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      A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

      1 , 1 , 2 , 3
      Mathematical Problems in Engineering
      Hindawi Limited

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          Abstract

          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.

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          Most cited references38

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          L-Shaped Linear Programs with Applications to Optimal Control and Stochastic Programming

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            Dynamic and Stochastic Models for the Allocation of Empty Containers

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              An Adaptive, Distribution-Free Algorithm for the Newsvendor Problem with Censored Demands, with Applications to Inventory and Distribution

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                Author and article information

                Contributors
                Journal
                Mathematical Problems in Engineering
                Mathematical Problems in Engineering
                Hindawi Limited
                1024-123X
                1563-5147
                August 28 2020
                August 28 2020
                : 2020
                : 1-16
                Affiliations
                [1 ]Department of Management Sciences, Sun Yat-Sen Business School, Sun Yat-Sen University, Guangzhou 510275, China
                [2 ]Department of Credit Management, School of Credit Management Guangdong University of Finance, Guangzhou 510521, China
                [3 ]School of Management, Guangdong University of Technology, Guangzhou 510006, China
                Article
                10.1155/2020/4762064
                5f33e512-aac9-46e3-9c99-b1e6b698079e
                © 2020

                http://creativecommons.org/licenses/by/4.0/

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