significance of modeling, simulation, and optimization in process development has grown. Quite often in such models some parameters are uncertain, e.g., due to high variance in experimental data used for their estimation. Methods for investigating the impact of uncertain model parameters in the simulation and a new extension of an adaptive multi‐criteria optimization method to account for these uncertainties are described and demonstrated based on a cumene process.

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Worst Case Prediction‐Based Differential Evolution for Multi‐Noisy‐Hard‐Objective Optimization Problems

Tagawa

Harada

2017

Elect Comm in Japan

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SUMMARY A new multiobjective optimization problem in presence of noise is formulated and called multi‐noisy‐hard‐objective optimization problem (MNHOP). Since considering the worst case performance is important in many real‐world optimization problems, each solution of MNHOP is evaluated based on the upper bounds of noisy objective functions’ values predicted statistically from multiple samples. Then an Evolutionary Multiobjective Optimization Algorithm (EMOA) based on Differential Evolution is applied to MNHOP. Three sample saving techniques, namely U‐cut, C‐cut, and resampling, are proposed and introduced into the EMOA for allocating its computing budget only to promising solutions. Finally, the effects of those techniques are examined through numerical experiments.

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Minmax robustness for multi-objective optimization problems

Cited by 237 publications

References 13 publications

First International Conference on Resource Efficiency in Interorganizational Networks – ResEff 2013 –

First International Conference on Resource Efficiency in Interorganizational Networks – ResEff 2013 –

Simulation and Multi‐criteria Optimization under Uncertain Model Parameters of a Cumene Process

Worst Case Prediction‐Based Differential Evolution for Multi‐Noisy‐Hard‐Objective Optimization Problems

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