Research on measuring airport operational performance has predominantly focused on technical aspects. However, there is still a need for studies to be conducted on measuring sustainable logistics and operational performance at international airports. The objective of this study is to develop a comprehensive measurement system for the Airport Operations Division in Indonesia that incorporates both operational and sustainable logistics performance. This will be achieved by integrating the company's vision, mission, and strategy into various performance measures using the Balanced Scorecard concept. The research methodology employed quantitative research methods, including primary data collection through observation and questionnaires. These questionnaires were developed using a pairwise comparison matrix derived from the Analytical Hierarchy Process (AHP). Data was collected from five international airports in Indonesia. The findings of the study demonstrate that the application of the Balanced Scorecard, coupled with the Objective Matrix method for setting performance targets and enriched with the AHP approach, enables the identification of priorities and assessment of performance. The research emphasizes the significance of considering non-financial aspects when measuring airport performance. This is crucial for supporting strategic decision-making and promoting sustainable performance improvement.

Patient Bed Assignment (PBA) consists of assigning patients to hospital beds according to specific requirements such as patient diagnosis, equipment requirements, age and gender policies, among others. We worked in conjunction with the Hospital Universitario San Ignacio (HUSI) with the goal of designing an application to support decision-making during the bed assignment process. We introduced a mathematical model for the PBA. We used Analytic Hierarchy Process (AHP) to determine the weights attributed to each part of the objective function. Due to the long execution time required, we used a Greedy Algorithm and Tabu Search (TS) to optimize the match between the patient’s requirements and the characteristics of the assigned bed. To test the algorithms, we created 15 test instances of various sizes. The results showed that the gap between the value of the objective function resulting from using the Greedy/TS in comparison with the optimal solution is on average 6.2%. Also, the TS takes 84% less time than the MILP for medium and large instances. We collected data from real life instances and compared the actual method with the designed metaheuristic. On average, the value of the objective function resulting from using the proposed Greedy/Tabu algorithm is 8.6% higher.

The aim of the present study is to find the relative importance of factors influencing the stu-dents’ decisions to adopt Massive Open Online Courses (MOOCs). Eight sub-factors catego-rized under three main factors namely “benefits of MOOCs”, “MOOCs features” and “social recognition”, are considered for prioritization. The analytic hierarchy process (AHP) method-ology is also employed to prioritize the factors. The primary data pertaining to pair-wise com-parisons of various factors and sub-factors have been obtained from 250 students by using convenience sampling. The results indicate that academic recognition, followed by openness, autonomy and cost effectiveness of MOOCs are the most important aspects which students consider while deciding to learn through MOOCs.

Nowadays, selecting the most appropriate location for hub is one of the most significant issues not only in road, rail and air transportations, but also in maritime. Transshipment is the fastest growing segment of the marine container market; it increases traffic flow of marine container and scope of this type of marine carriage, accordingly. In this way, determining a movement loop for the voyages of a shipping company, probes identification of container hub ports by considering different operational factors including distance to the destinations. The focus of this paper is to locate the best location for container transshipment hub in southern seas of Iran. In this paper, an MCDM model is proposed for evaluating and selecting the marine container transshipment hub port. Finally, the utilization of the proposed model is demonstrated with a real case study of Iranian main ports. The results show that the MCDM model can be used to explain the evaluation and decision-making procedures of a proper marine container hub location selection.

Supplier selection can be described as a very important problem that is not only multi-faceted but also takes into consideration the different choice making methods (MCDM). Supplier selection directly influences an entity's competitiveness, resilience, and sustainability. The main aim of this paper is to present the integration of the three most widely used MCDM methods -The Analytic Hierarchy Process (AHP), Best-Worst Method (BWM), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) -in supplier selection research in a systematic way. A bibliometric analysis of Scopus database shows a peculiar trend of geographical and journal-based variations. The search for "Supplier Selection" along with AHP yields a total of 521 articles, with India, China, and Turkey being the top three contributors, and Expert Systems with Applications and International Journal of Production Research being the main areas for publishing the findings. If one makes the same search with BWM one gets a total of 123 papers, where Iran and China take the lead, and the two areas with the highest representation are Journal of Cleaner Production and Sustainability. The integrated search consisting of supplier selection and one of the three MCDM methods mentioned above yields another 123 articles with India and Turkey as the leading contributors. The review goes into the detailed methodology of AHP, BWM, and TOPSIS explaining their specific advantages and disadvantages. Moreover, it brings together the results from more than 20 key studies to demonstrate the various ways of applying these methods in the area of green, resilient, and sustainable supply chains. The paper wraps up with a discussion on the emerging trends, challenges like methodological redundancy and geographical concentration, and future research directions which call for, among others, more hybrid models and the application of uncertainty handling techniques like fuzzy and rough sets.

This article introduces a scientometric investigation regarding the use of Multi-Criteria Decision-Making (MCDM) techniques in the area of portfolio optimization. The study, which employs a carefully selected dataset of 108 scholarly articles that are drawn from the Scopus database, covers the period 2003 to 2026, and maps the intellectual landscape, identifies the leading methodologies, and tracks the trends of migration. We perform a systematic analysis of the occurrence, impact, and application domains of 20 different MCDM methods, which include Data Envelopment Analysis (DEA), the Analytic Hierarchy Process (AHP), TOPSIS and PROMETHEE, among others. The study employs key indicators such as the number of publications, the number of citations, the geographical distribution of authors, as well as the dispersion of journals, to assess the impact and uptake of the various techniques. The analysis demonstrates the dominance of DEA as a method, which is often combined with other MCDM methods and metaheuristics. A trend towards hybridization, which includes the combination of MCDM with fuzzy set theory, machine learning, and evolutionary algorithms, has been recognized as one of the main factors contributing to the innovation of recent techniques. In addition, the study points out the increasing adoption of Environmental, Social, and Governance (ESG) factors and big data analytics into the portfolio selection process. The survey presents a quantitative picture of the domain and provides researchers and practitioners with valuable insights through the identification of the established pillars, the emerging hotspots, and the future research trajectories in the field of MCDM-based portfolio optimization.