Online optimization with gradual look-ahead

Fabian Dunke () and Stefan Nickel ()
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Fabian Dunke: Karlsruhe Institute of Technology
Stefan Nickel: Karlsruhe Institute of Technology

Operational Research, 2021, vol. 21, issue 4, No 12, 2489-2523

Abstract: Abstract We extend the setting of online optimization with look-ahead to online optimization with gradual look-ahead. While look-ahead as considered so far refers to a deterministic outlook on future data, gradual look-ahead only allows for an uncertain outlook on future data which becomes more and more precise as an input element’s release time is approached. After a discussion of related concepts, we formally introduce the class of online optimization problems with gradual look-ahead. Since the course of look-ahead information of a single input element is tied to a corresponding uncertainty set trajectory, we examine how different forecasting methods and different algorithmic approaches for dealing with gradual look-ahead can be instantiated and compared to each other with respect to the optimization output. We exemplify the introduced concepts by numerical experiments for the two applications of lot sizing and vehicle routing under gradual look-ahead.

Keywords: Online optimization; Gradual look-ahead; Lot sizing; Vehicle routing; 90-08 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12351-019-00506-z

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