Healthcare facilities in the United States account for 4.8% of the total area in the commercial sector and are responsible for 10.3% of total energy consumption in this sector. The number of healthcare facilities increased by 22% since 2003, leading to a 21% rise in energy consumption and an 8% reduction in energy intensity per unit of area (544.8 kWh/m²). This study provides an analytical overview of the end-use energy consumption data in healthcare systems for hospitals in the United States. The energy intensity of the U.S. hospitals ranges from 640.7 kWh/m² in Zone 5 (very hot) to 781.1 kWh/m² in Zone 1 (very cold), with an average of 738.5 kWh/m². This is approximately 2.6 times higher than that of other commercial buildings. High energy intensity in the healthcare facilities, particularly in hospitals, along with energy costs and associated environmental concerns make energy analysis crucial for this type of facility. The proposed analysis shows that U.S. healthcare facilities have higher energy intensity than those of most other countries, especially the European ones. This necessitates the adoption of more energy-efficient approaches to the infrastructure and the management of healthcare facilities in the United States.

Purpose: This paper aims to study a generalized type of mixed-model assembly line with multi-manned workstations where multiple workers simultaneously perform different tasks on the same product. This special kind of assembly line is usually utilized to assemble different models of large products, such as buses and trucks, on the same production line. Design/methodology/approach: To solve the mixed-model multi-manned assembly line balancing problem optimally, a new mixed-integer-programming (MIP) model is presented. The proposed MIP model is nondeterministic polynomial-time (NP)-hard, and as a result, a simulated annealing (SA) algorithm is developed to find the optimal or near-optimal solution in a small amount of computation time. Findings: The performance of the proposed algorithm is examined for several test problems in terms of solution quality and running time. The experimental results show that the proposed algorithm has a satisfactory performance from computational time efficiency and solution accuracy. Originality/value: This research is the very first study that minimizes the number of workers and workstations simultaneously, with a higher priority set for the number of workers, in a mixed-model multi-manned assembly line setting using a novel MIP model and an SA algorithm.

In this paper a two- phase method is applied for maintenance strategy selection problem. As selecting the most appropriate maintenance strategy requires different alternatives to be assessed for a range of criteria, it could be assumed as a typical multi-criteria decision making problem. There also exist several key factors affecting the selection procedure such as reliability, services quality, risk, etc., which taking all of these factors into account make the decision makers encounter with a huge amount of data that might be accounted as redundancy. In the first stage of the proposed methodology, Factor Analysis (FA) technique is applied to extract the covert structure between different criteria and provide a coherent set of variables by means of clustering. In the second phase, a fuzzy TOPSIS method is presented to deal with the uncertain and imprecise judgments of decision makers which would be translated into fuzzy numbers. In addition, a numerical example is presented to accredit the validity of the proposed model.

She also earned her masters and undergraduate degree in Industrial Engineering in Iran, Tehran. Dr. Ghazi-Nezami is a Certified Six Sigma Green Belt (CSSGB) from the American Society for Quality (ASQ). Her research interests include applied optimization, sustainability, energy efficient manufacturing systems, supply chain and operations management, and engineering education. In educational research, her interests include online education, active learning and entrepreneurial mindset development in engineering classes.