A new separable piecewise linear learning algorithm for the stochastic empty container repositioning problem
type: Item
instance of: scholarly article
title: A new separable piecewise linear learning algorithm for the stochastic empty container repositioning problem
zbMATH Open document ID: 1459.90234
DOI: 10.1155/2020/4762064
author: Shaorui Zhou
author: Xiaopo Zhuo
author: Zhiming Chen
author: Yi Tao
published in: Mathematical Problems in Engineering
publication date: 2020-10-12T00:00:00Z
review text: Summary: A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.
Mathematics Subject Classification ID: 90C90
Mathematics Subject Classification ID: 90C15
Mathematics Subject Classification ID: 90B80
zbMATH DE Number: 7259614
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/4762064
OpenAlex ID: W3082505654
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description: scientific article; zbMATH DE number 7259614