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The paper presents the optimization possibilities of port terminal operations in order to generate maximum gain. Planning these processes is a demanding daily task taken on before dealing with port management, since transhipment operations, cargo loading and dispatching, maintenance and control of transhipment and transportation means are subject to hardly predictable and unexpectable stochastic conditions under which planning optimal terminal operations will include the examination of stochastic processes on the terminal. A model of states and transitions with gain and a model of optimal strategies in terminal management are set up. Furthermore, a model for determining the structure of transhipment equipment is developed. The devised models were adapted to the specificities of a port terminal and tested using the sample of a general cargo terminal. First Published Online: 07 Jul 2011

The paper demonstrates application of the queuing theory in modelling a port's bulk cargo unloading terminal. A bulk cargo terminal can be observed as a queuing system defined with basic parameters: the rate of bulk cargo ship arrivals or quantity of bulk cargo and the rate of ship servicing i.e. quantity of bulk cargo, in an observed time unit. Appropriate indices of bulk cargo terminal operations are computed on the basis of these parameters. The unloading terminal is determined as queueing system marked with M/M/1 by Kendall notation, System is characterized with Poisson distribution of entity arrivals and exponentially distributed service time. Subsequently, the model set up will be tested on the real example of the unloading terminal of the bulk cargo port in Bakar. Through application of the proposed model it should be possible to make decision on how to make optimization of the transhipment processes on a bulk discharging terminal to increase its efficiency. Obtained parameters and calculated indices point to solid capacity employment rate in 2005 and the probability that the berth is unoccupied is relatively low. Indices show that the terminal traffic rate has dramatically improved taking into consideration previous years.

Necessity for studying the processes in bulk cargo ports, which are the objects of research in this paper, follows from the fact that many real processes, which are characterized with unpredictability and changeability, are called stochastic because the parameters that determine these processes are the stochastic ones. In this paper the theoretical features of the Poisson process and a non-Poisson process, related to the queue will be given. This type of the queue, marked with M/D/1 by the Kendall notation, characterizes Poisson distribution of entity arrivals and deterministic distribution of service time. The main goal of this paper is to define port's bulk cargo terminal as the queuing system and then set up the appropriate model in which the loading terminal is determined as queuing system of type M/D/1. Subsequently, the model set up will be tested on the real example of the port's bulk cargo loading terminal in Bakar. On the basis of this model, the operating of the port can be determined with the purpose of making decisions for further running of the business.

The paper demonstrates the application of the queuing theOIYin modelling the port's bulk cargo unloading terminal. Abulk cargo terminal can be obsetved as a queuing system definedby basic parameters: the rate of bulk cargo ship arrivals orquantity of bulk cargo and the rate of ship servicing i. e. quantityof bulk cwgo, in an observed time unit. Appropriate indices ofbulk cmgo terminal operations are computed on the basis ofthese parameters. The unloading terminal is detennined as aqueuing system marked with MIMI I by Kendall notation. Thesystem is characterized by Poisson distribution of entity arrivalsand exponentially distributed service time. Subsequently, themodel set up will be tested on the real example of the unloadingterminal of the bulk cmgo port in Bakar. Through applicationof the proposed model it should be possible to make a decisionon how to optimize the transhipment processes on the bulk dischmgingtemzinal to increase its efficiency. The obtained parametersand the calculated indices point to solid capacity employmentrate in 2005 and the probability that the berth is unoccupiedis relatively low. Indices show that the tenninaltrafficrate has dramatically improved taking into consideration theprevious years.