Robust Dual Dynamic Programming

Angelos Georghiou (), Angelos Tsoukalas () and Wolfram Wiesemann ()
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Angelos Georghiou: Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 1G5, Canada
Angelos Tsoukalas: Olayan School of Business, American University of Beirut, Beirut 1107–2020, Lebanon
Wolfram Wiesemann: Imperial College Business School, Imperial College London, London SW7 2AZ, United Kingdom

Operations Research, 2019, vol. 67, issue 3, 813-830

Abstract: In the paper “Robust Dual Dynamic Programming,” Angelos Georghiou, Angelos Tsoukalas, and Wolfram Wiesemann propose a novel solution scheme for addressing planning problems with long horizons. Such problems can be formulated as multistage robust optimization problems. The proposed method takes advantage of the decomposable nature of these problems by bounding the costs arising in the future stages through lower and upper cost-to-go functions. The proposed scheme does not require a relatively complete recourse, and it offers deterministic upper and lower bounds throughout the execution of the algorithm. The promising performance of the algorithm is shown in a stylized inventory-management problem in which the proposed algorithm achieved the optimal solution in problem instances with 100 time stages in a few minutes.

Keywords: robust optimization; multistage problems; dual dynamic programming; error bounds (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (15)

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