The paper examines the impact of integration of macroeconomic indicators on the accuracy of container through

An accurate forecasting system has manifested its role as an enabler in supply chains (SC), which makes the operation possible in a maximally synchronized manner. Its applications have gained the attention of scholars across various disciplines such as forecasting in market behavior analysis and tourism industry; material requirement planning in production; transport and logistics foresight in networks and facilities. Seaports, as specific SC members, are not an exception. Accurate forecasting is needed in almost all aspects of the ports' operation to avoid financial losses related to inappropriate investments and planning. The paper addresses the forecasting of joint demand-supply cargo throughputs in the Adriatic Seaport Koper. The research presents a new forecasting approach, namely, DFA-ARIMAX (Dynamic Factor Analysis-ARIMAX modeling). External economic indicators were screened to obtain useful information using the DFA prior to directing the dynamic factors into the ARIMAX forecasting model. The principal component regression and Monte Carlo framework were included to identify indicators that are unique to the port. Findings revealed that a forecasting system by its enriched capabilities to predict the observed throughputs could be seen as Functional Decision Support System. The benchmarking shows that proposed models outperform competitive models. Practical implications are discussed in detail.

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In the paper, we consider a well-known port choice problem. By applying Mixed Integer Linear Programing (MILP), the relevant importance of the decision factors is presented. The importance of the decision factors was analysed by several cases. First of all, the ports within a narrow region were taken into consideration, and afterwards the ports from different multiport regions were compared. In the first case, the distance between ports has small effect on the choices made by the decision makers, while in the second one, the distances and consequently both the land transport and subjective decision factors play an important role within the decision making process. The analysis therefore indicates that decision factors do not have equal importance, but depend on the problem perspective.

The provision of optimised routing solutions is a priority to generate maximum decreases in the cost of school transport. The aim of this study was to achieve a reduction in existing costs while processing the fare-free transportation of eligible pupil commuters (PCs). The optimisation problem comprised the minimisation of vehicle costs and the total travel time for all pupils. To solve this problem, a heuristic algorithm was developed based on route planning dependent on changes in schools’ starting times so that vehicles can visit a greater number of schools within one route. The algorithm was applied to a municipality in the EU region and the optimised system has been successfully running for 5 years. This study differs from others as it deploys a two-mechanism procedure: the identification of eligible PCs prior to assigning them to bus stops and determining the optimal assignment of school start/finish times for selected schools simultaneously with optimal driving routes and vehicle fleet. After application of the optimisation to the municipality, the total daily mileage of all vehicles was almost 300 km less than the previous un-optimised situation, while the total number of vehicles was reduced by almost 50%.