Glomb L, Liers F, Rösel F (2021)
Publication Language: English
Publication Status: Accepted
Publication Type: Journal article
Future Publication Type: Journal article
Publication year: 2021
Publisher: Elsevier
Edited Volumes: European Journal of Operational Research
Series: unbekannt
City/Town: Amsterdam, Niederlande
Book Volume: unbekannt
Edition: unbekannt
Journal Issue: unbekannt
URI: https://www.sciencedirect.com/science/article/abs/pii/S0377221721006536
DOI: 10.1016/j.ejor.2021.07.043
Mathematical optimization problems including a time dimension abound. For example, logistics,
process optimization and production planning tasks must often be optimized for a range of time
periods. Usually, these problems incorporating time structure are very large and cannot be solved to global
optimality by modern solvers within a reasonable period of time. Therefore, the so-called rolling-horizon
approach is often adopted. This approach aims to solve the problem periodically, including additional
information from proximately following periods. In this paper, we first investigate several drawbacks of
this approach and develop an algorithm that compensates for these drawbacks both theoretically and
practically. As a result, the rolling horizon decomposition methodology is adjusted to enable large scale
optimization problems to be solved efficiently. In addition, we introduce conditions that guarantee the
quality of the solutions. We further demonstrate the applicability of the method to a variety of challenging
optimization problems. We substantiate the findings with computational studies on the lot-sizing problem
in production planning, as well as for large-scale real-world instances of the tail-assignment problem in
aircraft management. It proves possible to solve large-scale realistic tail-assignment instances efficiently,
leading to solutions that are at most a few percent away from a globally optimum solution.
APA:
Glomb, L., Liers, F., & Rösel, F. (2021). A rolling-horizon approach for multi-period optimization. European Journal of Operational Research, unbekannt(unbekannt). https://dx.doi.org/10.1016/j.ejor.2021.07.043
MLA:
Glomb, Lukas, Frauke Liers, and Florian Rösel. "A rolling-horizon approach for multi-period optimization." European Journal of Operational Research unbekannt.unbekannt (2021).
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