A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

Juan, Ángel A.; Faulín, Javier; Grasman, Scott E.; Rabe, Markus; Figueira, Gonçalo

doi:10.1016/j.orp.2015.03.001

Cited by 320 publications

(187 citation statements)

References 66 publications

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“…Posteriorly, we applied a Monte Carlo simulation to validate the results and calculate the expected value of the indicators. A graphical representation of the methodology used in this study, based on the proposed algorithm in (Juan et al, 2015), is shown in Fig. 1.…”

Section: Methodsmentioning

confidence: 99%

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A simulation-optimization approach for the surgery scheduling problem: a case study considering stochastic surgical times

Díaz-López¹,

López-Valencia²,

González-Neira³

et al. 2018

10.5267/j.ijiec

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This work studies the scheduling of elective procedures, with stochastic durations, in surgery rooms. Given a set of rooms with limited availability and a set of procedures, it must be decided in which room and when each procedure will be performed. The problem's objectives are to maximize the use of the operating rooms and to minimize the delays in starting the scheduled surgeries. A simulation-optimization approach is proposed. First, procedures' durations are modeled as random variables and a set of test percentiles (i.e. it is assumed that all surgeries will last as many minutes as the 75th percentile of its probability density function) is selected. Subsequently, using these durations as a parameter, a greedy randomized adaptive search procedure (GRASP) is run. Consequently, as many solutions as selected test percentiles are generated. Finally, a Monte Carlo simulation is used to estimate three indicators: i) rooms utilization, ii) percentage of surgeries that had delays, and iii) average delay time of scheduled surgeries. The technique was tested by solving the elective procedures scheduling problem in a high-complexity hospital in Bogota. This hospital has 19 operating rooms and 35,000 surgeries performed annually. Currently, the scheduling process is manual. The simulation-optimization proposed approach allowed to determine the relation between utilization rate and delays in the service. As the occupation percentage increases, delay times also augment, implying a reduction of the service level. An average reduction of 5% in delay times entails a reduction between 3% and 9% of operating room occupancy.

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Section: Methodsmentioning

confidence: 99%

“…The same challenge has been addressed in other classical problems (Holm et al 2013;Juan et al, 2014;Sarin et al, 2013). Consequently, the use of hybrid simulation-optimization techniques has flourished (Juan et al, 2015).…”

Section: Introductionmentioning

confidence: 91%

A simulation-optimization approach for the surgery scheduling problem: a case study considering stochastic surgical times

Díaz-López¹,

López-Valencia²,

González-Neira³

et al. 2018

10.5267/j.ijiec

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“…Scheduling problems are combinatorial optimization problems (COPs) encountered in logistics, transportation, production, healthcare, financial, and telecommunications, among others (Juan et al, 2015). Even though the deterministic version of COPs has been widely studied, their practical application is most likely hampered due to their disregard of uncertainty factors (Sarin et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The use of hybrid simulation-optimization techniques has flourished in the last fifteen years or so, fertilized in large part by the increase in computational power, the development of advanced optimization methods and the emergence and dissemination of simulation software (Juan et al, 2015). According to (Figueira & Almada-Lobo, 2014), simulation-optimization methods can be classified considering the interaction between both techniques and the role of simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, it is often preferable to obtain an approximate solution to an accurate model rather than the optimal solution to an oversimplified model (Juan et al, 2015). Furthermore, since the metaheuristic procedure solves several times the deterministic problem, it is reasonable to expect high correlation between the performances in both scenarios, stochastic and deterministic (Juan et al, 2014;Juan et al, 2015). Therefore, selecting an appropriate metaheuristic can be made on the basis of its performance when solving the deterministic version of the combinatorial optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Duarte¹,

Cepeda²,

González-Neira³

et al. 2017

10.5267/j.uscm

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Stochastic scheduling addresses several forms of uncertainty to represent better production environments in the real world. Stochastic scheduling has applications on several areas such as logistics, transportation, production, and healthcare, among others. This paper aims to evaluate the performance of various greedy functions for a GRASP-based approach, under stochastic processing times. Since simulation is used for estimating the objective function, two simulation techniques, Monte Carlo simulation and Common Random Numbers (CRN), are used to compare the performance of different greedy (utility) functions within the GRASP. In order to validate the proposed methodology, the expected total weighted tardiness minimization for a single machine problem was taken as case study. Results showed that both, CRN and Monte Carlo, are not statistically different regarding the expected weighted tardiness results. However, CRN showed a better performance in terms of simulation replications and the confidence interval size for the difference between means. Furthermore, the statistical analysis confirmed that there is a significant difference between greedy functions.Growing Science Ltd. All rights reserved. 7

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Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

Liao

Sarker

et al. 2022

Advcd Theory and Sims

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Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs.

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A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

Cited by 320 publications

References 66 publications

A simulation-optimization approach for the surgery scheduling problem: a case study considering stochastic surgical times

A simulation-optimization approach for the surgery scheduling problem: a case study considering stochastic surgical times

Performance evaluation of a GRASP-based approach for stochastic scheduling problems

Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

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