Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand. (April 2015)

• Record Type: Journal Article
• Authors: Ng, ManWo
• Is Part Of: Transportation research. Volume 74(2015)
• Page Start: 79
• Page End: 87
• Publication Date: 2015-04
• Subjects: Fleet deployment -- Liner shipping -- Container shipping -- Maritime transportation -- Stochastic dependence -- Uncertainty; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2015.01.004
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Specifying only the mean and variance of the shipping demand suffices. Derived results are fully analytical, contrary to prior work. Derived bounds are shown to be sharp under certain conditions. Stochastic dependencies in shipping demands have been modeled. Not required to explicitly specify dependence form. Abstract: The problem of optimal container vessels deployment is one of great significance for the liner shipping industry. Although the pioneering work on this problem dates back to the early 1990s, only until recently have researchers started to acknowledge and account for the significant amount of uncertainty present in shipping demand in real world container shipping. In this paper, new analytical results are presented to further relax the input requirements for this problem. Specifically, only the mean and variance of the maximum shipping demand are required to be known. An optional symmetry assumption is shown to further reduce the feasible region and deployment cost for typical confidence levels. Moreover, unlike previous work that tends to ignore stochastic dependencies between the shipping demands on the various routes (that are known to exist in the real world), our models account for such dependencies in the most general setting to date. A salient feature of our modeling approach is that the exact dependence structure does not need to be specified, something that is hard, if not simply impossible, to determine in practice. A numerical case study is provided to illustrate the proposed models.