This study centers on sustainable operations management within the energy sector, identifying and synthesizing effective strategies for integrating sustainability into business practices. We perform a systematic literature review covering contributions from January 2000 to June 2024 extracted from Web of Science and Scopus databases. The methodology includes an explicit search and selection protocol to ensure relevant and unbiased insights into the evolution of sustainable practices in the energy sector. The results indicate an increase in publications over the years, particularly in areas such as low-carbon economies, environmental management, and innovation, all of which are crucial for reducing carbon footprints and enhancing operational sustainability. This study categorizes existing research into five main streams: Closed Loop Supply Chains (CLSC), Low Carbon Economy (LCE), Environmental Management and Performance (EMP), Innovation (INN), and Social Responsibility (SR). The review underscores the significant gap between current practices and the potential for incorporating renewable energy sources into existing systems. In addition, it highlights the need for robust governmental policies and international cooperation in order to foster a more rapid transition towards sustainable operations on the energy sector. Furthermore, our findings suggest that despite technological advances, significant implementation gaps remain that require focused research and policy adjustments in order to achieve sustainability targets in the energy sector.

The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India’s second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India’s second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India’s second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

Scheduling Advertising on Cable Television Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers’ campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, S. Souyris, S. Seshadri, and S. Subramanian develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. According to standard business metrics and the small integer programming gap, these schedules are of high quality. Using their methods, leading networks in the United States and India experience a 3% to 5% revenue increase, which translates to about $60 million annually for one prominent user.

Solar electricity generation is a strategic component in the energy portfolio of many countries to replace fossil fuels and reduce green-house gas emissions. Installations of solar photovoltaic (PV) systems by residential homes in the United States have grown rapidly in the past decade, driven by decreasing costs of PV systems and government incentives. However, PV systems currently still produce only a small fraction of the total electricity generated in the U.S. So, it is important to study how federal and local incentives can be made more effective to achieve the goal of widespread residential adoption of PV systems. We study the diffusion of PV systems by modeling the adoption decisions of individual households who are forward-looking and consider installing PV systems as an investment with its future economic benefits. Moreover, the space-time dynamics of installations reveal a clear pattern that a household’s adoption decision is strongly influenced by the adopters in their neighborhood. We propose a dynamic discrete choice model in which a household considers the inter-temporal trade-off between installing the system now and waiting by analyzing the current and future return on investment and peer effects. Our model incorporates unobserved household heterogeneity in addition to segmenting households by their home values and geographic locations. We collect a novel data set with detailed information on PV adoptions in Austin, Texas to estimate the model parameters. For inference, we use a Bayesian method to overcome the computational burden. Our estimation reveals a strong peer effect in a household’s adoption decision. Our structural model permits counterfactual analysis to generate insights to design incentive programs that are effective in accelerating the PV diffusion process. As an illustration, counterfactual policy simulations for local rebate programs demonstrate how policy-makers can achieve higher adoption rates with a lower budget.

In this study, we discuss the role of testing, along with other control measures, as a mitigation mechanism for emerging epidemics. We present several models that incorporate testing along with quarantine policies, lockdowns, and vaccination as control mechanisms for epidemics. We also present a model for data-driven prediction and allocation of test kits to a network of connected locations. This study will provide important modeling strategies and policy pointers for formulating testing policies for the control of epidemics.