ABSTRACT: At design stages of any sea port development projects one of the key tasks is to estimate the amount of cargo volume to be stored on the port warehouse. The shortage of the warehouse facilities would disrupt port operations and affect the port marketing position, while the surplus capacity would raise the self-cost of the services rendered by the port. Many port developing projects and long years of operational practice have resulted into certain commonly accepted mathematical techniques that enable to assess all main parameters sufficiently accurately. With ever-growing completion between the ports worldwide, to find a delicate balance between the cost and quality becomes a core task behind nearly every aspect of port design activity. The tools that have been used for centuries in port design and development started to lose their adequacy in modern economic and logistic environment. As the response for this challenge the port designers more and more move to simulation models. In the same time, an adequate simulation models need not only accurate and reliable data, but also requests quite long time. Moreover, the models of the kind usually are created ad hoc, reflecting particular features of the primal object under development and forfeiting the generality and universality of analytical models. At beginning stages of port developing one need to have simple and easy tools for the preliminary accession of project parameters, since usually there are several variants and the full-scaled simulation of them is excluded. Still, these tools should be more enhanced sophisticated than common analytical formulae. The main drawback of the formula calculation (streaming computing by the current IT terminology) is they principally deal with deterministic values, while the real worlds is inhibited with the stochastic ones. The study represented here is an attempt to narrow this gap. The area selected to demonstrate the approach is the port warehouse size, regardless of the cargo type handled. In the same time, this technique can be spread on many other port project parameters needed to be assessed.

KEYWORDS: Port Design, Port Development, Sea Ports, Port Operations, Port Storage Capacity, Stochastic Spread of Demand, Gaussian Distribution, Storage Capacity

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