A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem
| Autor: | Shaorui Zhou, Xiaopo Zhuo, Yi Tao, Zhiming Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Sequence 021103 operations research Article Subject Computer science General Mathematics 05 social sciences 0211 other engineering and technologies General Engineering 02 engineering and technology Function (mathematics) Engineering (General). Civil engineering (General) Stochastic programming Separable space Piecewise linear function Core (game theory) Operator (computer programming) 0502 economics and business QA1-939 TA1-2040 Algorithm Mathematics Curse of dimensionality |
| Zdroj: | Mathematical Problems in Engineering, Vol 2020 (2020) |
| ISSN: | 1563-5147 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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