Student evaluations of faculty members' teaching effectiveness are considered quite important in Higher Education (HE). In this paper, we elaborate on the framework of Nikolaidis and Dimitriadis, based mainly on Statistical Process Control techniques and tools, which enables a deeper analysis and broader exploitation of student evaluation data. More specifically, we thoroughly examine and evaluate through simulation, several popular types of control charts (CCs), identifying the most suitable among them, using as comparison criteria various statistical properties of CCs. The ultimate goal of our research is to provide decision makers in HE institutions with an easy-to-use reliable tool for not only monitoring the teaching process, but also identifying the effective and ineffective faculty members’ teaching performance to promote the quality of their Institution.

Contemporary ports are facing a variety of challenges due to technological advancements, economic pressures, and changing policies. Key issues include the effects of globalization, rapid advancements in information and communication technologies (ICTs), and the changing nature of port services. In order to tackle these challenges and achieve operational excellence, adapt to the shifting of activities, and meet new business demands, smart ports have been proposed as a comprehensive solution. These challenges arise because port success is often measured by traditional metrics such as port size and performance. To accurately assess the intelligence of a port, there is a need for a systematic and scientifically sound smart port evaluation method. This paper provides an overview of the concept of a smart port and develops a multi-criteria assessment framework of port smartness based on neutrosophic cognitive maps (NCMs). The unique and valuable characteristic of NCMs lies in their ability to manage the uncertainty associated with the relationship between two concepts, indicating their effects on each other in neutral states. This structure enables the NCM to provide results with a greater degree of sensitivity than fuzzy cognitive maps (FCMs) and allows for a greater degree of freedom of intuition for an expert to express not only the potential impacts but also the uncertainty associated with those impacts. Our methodology can make decisions using incomplete, uncertain, and inconsistent data during the assessment process, providing a rigorous quantitative framework for the assessment of port “smartness”. The proposed solution has the potential to act as a valuable tool in a group decision support environment and can be used to accelerate an organization’s development, improve productivity, and reinforce efforts to achieve strategic and sustainability objectives. To achieve this, an appropriate framework for such a methodology is demonstrated through an illustrative example offering actionable insights for improving port operations.

It is well-known that the use of quantitative methods and modern analytics by companies in competitive markets is of vital importance both for their efficiency and financial profit. This study presents a novel, mathematical production-planning model for a real-world production optimisation problem of a company located in Northern Greece. The company produces non-alcoholic soft drinks and needs to continuously allocate its human resources in order to have the optimum profit. Therefore, the objective of the model is the minimisation of the company's idle human-hours subject to the fulfilment of the demand of customers. As algebraic modelling languages are well-suited for prototyping and developing optimisation models, this production planning, mathematical model is implemented in Python v3.7.3 and solved using Gurobi solver v9.0. Furthermore, we also describe the competitive advantages offered by our quantitative approach and the initial allocation plan by the company.

Student evaluations on the teaching efficiency of faculty members are significant in Higher Education because they allow decisions makers to identify potential inefficient faculty members and by making appropriate decisions, to improve the quality of the teaching process of their Institution. In this paper, we revisit the methodology framework of the previous paper of Sivena and Nikolaidis in order to expand it in various ways. More specifically, we examine in detail and evaluate through simulation, several popular types of $\overline{X}$ and $\tilde{X}$ control charts, identifying the most suitable among them, using various statistical properties of control charts as optimization criteria. Additionally, we present two more tools that will allow Higher Education Institutions if exploited in real life, to find differences in the teaching performance between evaluated faculty members, either the latter are compared in one or more pairs. All in all, we provide Higher Education decision-makers with easy-to-use and reliable tools for monitoring the teaching process of their Institutions.

Dear authors, I would like to make some remarks: * The use of English should be improved (especially the sentences I put in handmade red brackets should be rephrased) * There is no literature review! What has been written by other researchers in t...