Simulation optimization: a review of algorithms and applications

Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.

doi:10.1007/s10288-014-0275-2

Cited by 185 publications

(160 citation statements)

References 190 publications

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“…Many optimization techniques that could be employed for solving the black-box optimization problem (1) have been reported in literature. The overview of various techniques is presented in [2]- [3], [16]- [18]. The algorithms can be classified into the following groups: stochastic approximation techniques (gradient-based approaches), sample path optimization, response surface methodology, deterministic search methods, random search methods, heuristics and metaheuristics.…”

Section: Related Workmentioning

confidence: 99%

“…Then, standard optimization algorithms are applied to locate the optimal solution. Response surface methodology [5], [16] is a sequential strategy based on local approximation F(x, α (k) ) of the performance f in the neighborhood of x, where the parameters α are calculated using simulations performed every k-th iteration. Next, the minimal value of F(x, α (k) ) is calculated.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks

Szynkiewicz

2018

JTIT

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Numerous practical engineering applications can be formulated as non-convex, non-smooth, multi-modal and ill-conditioned optimization problems. Classical, deterministic algorithms require an enormous computational effort, which tends to fail as the problem size and its complexity increase, which is often the case. On the other hand, stochastic, biologically-inspired techniques, designed for global optimum calculation, frequently prove successful when applied to real life computational problems. While the area of bio-inspired algorithms (BIAs) is still relatively young, it is undergoing continuous, rapid development. Selection and tuning of the appropriate optimization solver for a particular task can be challenging and requires expert knowledge of the methods to be considered. Comparing the performance of viable candidates against a defined test bed environment can help in solving such dilemmas. This paper presents the benchmark results of two biologically inspired algorithms: covariance matrix adaptation evolution strategy (CMA-ES) and two variants of particle swarm optimization (PSO). COCO (COmparing Continuous Optimizers) -a platform for systematic and sound comparisons of real-parameter global optimization solvers was used to evaluate the performance of CMA-ES and PSO methods. Particular attention was paid to the efficiency and scalability of both techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks

Szynkiewicz

2018

JTIT

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“…Exact gradient methods are no longer applicable, and metaheuristics (Unnikrishnan, Valsaraj, Damnjanovic, & Waller, ) are inappropriate given computational concerns. To address this network design problem with an expensive‐to‐evaluate objective function featuring nonconvexity, nonlinearity, and nonclosed form, simulation optimization (SO) or simulation‐based optimization has recently been investigated and advocated (Amaran, Sahinidis, Sharda, & Bury, ; Osorio & Bierlaire, ). When SO is applied to solve the TLP, existing methods can be classified into two broad categories of feedback control (Gu, Shafiei, et al., ; Simoni et al., ; Zheng et al., ; Zheng et al., ) and surrogate‐based optimization (Chen et al., ; Chen, Xiong, He, Zhu, & Zhang, ; Chen, Zhang, He, Xiong, & Zhu, ; Chow & Regan, ; Ekström, Kristoffersson, & Quttineh, ; He, Chen, Xiong, Zhu, & Zhang, ).…”

Section: Introductionmentioning

confidence: 99%

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Waller

Saberi

2019

Computer aided Civil Eng

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Toll optimization in a large‐scale dynamic traffic network is typically characterized by an expensive‐to‐evaluate objective function. In this paper, we propose two toll‐level problems (TLPs) integrated with a large‐scale simulation‐based dynamic traffic assignment model of Melbourne, Australia. The first TLP aims to control the pricing zone (PZ) through a time‐varying joint distance and delay toll such that the network fundamental diagram (NFD) of the PZ does not enter the congested regime. The second TLP is built upon the first TLP by further considering the minimization of the heterogeneity of congestion distribution in the PZ. To solve the two TLPs, a computationally efficient surrogate‐based optimization method, that is, regressing kriging with expected improvement sampling, is applied to approximate the simulation input–output mapping, which can balance well between local exploitation and global exploration. Results show that the two optimal TLP solutions reduce the average travel time in the PZ (entire network) by 29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of congestion distribution achieves higher network flows in the PZ and a lower average travel time or a larger total travel time saving in the entire network.

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“…In this paper, we propose a simulation optimisation (i.e., SO) based approach to optimise the configuration of trailer management process. 1 Recent works on simulations with infinite or, finite but having many parameter combinations, have focused on the development of simulation optimisation algorithms that combine metaheuristics with fitness approximation models [1], [2], [3], [4], [5]. These fitness approximations, also called meta-models are subsequently used to replace simulated fitness evaluations or to select which solutions should undergo a simulation procedure.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Simulation Optimisation for Trailer Management

Rijnen

Rhuggenaath

Costa

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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In many situations, simulation models are developed to handle complex real-world business optimisation problems. For example, a discrete-event simulation model is used to simulate the trailer management process in a big Fast-Moving Consumer Goods company. To address the problem of finding suitable inputs to this simulator for optimising fleet configuration, we propose a simulation optimisation approach in this paper. The simulation optimisation model combines a metaheuristic search (genetic algorithm), with an approximation model filter (feed-forward neural network) to optimise the parameter configuration of the simulation model. We introduce an ensure probability that overrules the rejection of potential solutions by the approximation model and we demonstrate its effectiveness. In addition, we evaluate the impact of the parameters of the optimisation model on its effectiveness and show the parameters such as population size, filter threshold, and mutation probability can have a significant impact on the overall optimisation performance. Moreover, we compare the proposed method with a single global approximation model approach and a random-based approach. The results show the effectiveness of our method in terms of computation time and solution quality.

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Simulation optimization: a review of algorithms and applications

Cited by 185 publications

References 190 publications

A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks

A Comparative Study of PSO and CMA-ES Algorithms on Black-box Optimization Benchmarks

Surrogate‐based toll optimization in a large‐scale heterogeneously congested network

Machine Learning based Simulation Optimisation for Trailer Management

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