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QBWL Workshop
Aus der Forschung
Study on the oil-electricity retrofit of rubber-tyred gantry cranes
von QBWL Team am 12.10.2021, 11:22
In Computers & Industrial Engineering erscheint ein neuer Artikel:
Deployment and retrofit strategy for rubber-tyred gantry cranes considering carbon emissions
Driven by green port initiatives, container terminal operators have been substituting environmentally and economically inefficient diesel-powered rubber-tyred gantry cranes (RTGs) with new or retrofitted electric ones. Such an investment is closely related to operational activities, and the process of retrofitting should be carried out under the premise of satisfying the terminal yard container handling requirement. An integer programming model is proposed to help decision-makers determine the investment timing and deploy available RTGs to achieve a CO-2 emissions reduction target. Considering the distinctive characteristics of different types of RTGs, we develop a deployment plan as well as an RTGs purchase and retrofit strategy. Moreover, the decisions satisfy an operation workload requirement. A tailored Corridor Method (CM) is used to accommodate large scale real-world instances. The application is illustrated with a modestly sized container terminal yard. Results confirm that electrifying diesel-powered RTGs is effective in curbing CO-2 emissions and reducing energy costs. Our numerical experiments also highlight the importance of an appropriate emissions reduction target.
Zitation
Yi Ding, Yang Yang, Leonard Heilig, Eduardo Lalla-Ruiz, Stefan Voss,
Deployment and retrofit strategy for rubber-tyred gantry cranes considering carbon emissions,
Computers & Industrial Engineering, Volume 161,2021,107645,ISSN 0360-8352, DOI: 10.1016/j.cie.2021.107645
https://doi.org/10.1016/j.cie.2021.107645
Winter runway scheduling
von QBWL Team am 04.10.2021, 13:00
Im Bereich Innovative Applications of O.R. des European Journal of Operational Research stellen Prof. Dr. Rainer Kolisch, Maximilian Pohl und Christian Artigues einen neuen Artikel vor:
Solving the time-discrete winter runway scheduling problem: A column generation and constraint programming approach
We address the runway scheduling problem under consideration of winter operations. During snowfall, runways have to be temporarily closed in order to clear them from snow, ice and slush. We propose an integrated optimization model to simultaneously plan snow removal for multiple runways and to assign runways and take-off and landing times to aircraft. For this winter runway scheduling problem, we present a time-discrete binary model formulation using clique inequalities and an equivalent constraint programming model. To solve the winter runway scheduling problem optimally, we propose an exact solution methodology. Our start heuristic based on constraint programming generates a feasible initial start solution. We use a column generation scheme, which we initialize with a heuristic solution, to identify all variables of the binary program which are required to solve it optimally. Finally, we apply a branch-and-bound procedure to our resulting binary program. Additionally, we present an enhanced time discretization method to balance model size and solution quality. We apply our algorithm to realistic instances from a large international airport. An analysis of resulting model sizes proves the ability of our approach to significantly reduce the number of required variables and constraints of the time-discrete binary program. We also show that our method computes optimal schedules in a short amount of time and often outperforms a time-continuous formulation as well as a pure constraint programming approach.
Zitation
Maximilian Pohl, Christian Artigues, Rainer Kolisch,
Solving the time-discrete winter runway scheduling problem: A column generation and constraint programming approach,
European Journal of Operational Research, 2021,ISSN 0377-2217, DOI: 10.1016/j.ejor.2021.08.028
https://doi.org/10.1016/j.ejor.2021.08.028
Stochastic flow lines with limited material supply
von QBWL Team am 22.09.2021, 15:36
Im International Journal of Production Research erschien ein Artikel von Julia Mindlina und Prof. Dr. Horst Tempelmeier (Universität Köln):
Performance analysis and optimisation of stochastic flow lines with limited material supply
We consider stochastic flow lines with limited buffer sizes and limited material supply. In these systems, the configuration of the flow line parameters and the configuration of the material supply determine the system output. Shortages of material supply can limit the performance of the production system. We use flexible (mixed-integer) linear programming approaches to evaluate and optimise the performance of long stochastic flow lines with limited material supply in discrete and continuous time. The approaches are used to quantify the impact of material shortages on the system output. Further, they are applied to determine the minimum material levels that are required to prevent material shortages of a given flow line configuration. The results of the numerical study reveal insights on the approximation accuracy of the linear programs as well as on the dependence of optimal material levels on flow line characteristics such as the presence of bottleneck machines and the system variability. The contribution of this paper consists of both, integrated models for stochastic flow lines with limited material supply and new insights on the optimal material supply of stochastic flow lines.
Zitation
Julia Mindlina, Horst Tempelmeier
Performance analysis and optimisation of stochastic flow lines with limited material supply,
International Journal of Production Research (2021), DOI: 10.1080/00207543.2021.1954712
https://doi.org/10.1080/00207543.2021.1954712
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