Liner Shipping Cargo Allocation with Repositioning of Empty Containers

Brouer, Berit Dangaard; Pisinger, David; Spoorendonk, Simon

doi:10.3138/infor.49.2.109

Cited by 39 publications

(47 citation statements)

References 24 publications

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“…In the literature, the space-time network model is often used to formulate the container flows in a shipping network (e.g. Brouer et al, 2011). We present a slightly different model in the following, which offers a more intuitive view of the evolution of the jobs' status over space and time.…”

Section: Model Formulationmentioning

confidence: 99%

“…In the last decade, it has attracted a lot of attention. The existing studies can be classified as link-based routing (Alvarez, 2009;Agarwal and Ergun, 2008;Bell et al, 2011Bell et al, , 2013Yan et al, 2009) and path-based routing (Brouer et al, 2011;Song and Dong, 2012;Wang et al, 2013;Wang and Meng, 2012b). In general, the scale of link-based routing model is smaller than that of path-based routing model as path-based model is based on the enumeration of all possible paths or dynamical generation of the profitable paths (Wang, 2014).…”

Section: Introductionmentioning

confidence: 98%

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Joint service capacity planning and dynamic container routing in shipping network with uncertain demands

Dong

Lee

Song

2015

Transportation Research Part B: Methodological

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a b s t r a c tService capacity planning is a key tactic decision in container shipping, which has a significant impact on daily operations of shipping company. On the other hand, operational decisions such as demand fulfilment and shipment routing will impact on service capacity requirements and utilisation, particularly in the presence of demand uncertainty. This article proposes a two stage stochastic programming model with recourse to deal with the problem of joint service capacity planning and dynamic container routing in liner shipping. The first stage of the model concerns how to determine the optimal service capacity, and the second focuses on the optimal routing of shipments in stochastic and dynamic environments under a given service capacity plan. Initially, SAA (Sample Average Approximation) is employed to solve the model. Noting the computational complexity of the problem, Progressive Hedging Algorithm (PHA) is employed to decompose the SAA model into a number of scenario-based models so that reasonably large scale problems can be solved. To handle larger scale problems, we develop a new solution procedure termed as APHA (Adapted Progressive Hedging Algorithm) that further decomposes the scenario-based model into job (customer order) based models with measurable error bounds. Numerical experiments are conducted to illustrate the effectiveness of the proposed APHA in solving the problems under consideration.

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Section: Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Joint service capacity planning and dynamic container routing in shipping network with uncertain demands

Dong

Lee

Song

2015

Transportation Research Part B: Methodological

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“…In recent decades, the container cargo allocation/assignment problem has attracted much attention in the maritime studies, for example, Ronen (1983), Christiansen et al (2004), Branchini et al (2015), Brouer et al (2011), Song and Dong (2012), Christiansen et al (2013), Bell et al (2013), Brouer et al (2014), Norstad et al (2011), Wang et al (2014b), Karsten et al (2015), to name but a few. For instance, Brouer et al (2011) investigated the cargo allocation problem subject to the availability of empty containers and put forward both arc-flow and path-flow formulations.…”

Section: Introductionmentioning

confidence: 99%

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

Wang

Zhang

Wang

2016

Transportation Research Part C: Emerging Technologies

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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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Liner Shipping Cargo Allocation with Repositioning of Empty Containers

Cited by 39 publications

References 24 publications

Joint service capacity planning and dynamic container routing in shipping network with uncertain demands

Joint service capacity planning and dynamic container routing in shipping network with uncertain demands

A joint optimization model for liner container cargo assignment problem using state-augmented shipping network framework

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

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