Exact stochastic constraint optimisation with applications in network analysis

Latour, Anna L. D.; Babaki, Behrouz; Fokkinga, Daniël; Anastacio, Marie; Hoos, Holger H.; Nijssen, Siegfried

doi:10.1016/j.artint.2021.103650

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…In the context of constraint programming, optimization under probability, confidence or statistical constraints is becoming standard (Pachet et al 2015;Pesant 2015;Perez and Régin 2017;Perez, Rappazzo, and Gomes 2018;Hooker 2022;Latour et al 2022). At the beginning of this century stochastic constraint programming has been defined (Walsh 2002).…”

Section: Introductionmentioning

confidence: 99%

Generalized Confidence Constraints

Perez

Malalel

Glorian

et al. 2023

AAAI

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In robust optimization, finding a solution that solely respects the constraints is not enough. Usually, the uncertainty and unknown parameters of the model are represented by random variables. In such conditions, a good solution is a solution robust to most-likely assignments of these random variables. Recently, the Confidence constraint has been introduced by Mercier-Aubin et al. in order to enforce this type of robustness in constraint programming. Unfortunately, it is restricted to a conjunction of binary inequalities In this paper, we generalize the Confidence constraint to any constraint and propose an implementation based on Multi-valued Decision Diagrams (MDDs). The Confidence constraint is defined over a vector of random variables. For a given constraint C, and given a threshold, the Confidence constraint ensures that the probability for C to be satisfied by a sample of the random variables is greater than the threshold. We propose to use MDDs to represent the constraints on the random variables. MDDs are an efficient tool for representing combinatorial constraints, thanks to their exponential compression power. Here, both random and decision variables are stored in the MDD, and propagation rules are proposed for removing values of decision variables that cannot lead to robust solutions. Furthermore, for several constraints, we show that decision variables can be omitted from the MDD because lighter filtering algorithms are sufficient. This leads to gain an exponential factor in the MDD size. The experimental results obtained on a chemical deliveries problem in factories – where the chemicals consumption are uncertain – shows the efficiency of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Generalized Confidence Constraints

Perez

Malalel

Glorian

et al. 2023

AAAI

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Identification of products for campaigning with budget constraint

Kumar,

Bishnu

2024

Int. j. inf. tecnol.

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Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints

Horng

Lin

2023

Mathematics

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Simulation optimization problems with stochastic constraints are optimization problems with deterministic cost functions subject to stochastic constraints. Solving the considered problem by traditional optimization approaches is time-consuming if the search space is large. In this work, an approach integration of beluga whale optimization and ordinal optimization is presented to resolve the considered problem in a relatively short time frame. The proposed approach is composed of three levels: emulator, diversification, and intensification. Firstly, the polynomial chaos expansion is treated as an emulator to evaluate a design. Secondly, the improved beluga whale optimization is proposed to seek N candidates from the whole search space. Eventually, the advanced optimal computational effort allocation is adopted to determine a superior design from the N candidates. The proposed approach is utilized to seek the optimal number of service providers for minimizing staffing costs while delivering a specific level of care in emergency department healthcare. A practical example of an emergency department with six cases is used to verify the proposed approach. The CPU time consumes less than one minute for six cases, which demonstrates that the proposed approach can meet the requirement of real-time application. In addition, the proposed approach is compared to five heuristic methods. Empirical tests indicate the efficiency and robustness of the proposed approach.

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Exact stochastic constraint optimisation with applications in network analysis

Cited by 3 publications

References 21 publications

Generalized Confidence Constraints

Generalized Confidence Constraints

Identification of products for campaigning with budget constraint

Improved Beluga Whale Optimization for Solving the Simulation Optimization Problems with Stochastic Constraints

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