Nowadays, container terminals are subject to extensive technological changes and specific transformations. Changes applied to terminals tend to increase their ability to offer high- end personalized services to the customers and finally affect the competitiveness. The estimation of efficiency corresponds to terminals’ ability to increase the production with a specific level of inputs and has been the topic of many studies, especially those conducted on a wider regional or global level. The main objectives of our research are to evaluate the model, conduct sensitivity analysis, and estimate technical efficiencies on a sample of North Adriatic Ports Association (NAPA) interconnected medium-sized terminals, located in the narrow geographic area, on the same transport corridor thus representing each other’s competition. For that purpose, we have implemented a stochastic frontier approach on a balanced panel dataset of first-order and additionally introduced control input variables with Cobb-Douglas and trans-logarithmic functional forms. The stochastic production frontier estimation shows the range of NAPA terminals’ technical efficiencies from 65.24% to 93.92%, with a global average of 78.49% and a positive trend of 1.28% over the observed period of time. Our findings also indicate that NAPA terminals with the highest estimated technical efficiencies do not necessarily need to be the most productive ones, and vice versa.

Successful management of any sea port depends primarily on the harmonisation of transport supply and demand, whereas their incompatibility leads to a number of problems. The port, i.e. its management, through its operation and part of port policy may affect the planning of the construction or modernization of its port facilities. In doing so, the specified planning requires forecasting and quantification of the needs for infrastructural services of specified port, i.e. assessment of traffic demand. Accordingly, the basic problem of research in this paper is forecasting of traffic demand for the port services by applying the appropriate forecasting methods. In order to find ways of solving concrete problems in the port operations the methods for traffic demand forecasting are set by applying the methodology and the presentation of the application of economic forecasting methods. The selected methods of demand forecasting for port services in demand are illustrated and tested on the example of the Croatian largest cargo port, the Port of Rijeka. KEY WORDS: planning, forecasting, traffic demand, commodity flows, port capacity

This paper considers a problem of planning short term operations in a bulk terminal faced by port management when making tactical decisions. Ship loading and discharging, cargo stocking, the maintenance and service of facility equipment are regular operations of a bulk terminal which is a subsystem of a sea port the conduct of which is subject to difficult‐to‐predict or unforeseen influences. The problem that port management encounters in day to day operations looks into making the best possible plan with the scheduled duration of operations/states and transition instants, considering various internal and external factors influencing terminal performance. A state and transition model is used for deriving effective solutions to obtaining the state order and state transition time of a bulk terminal with an objective of minimizing operational costs. The behaviour of the terminal is tested applying the stochastic and deterministic method.

The paper gives an overview of the real system as a multiphase single server queuing problem, which is a rare case in papers dealing with the application of the queueing theory. The methodological and scientific contribution of this paper is primarily in setting up the model of the real problem applying the multiphase queueing theory. The research of service system at Rijeka Airport may allow the airport to be more competitive by increasing service quality. The existing performance measures have been evaluated in order to improve Rijeka Airport queueing system, as a record number of passengers is to be expected in the next few years. Performance indicators have pointed out how the system handles congestion. The research is also focused on defining potential bottlenecks and comparing the results with IATA guidelines in terms of maximum waiting times.

Modelling of a traffic system refers to the efficiency of operations for establishing successful business performance by examining the possibilities for its improvement. The main purpose of each container terminal is to ensure continuity and dynamics of the flow of containers. The objective of this paper is to present a method for determining the amount of certain types of containers that can be transhipped at each berth, with the proper cargo handling, taking into account minimum total costs of transhipment. The mathematical model of planning the transhipment and transportation of containers at the terminal is presented. The optimal solution, obtained with the method of linear programming, represents a plan for container deployment that will ensure effective ongoing process of transhipment, providing the lowest transhipment costs. The proposed model, tested in the port of Rijeka, should be the basis for makingadequate business decisions in the operational planning of the container terminal.

This paper proposes a new optimization model for ship’s cargo handling operations which solution gives the structure of cargo handling resources required, along with attaining the minimum total ‘in-port’ costs and the minimum of time required for completion of cargo operations. Due to complexity of the model which consists of composite multi-objective functions together with several decision variables and constraints, the solution has been sought by utilization of an adapted genetic algorithm combined with a hybrid algorithm. Testing of the model on real world data yielded acceptable results in a short time. In the course of decision making, the ship’s operator can, on the basis of the proposed model and taking into consideration shipping market data, choose appropriate variation of the returned solution, which incorporates minimum costs, minimum of operational time and related cargo handling resources.