A DSS for integrated distribution of empty and full containers
Decision Support Systems
Empty containers repositioning: a state of the art overview
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
A Fluid Flow Model for Empty Container Repositioning Policy with a Single Port and Stochastic Demand
SIAM Journal on Control and Optimization
A recursive model for static empty container allocation
Frontiers of Computer Science in China
Positioning empty containers under dependent demand process
Computers and Industrial Engineering
Empty container management in a port with long-run average criterion
Mathematical and Computer Modelling: An International Journal
Maritime repositioning of empty containers under uncertain port disruptions
Computers and Industrial Engineering
Optimization of empty container movements using street-turn: Application to Valencia hinterland
Computers and Industrial Engineering
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Containerized liner trades have been growing steadily since the globalization of world economies intensified in the early 1990s. However, these trades are typically imbalanced in terms of the numbers of inbound and outbound containers. As a result, the relocation of empty containers has become one of the major problems faced by liner operators. In this paper, we consider the dynamic empty container allocation problem where we need to reposition empty containers and to determine the number of leased con tainers needed to meet customers? demand over time. We formulate this problem as a two-stage stochastic network: in stage one, the parameters such as supplies, demands, and ship capacities for empty containers are deterministic; whereas in stage two, these parameters are random variables. We need to make decisions in stage one such that the total of the stage one cost and the expected stage two cost is minimized. By taking advantage of the network structure, we show how a stochastic quasi-gradient method and a stochastic hybrid approximation procedure can be applied to solve the problem. In addition, we propose some new variations of these methods that seem to work faster in practice. We conduct numerical tests to evaluate the value of the two-stage stochastic model over a rolling horizon environment and to investigate the behavior of the solution methods with different implementations.