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Particle swarm optimization in Machine Learning

von QBWL Team am 06.08.2021, 10:47

Ein neuer Artikel von Prof. Dr. Stefan Voß und Malek Sarhani (Universität Hamburg) ist in Annals of Mathematics and Artificial Intelligence erschienen:

Chunking and cooperation in particle swarm optimization for feature selection

Bio-inspired optimization aims at adapting observed natural behavioral patterns and social phenomena towards efficiently solving complex optimization problems, and is nowadays gaining much attention. However, researchers recently highlighted an inconsistency between the need in the field and the actual trend. Indeed, while nowadays it is important to design innovative contributions, an actual trend in bio-inspired optimization is to re-iterate the existing knowledge in a different form. The aim of this paper is to fill this gap. More precisely, we start first by highlighting new examples for this problem by considering and describing the concepts of chunking and cooperative learning. Second, by considering particle swarm optimization (PSO), we present a novel bridge between these two notions adapted to the problem of feature selection. In the experiments, we investigate the practical importance of our approach while exploring both its strength and limitations. The results indicate that the approach is mainly suitable for large datasets, and that further research is needed to improve the computational efficiency of the approach and to ensure the independence of the sub-problems defined using chunking.

Zitation

Sarhani, M., Voß, S.
Chunking and cooperation in particle swarm optimization for feature selection.
Ann Math Artif Intell (2021), DOI: 10.1007/s10472-021-09752-4

https://doi.org/10.1007/s10472-021-09752-4

Integrated planning in the automotive industry

von QBWL Team am 19.07.2021, 12:43

Ein neues Paper von Prof. Dr. Renzo Akkermann und Prof. Dr. Rainer Kolisch ist in der Zeitschrift Omega erschienen.

Strategic planning of new product introductions: Integrated planning of products and modules in the automotive industry

Timing the introduction of new products to the market is an important strategic decision in the automotive industry. For several reasons, it is also a complex decision problem. First, the use of platforms creates many interactions between different vehicles via shared modules (e.g. engines). Second, new and existing products rely on various shared resources (e.g. development resources or production capacities). Furthermore, different conflicting objectives must be considered. In this paper, we develop a mathematical linear programming model describing the decision problem based on the resource-constrained project scheduling problem. It simultaneously decides on the start of production date for vehicle models, variants, and engines as well as on the assignment of engines to the given variants. Integrating a multi-criteria approach, the model helps to analyze trade-offs between important managerial objectives related to resource utilization and fleet emission metrics. Using realistic data from a major European automotive company, we demonstrate that our model enables the efficient evaluation of various courses of action. Such capabilities are especially relevant in times of rapid technological change, such as the current transition towards electrified vehicle portfolios.

Zitation

Christopher V. Bersch, Renzo Akkerman, Rainer Kolisch,
Strategic planning of new product introductions: Integrated planning of products and modules in the automotive industry,
Omega,Volume 105,2021,102515,ISSN 0305-0483, DOI: 10.1016/j.omega.2021.102515

 

https://doi.org/10.1016/j.omega.2021.102515

Sport academy training transfers

von QBWL Team am 13.07.2021, 10:06

Im European Journal of Operational Research ist ein neuer Artikel von Prof. Dr. Rainer Kolisch und seinem Team (TUM School of Management) publiziert worden.

Consistent vehicle routing with pickup decisions - Insights from sport academy training transfers

In professional team sports, clubs compete in a highly contested market for young talents. Especially in rural areas training transfer services are common to recruit promising underage players. If the demand for training transfers exceeds the available pickup capacities, a prioritization of pickups is necessary. Additionally, the transport of underage children has the special premises of consistent driver-player assignments. To the best of our knowledge, we are the first to present an algorithmic approach to passenger transportation problems with priorities under limited capacity and consistency requirements. We propose an iterative template-based procedure to handle the trade-off between priority maximizing pickups and assignment consistency. So far, the literature has focused on the consistent service of a predefined subset of customers. With our work we focus on the consistent service of all requesting customers. Consequently, the identification of promising template players is endogenous to our approach. We discuss the effect of template players on the service of non-template players and identify drivers of consistency. In addition, we provide a new Tabu Search neighborhood structure for heterogeneous vehicle routing under consistency, ensuring efficient vehicle deployment. We highlight the benefits of the approach by comparing our results with the routing solutions of a Bundesliga soccer club.

Zitation

Christian Jost, Alexander Jungwirth, Rainer Kolisch, Sebastian Schiffels,
Consistent vehicle routing with pickup decisions - Insights from sport academy training transfers,
European Journal of Operational Research, 2021,ISSN 0377-2217, DOI: 10.1016/j.ejor.2021.06.035

 

https://doi.org/10.1016/j.ejor.2021.06.035

Flexible job shop scheduling

von QBWL Team am 29.06.2021, 09:09

In der Zeitschrift International Journal of Production Research haben Prof. Dr. Dominik Kreß (HSU) und David Müller (Universität Siegen) einen Artikel veröffentlicht:

Filter-and-fan approaches for scheduling flexible job shops under workforce constraints

This paper addresses a flexible job shop scheduling problem that takes account of workforce constraints and aims to minimise the makespan. The former constraints ensure that eligible workers that operate the machines and may be heterogeneously qualified, are assigned to the machines during the processing of operations. We develop different variants of filter-and-fan (F&F) based heuristic solution approaches that combine a local search procedure with a tree search procedure. The former procedure is used to obtain local optima, while the latter procedure generates compound transitions in order to explore larger neighbourhoods. In order to be able to adapt neighbourhood structures that have formerly shown to perform well when workforce restrictions are not considered, we decompose the problem into two components for decisions on machine allocation and sequencing and decisions on worker assignment, respectively. Based on this idea, we develop multiple definitions of neighbourhoods that are successively locked and unlocked during runtime of the F&F heuristics. In a computational study, we show that our solution approaches are competitive when compared with the use of a standard constraint programming solver and that they outperform state-of-the-art heuristic approaches on average.

Zitation

David Müller & Dominik Kress (2021)
Filter-and-fan approaches for scheduling flexible job shops under workforce constraints,
International Journal of Production Research, DOI: 10.1080/00207543.2021.1937745

https://doi.org/10.1080/00207543.2021.1937745

Hotel revenue management

von QBWL Team am 21.06.2021, 13:46

Im Journal of Heuristics erscheint ein Artikel eines Forscherteams um Prof. Dr. Erwin Pesch (Universität Siegen):

Dynamic pricing with demand disaggregation for hotel revenue management

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

Zitation

Bandalouski, A.M., Egorova, N.G., Kovalyov, M.Y. et al.,
Dynamic pricing with demand disaggregation for hotel revenue management,
J Heuristics (2021). DOI: 10.1007/s10732-021-09480-2

https://doi.org/10.1007/s10732-021-09480-2

Veröffentlicht: Juni 2021