California community college leaders are looking for strategies to sustain facilities and maintenance operations because the governor only approved the allocation of $87.5 million in the 2014-2015 Budget Act for facilities maintenance operations. Guided by the change and strategy theories, the purpose of this multicase study was to explore the strategies that a select group of college leaders have used to sustain or improve their facilities maintenance operations. The data collection process included a review of college planning documents and semistructured interviews with 10 senior administrators from 3 large California community colleges who have used strategies to address sustaining or improving their facilities maintenance operations. Saldana coding and an inductive analysis process were used to identify themes. Triangulation was employed to increase the trustworthiness of interpretations. The analysis revealed the central role of planning as the strategy leaders should employ to improve institutional success. Funding was an additional theme leaders regarded as the issue that most often undermined planning and effective maintenance operations. All participants acknowledged the need for the integration of planning and funding to create institutional success. These findings suggest that community college leaders who use planning, funding strategies, maintenance strategies, and who empower people to sustain facility and maintenance operations can improve the teaching-learning environment. When community college leaders transform the teaching-learning environment, they enable student success. Student success increases the earning power of students that contributes to social change by expanding the tax-base and creating greater economic development.

Modern tournament formats for versus-type games are well adapted to games with no more than 2 actors in a single match but poorly performs in terms of accuracy (i.e., having the strongest participant win) or match count when used for a game that pits three players against each other in a single match. Using Monte Carlo simulations, this study tries to adapt widespread formats to this context, measure their performance, and raise the implication of organising a tournament with this format. Then, with the results from that first analysis, tries to design an adapted tournament format. Findings include numerical analysis of the tried tournaments’ performances in regard to ranking accuracy and time consumption (e.g., how many matches are required to be played in the tournament) as well as a first draft of a tournament format tailored for a competitive and tactical game pitting three participants in a single match.

We present the process followed to create a decision-aid tool for use in renewable energy and energy efficiency investment decisions. Our tool is targeted at home and small business owners in the Pioneer Valley. We begin with the development of two prototype tools. The first was created for the Hitchcock Center for the Environment, and is an Excel-based tool that allows users to select various combinations of technologies and instantly see the financial, environmental, and educational impacts of their choice. The second examines only two technologies, solar photovoltaics and combined heat and power, and uses a cost minimization approach. These prototype tools inform the development of the Pioneer Valley Sustainability Network (PVSN) decision-aid tool. The PVSN tool allows users to compare a building’s current energy consumption with the expected performance given the implementation of one or several renewable energy or energy efficient technologies. The PVSN tool evaluates financial costs along with externalities like emissions damages and health impacts. It also provides modeling of decision making under uncertain costs of damages from carbon emissions.

Complex assembly systems are made up of hundreds, and in some cases, thousands of parts, that all need to be managed in a proper manner so part arrivals will coincide to meet a build plan, and ensure production requirements are satisfied. A major challenge faced by manufacturers for these complex systems is that many parts have long and complex supply chains, which result in long and highly variable supply lead times. The high cost and low volume makes holding large stocks of these components unviable. Thus, the need arises for the development of a simulation tool that can predict the time all of the required parts are ready for assembly, and allow for comparison of various ordering and inventory strategies. Two strategies were tested, the current practice of ordering to an agreed upon quoted lead time, and a strategy which accounts for lead time variability through advanced ordering. The results of these two strategies displayed the benefits of synchronizing the system through advance ordering, as a potential 60% reduction in inventory was observed. Future development in the tool would incorporate more granular steps of the build sequence, as well as the inclusion of quality non-conformance (QN) issues.

Hospitals around the country are struggling to provide timely access to inpatient beds. We use discrete event simulation to study the inpatient admission and discharge processes in US hospitals. Demand for inpatient beds comes from two sources: the Emergency Department (ED) and elective surgeries (NonED). Bed request and discharge rates vary from hour to hour; furthermore, weekday demand is different from weekend demand. We use empirically collected data from national and local (Massachusetts) sources on different-sized community and referral hospitals, demand rates for ED and NonED patients, patient length of stay (LOS), and bed turnover times to calibrate our discrete event simulation model. In our computational experiments, we find that expanding hours of discharge, increasing the number of days elective patients are admitted in a week, and decreasing length of stay all showed statistically significant results in decreasing the average waiting time for patients. We discuss the implications of these results in practice, and list the key limitations of the model.

In business-to-business logistical sourcing events, companies regularly use a bidding process known as tendering in the procurement of transportation services from third-party providers. Usually in the form of an auction involving a single buyer and one or more sellers, the buyer must make decisions regarding with which suppliers to partner and how to distribute the transportation lanes and volume among its suppliers; this is equivalent to solving the optimization problem commonly referred to as the Winner Determination Problem. In order to take into account the complexities inherent to the procurement problem, such as considering a supplier’s network, economies of scope, and the inclusion of business rules and preferences on the behalf of the buyer, we present the development of a mixed-integer linear program to model the reverse combinatorial auction for logistical tenders.

This is a report of a professional project intended to act as an informational tool for the evaluation of a dynamic dispatch system at Embarcadero Station for the San Francisco Municipal Transportation Agency (SFMTA). Systems that operate dispatch algorithms do so in response to on-time performance and reliability. The optimization problem is documented in many transportation contexts including airline networks, bus dispatch, and freight routing. According to the research, optimizing available options and re-routing based on available options can create a more efficient system that would minimize operating costs and improving service reliability for customers. The methodology presented here uses current headway information to dynamically change dispatch assignment between J, L, and M lines. The reassignment program was applied to randomly selected weekdays in March of 2012. After analysis it was determined that the application has the potential to decrease mean operating headways by up to 3% (or approximately 15 seconds).

Individuals' decisions to adopt an innovation can be influenced by the group identity of previous adopters or non-adopters in their social network. Previous research in innovation diffusion considered initial innovators and word-of-mouth imitator effects using analytical models. Simulations and agent-based models have been developed to address the heterogeneity of decision makers and the non-linearity of the process. A further refinement modeled adoption based on networks of social relationships between potential decision makers, analogous to the spread of disease on networks. In addition, adoption or non-adoption of some innovations has been characterized as a means of signaling identification with or aversion to a group. While identity signaling and outgroup aversion effects on adoption have been considered in a geo-spatial environment, this work extends these concepts to social network environments. The results show that adoption levels were significantly different in a network environment with outgroup effects present. Additionally, as outgroup and imitation factors increase, adoption levels decrease, and polarization increases in network environments. With group effects present, adoption was found to be higher when modularity and eigenvector centrality are high in a social network. Next, to test the model, Covid-19 vaccination adoption behavior was examined to find statistical evidence of the outgroup effect with groups defined by political affiliation. Finally, a model was developed to address gaps in the original model and evaluated with vaccine adoption data. In today's polarized social environment, understanding these effects is critical to the adoption of emerging innovations such as mitigating climate change, combating novel viruses, or decentralizing financial transactions. While innovators are often focused on solving technical challenges to advance adoption of an innovation, equal emphasis on understanding and solving social and potential outgroup effects will be needed to accurately project the rate of adoption and to achieve the desired outcome.

This study aims to explore the impact of COVID-19 on consumer buying behavior and shifts in retail business models within the Kingdom of Saudi Arabia, in response to the challenges introduced by the pandemic. The study had several objectives, including examining the global impact of the pandemic on retail consumers, analyzing variations in the effect on luxury and essential products, investigating the strategies employed by physical store retailers to address the consequences of COVID-19, and providing recommendations for retailers to operate sustainably in the future. To achieve these objectives, the study utilized a mixed-methods approach, integrating quantitative and qualitative research. Data was collected from 180 consumer respondents via a quantitative survey questionnaire and from eight retailers through one-on-one interviews. Reliability analysis was conducted using Cronbach's Alpha. Subsequently, the data was evaluated through frequency and cross-tabulation methods, while interview transcripts served to corroborate and contrast the quantitative findings. The study revealed that the COVID-19 pandemic significantly altered consumer shopping patterns. There was a heightened demand for groceries, essentials, health and hygiene products, while non-essentials and luxury items experienced reduced demand. Consumers showed a preference for online retail platforms over traditional brick-and-mortar stores. Based on these findings, Saudi Arabian retailers are advised to exploit a hybrid model, blending online and physical stores in the post-COVID period, and to establish multiple online sales touchpoints. Luxury retailers, in particular, are encouraged to diversify their portfolio range to include value-for-money options, leverage social media marketing to promote their offerings and improve consumer perceptions around online purchasing, specifically in delivery and return policies.

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