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Updated-Absolute Expected Value Solution Approach for multistage stochastic programming�problems

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Bibliographic Details
Title:	Updated-Absolute Expected Value Solution Approach for multistage stochastic programming�problems
Authors:	Yasuhiro Shoji, Selen Cremaschi
Source:	Systems and Control Transactions. 4:1275-1280
Publisher Information:	PSE Press, 2025.
Publication Year:	2025
Description:	This paper introduces the Updated Absolute Expected Value Solution, U-AEEV, a heuristic for solving multi-stage stochastic programming (MSSP) problems with type 2 endogenous uncertainty. U-AEEV is an evolution of the Absolute Expected Value Solution, AEEV [1]. This paper aims to show how U-AEEV overcomes the drawbacks of AEEV and performs better than AEEV. To demonstrate the performance of U-AEEV, we solve 6 MSSP problems with type 2 endogenous uncertainty and compare the solutions and computational resource requirements.
Document Type:	Article
ISSN:	2818-4734
DOI:	10.69997/sct.188893
Accession Number:	edsair.doi...........762f37a5fd80c97a36fe7fd5ff1e4e31
Database:	OpenAIRE

Description
Abstract:	This paper introduces the Updated Absolute Expected Value Solution, U-AEEV, a heuristic for solving multi-stage stochastic programming (MSSP) problems with type 2 endogenous uncertainty. U-AEEV is an evolution of the Absolute Expected Value Solution, AEEV [1]. This paper aims to show how U-AEEV overcomes the drawbacks of AEEV and performs better than AEEV. To demonstrate the performance of U-AEEV, we solve 6 MSSP problems with type 2 endogenous uncertainty and compare the solutions and computational resource requirements.
ISSN:	28184734
DOI:	10.69997/sct.188893