In this work we consider optimization problems that require to make interdependent offline and online decisions under uncertainty. We broadly refer to long-term strategic decisions as offline and to short-term operational decisions as online. For example, in Distributed Energy Management Systems we may need to define (offline) a daily production schedule for an industrial plant, and then manage (online) its power supply on a hour by hour basis. Traditionally offline and online phases are tackled in isolation, leading to some drawbacks: Offline decisions are taken without regard for the capabilities of the downstream online solver; while the applicability of the best approaches for online decisions (e.g. Anticipatory algorithms) is limited by the need to provide high responsiveness. Starting from a (literature-based) baseline, we define general methods for leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. All our methods have broad applicability, and provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios with both offline and online decisions and featuring continuous and discrete decisions. An extensive analysis of the experimental results shows that offline/online integration may lead to substantial benefits.

Hybrid offline/online optimization under uncertainty

De Filippo A.;Lombardi M.;Milano M.
2020

Abstract

In this work we consider optimization problems that require to make interdependent offline and online decisions under uncertainty. We broadly refer to long-term strategic decisions as offline and to short-term operational decisions as online. For example, in Distributed Energy Management Systems we may need to define (offline) a daily production schedule for an industrial plant, and then manage (online) its power supply on a hour by hour basis. Traditionally offline and online phases are tackled in isolation, leading to some drawbacks: Offline decisions are taken without regard for the capabilities of the downstream online solver; while the applicability of the best approaches for online decisions (e.g. Anticipatory algorithms) is limited by the need to provide high responsiveness. Starting from a (literature-based) baseline, we define general methods for leading to significant quality improvements, at the cost of an increased computation effort either in the offline or the online phase. All our methods have broad applicability, and provide multiple options to balance the solution quality/time trade-off, suiting a variety of practical application scenarios with both offline and online decisions and featuring continuous and discrete decisions. An extensive analysis of the experimental results shows that offline/online integration may lead to substantial benefits.
Frontiers in Artificial Intelligence and Applications
2899
2900
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
De Filippo A.; Lombardi M.; Milano M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/802160
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