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.

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In this study, we analyze an integrated productionpreventive maintenance planning problem where product processing times are affected by machine degradation. Preventive maintenance and repair can return the degraded machines/ equipment to normal conditions, which can improve the job processing times and the amount of energy consumed. We propose a mathematical model for an integrated production and preventive maintenance planning problem in a multiproduct, multi-period, single-machine manufacturing environment with minimal repair and energy consumption considerations in order to minimize the overall cost of production, inventory, energy, maintenance, and repair. The model investigates the impact of imperfect and "as-good-as-new" (AGAN) maintenance strategies on production plan and total cost. Experimental analyses provide insights about the proposed model: when the sensitivity of processing times to machine health status increases, the required number of maintenance actions increases. In addition, enforcing more number of maintenance actions into a production plan decreases energy cost of the system.

Instilling entrepreneurial mindset among engineering students is one of the challenges in engineering education. This paper presents the efforts to improve a core undergraduate industrial engineering course, Designing Value in Supply Chain, to infuse entrepreneurial thinking among students using an internally funded grant by Kern Entrepreneurial Engineering Network (KEEN). For this purpose, three new course modules are designed and their effectiveness on student learning is evaluated. This course is ideal for establishing entrepreneurially minded learning (EML) as a systematic approach is required for managing the chain of supply, especially since the impacts of the decisions are not isolated and will be spread out through the entire chain. In addition, creative multidisciplinary knowledge is required to address most of the supply chain challenges. The proposed modules are expected to promote students' creative thinking, curiosity, collaboration and communication skills, and enable them to identify the opportunities where they can apply their technical skills to create value in the community based on customers' expectations. These factors are key pillars of EML as proposed by KEEN.In the first course module, students propose a new product to be released to the market (idea generation). They complete this module as the product moves toward the end user in the supply chain following the concepts they learn during the term. This module enables the students to observe the domino impact of the decisions they make in the initial stages of supply chain and enhances structured learning experience by linking different concepts. In the second module, in order to expose the students to real life applications of the course content, wireless consumption data provided by students is used to practice different demand forecasting methods. Students also need to provide some economic analysis to choose the best solution alternative regarding their forecasted values. This module makes the learning process more meaningful as the learners observe a real life application of the subject. In the third module, students practice energy management in order to minimize energy waste as one of the most important types of waste in lean production systems. In this module, they are expected to determine several sources of energy waste on campus and propose action plans, and estimate the economic impact of their solution. As a result of this project, students learn how to create value and communicate an engineering solution in terms of economic benefits. Students provide a report for each module which is graded based on designed rubrics. All these modules are performed in teams which in turn improves students' team work and collaboration skills. This paper elaborates the details of each module and learning outcomes, and presents the student evaluation results, and at the end discusses the lessons learned.