Speeding Up Simulation-Based Optimization of Supply Networks by Means of a Multi-Population Genetic Algorithm and Reuse of Partial Solutions

Gutenschwager, Kai; Wilhelm, Bastian; Völker, Sven

doi:10.1109/wsc.2018.8632533

Cited by 3 publications

(2 citation statements)

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“…[63] presents the application of . Extreme Learning Machine [64], which is categorized as "other ML" in spite it is a special variant of Feedforward Neural Network. In [65], an ML package "scikit-learn" is utilized for selecting a mathematical framework in order to surrogate objective the functions.…”

Section: A Optimizationmentioning

confidence: 99%

“…Among the six literature, [66] focuses on the hybrid object types of multilayer and multi-core, and [67] hybrids multigrid and multi-core. In addition, "multi-layer" accounts for 2 papers [68,69] and each of the remaining "object type" is mentioned for once in the literature pool, that include multi-processor [70], multi-goal [71], multioutput [72], multi-constrained path [73], feature-rich [74], multi-phase [75], multiple Indexes [76], multiechelon [76], multi-heuristic [77], multi-Level [78], multi-view [79], multi-class [80], multigrid [81], many-field [82], multiple access [81], multi-population [64], multiple voltage regulators [83], multiple Smartphones [84].…”

Section: A Optimizationmentioning

confidence: 99%

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A Comprehensive Review of Machine Learning in Multi-objective Optimization

Clausen

et al. 2021

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

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In the real world, it is challenging to calculate a trade-off alternative with traditional classical methods for complex non-linear systems, which always involve multiple conflicting objectives. Such complicated systems urgently desire advanced methods to conquer the multi-objective optimization problems (MOPs). As a promising AI method, the development and application of Machine Learning (ML) attract increasingly more attention from researchers. The natures of ML methods, such as parallel computation possibility, no need for any priori assumptions, etc., ensure the effectiveness and efficiency for solving MOPs. However, as we know, there is no literature related to the comprehensive review of ML in multi-objective optimization domain until now. This literature review aims to provide researchers a global view of mainstream ML methods for MOO in a general domain and a reference for applying ML methods to solve a specific type of MOPs. In this paper, the general ML mainstream methods are summarized, based on which the literature relating to ML on MOPs are retrieved in comprehensive domains. The relevant literature is categorized according to the emphasis of object types, purposes and methods, and the categorization results are finally analyzed and discussed.

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Section: A Optimizationmentioning

confidence: 99%

Section: A Optimizationmentioning

confidence: 99%

A Comprehensive Review of Machine Learning in Multi-objective Optimization

Clausen

et al. 2021

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

View full text Add to dashboard Cite

show abstract

Enhancing Digital Twins with Advances in Simulation and Artificial Intelligence: Opportunities and Challenges

Taylor,

Macal,

Matta

et al. 2023

2023 Winter Simulation Conference (WSC)

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Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks

et al. 2021

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The distribution process in business-to-business materials trading is among the most complex and in transparent ones within logistics. The highly volatile environment requires continuous adaptations by the responsible decision-makers, who face a substantial number of potential improvement actions with conflicting goals, such as simultaneously maintaining a high service level and low costs. Simulation-optimisation approaches have been proposed in this context, for example based on evolutionary algorithms. But, on real-world system dimensions, they face impractically long computation times. This paper addresses this challenge in two principal streams. On the one hand, reinforcement learning is investigated to reduce the response time of the system in a concrete decision situation. On the other hand, domain-specific information and defining equivalent solutions are exploited to support a metaheuristic algorithm. For these approaches, we have developed suitable implementations and evaluated them with subsets of real-world data. The results demonstrate that reinforcement learning exploits the idle time between decision situations to learn which decisions might be most promising, thus adding computation time but significantly reducing the response time. Using domain-specific information reduces the number of required simulation runs and guides the search for promising actions. In our experimentation, defining equivalent solutions decreased the number of required simulation runs up to 15%.

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Speeding Up Simulation-Based Optimization of Supply Networks by Means of a Multi-Population Genetic Algorithm and Reuse of Partial Solutions

Cited by 3 publications

References 0 publications

A Comprehensive Review of Machine Learning in Multi-objective Optimization

A Comprehensive Review of Machine Learning in Multi-objective Optimization

Enhancing Digital Twins with Advances in Simulation and Artificial Intelligence: Opportunities and Challenges

Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks

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