A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem
Joint Authors
Zhou, Shaorui
Zhuo, Xiaopo
Chen, Zhiming
Tao, Yi
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-28
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhou, Shaorui& Zhuo, Xiaopo& Chen, Zhiming& Tao, Yi. 2020. A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1195406
Modern Language Association (MLA)
Zhou, Shaorui…[et al.]. A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem. Mathematical Problems in Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1195406
American Medical Association (AMA)
Zhou, Shaorui& Zhuo, Xiaopo& Chen, Zhiming& Tao, Yi. A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1195406
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1195406