The Quadratic Assignment Problem (QAP) is still considered to be one of the most difficult and widely used models in combinatorial optimization. The layout of healthcare facilities has been its most significant application area since the 1970s, representing a crucial field of study for increasing operational efficiency, patient safety, and staff flow. The QAP context has been continually altered and supplemented to cover the particular intricacies of the healthcare sector. After Elshafei's groundbreaking paper in 1977, the QAP framework was reinvented and extended to the point where it gained acceptance in the healthcare facility location planning area. This review offers a synthesis of the existing literature from 1977 to 2025 and classifies the research into ten different methodological streams: Exact Solution Methods, Classical Heuristics, Metaheuristics, Hybrid Approaches, Robust Optimization, Fuzzy QAP, Stochastic Programming, Multi-Objective QAP, Special Structure Exploitation, and Parallel & Dis-tributed Computing. The critical assessment of the transition of solution procedures and how the techniques for handling uncertainty have been developed shows how the research has progressed from modeling with one deterministic objective to a sophisticated data-driven approach where multiple objectives are characterized as well as the inherent uncertainties of the system. The analysis indicates the integration and hybridization trend—in the case of algorithms, objectives, and data sources is quite strong—pointing out the future lines of research in areas such as real-time adaptive layouts, deep learning integration, and pandemic-responsive design.

Supply chain disruption has become a serious world's problem during the past few years. Many businesses lose their customers due to late delivery or shortage of raw materials. Thus, it is necessary to look for expert systems to handle such issues. The paper presents a scientometrics survey on the role of artificial intelligence on supply chain management. The study uses the Scopus database to collect data from 1995 to 2022. The study collects nearly 750 articles which are sorted based on their citation records. Using some scientometric tools, the study has determined that the decision support system has been the most important tool to handle disruption in supply chain management. Moreover, the study shows that most studies were accomplished in North America, and some were partnerships with China. The study also detected nine groups of researchers who contributed the most in the supply chain. Moreover, the study discusses the concept of uncertainties associated with mathematical modeling associated with supply chain management and categorizes different works according to the methods used to handle the uncertainties. Finally, the study explains some of the recent developments of the implementation of artificial intelligence in various areas of supply chain management such as waste management, blood supply chain, etc. The results indicate that artificial neural networks are the most popular technique used among researchers to provide more efficient solutions for supply chain management.

Plastic pollution is one of the most importation subjects of water contaminations in the world. Plastic pollutions not only threats locally, but also, they are widespread, posing broader risks to the world and environment. Plastics which act as pollutants are categorized by size into micro-, meso-, or macro debris. The micro plastics move through oceans from one region to another region. Today, there are several evidence of micro plastics in south pole where we believe it as a clean area. This paper surveys the effects of micro plastics on the Great Lakes.

Therapeutic medications are the primary concern for restoration of functional independence and the quality of Canadian’s lives across the country. Bigger requirements and limiting opportunities have begun pushing on assessing the relative efficiency of all rehabilitation centers to find with evidence-based policy and funding decisions. Thus, this primary objective of this paper is to consider throughput, functional outcome, and budget allocations for ten Canadian provinces to measure the relative efficiency using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to provide a comparative view at service delivery and resource utilization. According to our results, when we consider equal weights for three factors, Manitoba is ranked first followed by Nova Scotia, New Brunswick and Saskatchewan. When we increase the weight of the budget in our method, these provinces still perform better than other provinces. Even when we reduce the weights of the budget, these provinces demonstrate good performance. Surprisingly, Ontario has presented the worst performance compared with other provinces.