Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.

Sustainable engineering is very important for logistics systems. Nowadays, sustainable warehouse management is a key factor in market success. Workforce fluctuation and inverting the number of customers’ demands make a lot of problems in distribution warehouses. This study addresses a sustainable approach for the workforce scheduling problem recognized in a real distribution warehouse. The problem arises from the high variability of demand for workers over one workday, which causes workforce surplus in some periods of the workday and shortages in others. Engineering managers of the distribution warehouse already use different full-time and part-time shifts, and schedule workers on different activities, but they still have significant workforce surpluses or shortages in some periods. This study proposes the scheduling of activities’ execution together with workers to face that variability and decrease the cost of the workforce. This idea comes from the fact that some activities in a distribution warehouse can be done in a specific time period after the need for them occurs. In this way, the variability of demand for workers can be decreased, and a lower workforce cost may be ensured. Based on this idea, the entire problem is modeled as integer linear programming. The real example of the problem is solved, and the proposed model is tested on randomly generated instances of the problem in Python by means of the PuLP linear programming package. The results indicate different positive effects in the manner of sustainable warehouse management: lower workforce costs, time savings, better utilization of all types of resources and equipment, increased employee satisfaction, and so on. For even 61% of instances of the introduced problem, the obtained cost of the workforce is lower by more than 20% if activities’ executions are scheduled together with employees.

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Managing empty-container movements is one of the most challenging logistics problems in the shipping field. With the growth of global trade imbalance, the repositioning process has become necessary, immediately after emptying a container. The main contribution of this research paper is to enrich the most frequently used methods, models, and applications in the literature, for relaxing the empty-container-repositioning problem. The article presents practices that vary between organizational policies, technical solutions, and modelling applications. A review of optimization models has been used for comparisons, based on specified criteria, such as the time frame, inputs, outputs, scale of the project, and value. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was applied through the online database Web of Science (WOS). It gives a comprehensive description of all the relevant published documents. On the basis of conducting a brief systematic review, future research opportunities have been determined, considering the emerging phenomena in container transport chains.

The urban mobility system is an important factor in social development and must, therefore, be tackled in a way that enables balanced, sustainable development. The purpose of the present work was to introduce a new holistic approach to urban mobility system (UMS) planning, which involves a strategic decision-making process with a broad involvement of various stakeholders. For this purpose, an innovative model was created by synthesizing the focus group (FG) method with the nominal group technique (NGT), SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis, and the fuzzy analytical hierarchical process (FAHP) method. The fuzzy approach was chosen for its ability to incorporate imprecise and vague information and make a decision-making UMS process more realistic. Accordingly, the objective of the paper was to propose a newly developed model that will (considering the integration of various urban mobility subsystems) enable the detection, identification, and ranking of key priorities required for a more holistic approach to UMS planning. The results revealed that the developed integrated model enables acquired areas to be ranked according to priorities, which further allows the development of scenarios. Moreover, the model allows a better understanding of how to search for compromises when one is faced with multi-criteria decision-making and coordination of frequently contradictory goals. A new integrated urban mobility model, as proposed herein, was also successfully tested in a real-life application, which proves its potential for use in sustainable urban mobility planning in a holistic way.

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.