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A general modeling approach to online optimization with lookahead

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  • Dunke, Fabian
  • Nickel, Stefan

Abstract

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.

Suggested Citation

  • Dunke, Fabian & Nickel, Stefan, 2016. "A general modeling approach to online optimization with lookahead," Omega, Elsevier, vol. 63(C), pages 134-153.
    • Handle: RePEc:eee:jomega:v:63:y:2016:i:c:p:134-153
    DOI: 10.1016/j.omega.2015.10.009
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    References listed on IDEAS

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    1. Patrick Jaillet & Michael R. Wagner, 2006. "Online Routing Problems: Value of Advanced Information as Improved Competitive Ratios," Transportation Science, INFORMS, vol. 40(2), pages 200-210, May.
    2. Rajeev Motwani & Vijay Saraswat & Eric Torng, 1998. "Online Scheduling with Lookahead: Multipass Assembly Lines," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 331-340, August.
    3. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Citations

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    Cited by:

    1. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    2. Farzaneh Karami & Wim Vancroonenburg & Greet Vanden Berghe, 2020. "A periodic optimization approach to dynamic pickup and delivery problems with time windows," Journal of Scheduling, Springer, vol. 23(6), pages 711-731, December.
    3. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    4. Farzaneh, Mohammad Amin & Rezapour, Shabnam & Baghaian, Atefe & Amini, M. Hadi, 2023. "An integrative framework for coordination of damage assessment, road restoration, and relief distribution in disasters," Omega, Elsevier, vol. 115(C).
    5. Feifeng Zheng & Yuhong Chen & Ming Liu & Yinfeng Xu, 2022. "Competitive analysis of online machine rental and online parallel machine scheduling problems with workload fence," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1060-1076, September.
    6. Fabian Dunke & Stefan Nickel, 2021. "Online optimization with gradual look-ahead," Operational Research, Springer, vol. 21(4), pages 2489-2523, December.
    7. Dunke, Fabian & Heckmann, Iris & Nickel, Stefan & Saldanha-da-Gama, Francisco, 2018. "Time traps in supply chains: Is optimal still good enough?," European Journal of Operational Research, Elsevier, vol. 264(3), pages 813-829.
    8. Taube, F. & Minner, S., 2018. "Resequencing mixed-model assembly lines with restoration to customer orders," Omega, Elsevier, vol. 78(C), pages 99-111.

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