Effectiveness of an empty container repositioning policy with flexible destination ports

Song, Dong‐Ping; Dong, Jingxin

doi:10.1016/j.tranpol.2010.06.004

Cited by 64 publications

(38 citation statements)

References 25 publications

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“…Studies on network design (e.g., Fagerholt, 1999Fagerholt, , 2004Sambracos et al, 2004;Alvarez, 2009;Karlaftis et al, 2009;Brouer et al, 2011;Jepsen et al, 2011;Reinhardt and Pisinger, 2012) and empty container repositioning (e.g., Song and Dong, 2011;Song and Xu, 2012) have also examined the fixed container demand in liner shipping. Bell et al (2011), Wang andMeng (2012c) and Qi and Song (2012) have incorporated the uncertainty in the liner service schedules but not investigated the demand uncertainty.…”

Section: Risk Analysis In Shipping Industrymentioning

confidence: 99%

Risk management in liner ship fleet deployment: A joint chance constrained programming model

Wang

Meng

Wang

et al. 2013

Transportation Research Part E: Logistics and Transportation Re

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This paper provides a tangible methodology to deal with the liner ship fleet deployment problem aiming at minimizing the total cost while maintaining a service level under uncertain container demand. The problem is first formulated as a joint chance constrained programming model, and the sample average approximation method and mixed-integer programming are used to deal with it. Finally, a numerical example of a liner shipping network is carried out to verify the applicability of the proposed model and solution algorithm. It is found that the service level has significant effect on the total cost. AbstractThe liner ship fleet deployment problem with uncertain container demand is one of the risk management issues in liner shipping industry. This paper provides a methodology to deal with this problem, which ensures that the deployed liner ship fleet satisfy the shipping requirement of shippers at least with a predetermined probability. The problem is formulated as a joint chance constrained programming (JCCP) model to minimize the total expected cost incurred in container shipment. As the critical issue of the JCCP model is that the closed forms of the joint chance constraints are analytically intractable, the sample average approximation (SAA) method is used to deal with this issue and a SAA model is then proposed to approximate the JCCP model. Further, the SAA model is equivalently transformed into a mixed-integer linear programming (MIP) model which can be efficiently solved by an optimization solver CPLEX. Finally, a sensitivity analysis is conducted to evaluate the performance of SAA method and a numerical example of a real world liner shipping network provided by a liner shipping company is carried out to show the risk analysis based on the proposed model.

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Section: Risk Analysis In Shipping Industrymentioning

confidence: 99%

Risk management in liner ship fleet deployment: A joint chance constrained programming model

Wang

Meng

Wang

et al. 2013

Transportation Research Part E: Logistics and Transportation Re

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show abstract

“…One direction makes use of mathematical programming. According to Song and Dong (2011) these mathematical models often successfully capture the stochastic and dynamic nature of the problem, but they may give rise to some issues such as determining an appropriate planning horizon, ensuring computational tractability and ensuring robustness of the policy to uncertainties. An overview of literature on models using mathematical programming is presented in Table 2.3.…”

Section: Global Container Allocation Modelsmentioning

confidence: 99%

“…These threshold policies have the advantage of being easy-tooperate and easy-to-understand. (Song and Dong, 2011) Chou et al (2010) which combines inventory theory and mathematical programming is discussed as well. A decision support system based on network optimization is proposed by Shen and Khoong (1995).…”

Section: Global Container Allocation Modelsmentioning

confidence: 99%

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Optimization of empty container movements in intermodal transport

Braekers¹

2012

4OR-Q J Oper Res

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“…In the first stage, containers demand split rate was computed, while in the second case network optimization model was developed to determine the inland origin destination of import/export containers. Song and Dong (2011) used simulation and mathematical modeling to find an appropriate policy for the problem of repositioning empty containers in the presence of flexible destination ports. In this regards, no specific ports were chosen in advance, however, empty containers are unloaded in ports based on demand.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Empty Containers Distribution among Ports

Hajeeh¹,

Behbehani²

2011

Journal of Mathematics and Statistics

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Problem statement: Empty containers are an essential part of the logistics of the movement, repositioning and distribution of containers. Optimizing the allocation and transportation of empty containers contributes significantly in reducing cost. Approach: A mathematical model is formulated with the objective of finding an optimal sequence of ships movement among ports in order to satisfy demands at the ports with minimum total cost. Results: Several numerical examples are used to compare the performance of the developed optimization model with well known standard models available in the literature. Conclusion/Recommendation: It was observed that the solution obtained by the developed method to be either as good as or better than the solution provided by other standard models. Future work should consider contribution of full containers in meeting demand for empty containers.

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Effectiveness of an empty container repositioning policy with flexible destination ports

Cited by 64 publications

References 25 publications

Risk management in liner ship fleet deployment: A joint chance constrained programming model

Risk management in liner ship fleet deployment: A joint chance constrained programming model

Optimization of empty container movements in intermodal transport

Optimizing Empty Containers Distribution among Ports

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