Neural Networks in Multicriteria Decision Support

Hanne, Thomas

doi:10.1002/9781118522516.ch5

Cited by 1 publication

(1 citation statement)

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“…For instance, in an investment decision, knowledge about the benefit of different decisions (such returns form specific portfolios or assets) should become available at a later time. The basic idea of using (supervised) machine learning in multicriteria decision making dates back to the late 1980s and was mostly related to the idea of employing neural networks in that context as discussed in a survey [21]. A recent example of using deep neural networks for providing results according to various established multicriteria approaches is analyzed in [22].…”

Section: B Background On the Problem Of Determining Weightsmentioning

confidence: 99%

Determination of Weights for Multiobjective Combinatorial Optimization in Incident Management With an Evolutionary Algorithm

Gachnang,

Ehrenthal,

Telesko

et al. 2023

IEEE Access

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Incident management in railway operations includes dealing with complex and multiobjective planning problems with numerous constraints, usually with incomplete information and under time pressure. An incident should be resolved quickly with minor deviations from the original plans and at acceptable costs. The problem formulation usually includes multiple objectives relevant to a railway company and the employees involved in controlling operations. Still, there is little established knowledge and agreement regarding the relative importance of objectives such as expressed by weights. Due to the difficulties in assessing weights in a multiobjective context directly involving decision makers, we elaborate on the autoconfiguration of weighted fitness functions based on nine objectives used in a related Integer Linear Programming (ILP) problem. Our approach proposes an evolutionary algorithm and tests it on real-world railway incident management data. The proposed method outperforms the baseline, where weights are equally distributed. Thus, the algorithm shows the capability to learn weights in future applications based on the priorities of employees solving railway incidents which is not yet possible due to an insufficient availability of real-life data from incident management.

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Section: B Background On the Problem Of Determining Weightsmentioning

confidence: 99%