In the face of climate change, cities are becoming interested in developing policies and programs that will increase sustainability and resilience in their neighborhoods. In particular, government officials, planning agencies, and residents of the City of Des Moines, Iowa, would like to find ways to improve the energy efficiency of their urban built environment. Weatherization of residential buildings is one way of reducing energy consumption, particularly in winter months. While financial incentives might increase residents' adoption of weatherization measures, research has shown that social interactions more strongly influence this decision. To enable stakeholders to explore different scenarios for encouraging weatherization, a hybrid simulation model that integrates an urban energy model with an agent-based model has been developed to connect the physical processes of built environment systems with the goals, constraints, and interactions that drive resident behavior. This paper describes an application of the model to a specific residential city block.

Regional food hubs help to promote social, economic, and environmental sustainability by helping small-scale farmers distribute food to regional customers. To satisfy the diverse requirements of their entire customer base, food hubs must often work with a large number of farmers, resulting in prohibitive transportation costs. However, if they limit their supply base, they risk gaps in their offerings. To address this challenge, food hubs that are located in the same region can coordinate with one another to increase their supply base and reduce fuel consumption through shared infrastructure. In this paper we assess the value of coordination among four Iowa food hubs by examining their logistics networks and their customers’ product preferences. By analyzing their sales data, we demonstrate how cross-listing different regional products can result in increased business opportunities for the food hubs and farmers, as well as providing customers with greater variety. We propose the implementation of a standardized inventory tracking mechanism, which will enable efficient and effective supply chain coordination among the food hubs.

Regional food hubs aggregate, distribute, and market local food, with a goal of promoting environmental and social sustainability. They provide an alternative distribution channel through which small-scale producers can access wholesale markets. However, food hubs face many barriers to growth and success. In particular, they are often unable to achieve the logistical and operational efficiencies that characterize conventional large-scale food distribution. One possible method of improving food hub efficiency targets inbound logistics operations - specifically, the scheduling of producer deliveries to the food hub. In this paper, we describe a hybrid simulation model of the inbound logistics operations of a food hub. Using this model, we observe the scheduling behavior of the producers under different conditions and explore the effectiveness of implementing incentives to encourage producers to schedule their deliveries in advance.

This paper presents a novel modeling methodology that integrates the near building environmental conditions (or microclimate), whole-building design, and occupant behavior. Accurate predictions of the future building operating conditions lead to designs that serve the building’s purpose – to support occupants’ tasks. This study bridges the gap between human factors and architecture to include physical, cognitive, and organizational systems into building information modeling using future typical meteorological year climate data, canyon air temperature microclimate model, and a whole-building energy simulation to investigate the impact of future microclimate conditions on a “typical” single-occupant office. Additionally, to capture the effects of building occupant decision-making and adaptive behaviors, an agent-based model is proposed. Model inputs are task-based which aim to produce a more robust model to investigate a variety of human-building control interactions to ensure high building performance and occupant comfort and satisfaction.

Objective: Factors influencing long-term viability of an intermediated regional food supply network (food hub) were modeled using agent-based modeling techniques informed by interview data gathered from food hub participants.

Background: Previous analyses of food hub dynamics focused primarily on financial drivers rather than social factors and have not used mathematical models.

Method: Based on qualitative and quantitative data gathered from 22 customers and 11 vendors at a midwestern food hub, an agent-based model (ABM) was created with distinct consumer personas characterizing the range of consumer priorities. A comparison study determined if the ABM behaved differently than a model based on traditional economic assumptions. Further simulation studies assessed the effect of changes in parameters, such as producer reliability and the consumer profiles, on long-term food hub sustainability.

Results: The persona-based ABM model produced different and more resilient results than the more traditional way of modeling consumers. Reduced producer reliability significantly reduced trade; in some instances, a modest reduction in reliability threatened the sustainability of the system. Finally, a modest increase in price-driven consumers at the outset of the simulation quickly resulted in those consumers becoming a majority of the overall customer base.

Conclusion: Results suggest that social factors, such as desire to support the community, can be more important than financial factors.

Application: An ABM of food hub dynamics, based on human factors data gathered from the field, can be a useful tool for policy decisions. Similar approaches can be used for modeling customer dynamics with other sustainable organizations.

Food hubs aggregate and distribute regionally-produced food and help small and mid-sized farmers find markets for their products. They provide a valuable service as an intermediary between consumers and farmers who value sustainable food systems. However, their internal operations are often highly inefficient, and as a result they struggle to meet their customers’ expectations. To maintain low operational costs, food hubs often depend upon volunteer labor. However, an inconsistent and improperly trained volunteer labor force can lead to inventory and order fulfillment errors. Additionally, because many food hubs are unable to afford sophisticated inventory management software, they employ ad-hoc methods to track inventory and sales. This results in inadequate data collection to support decision making. In this paper we identify the operational challenges that regional food hubs commonly face, and we evaluate methods based on lean principles for overcoming these challenges, including standardized work procedures, process improvement mechanisms, and efficient data capturing techniques.

The recent increase in demand for regionally-produced food has resulted in a need for more efficient distribution methods. To connect regional food producers and consumers, intermediated regional food supply networks have developed. The intermediary, known as a regional food hub, serves as an aggregation point for products and information and may also act as a filter to ensure that the requirements of both producers and consumers are consistently met. This paper describes an empirically-based agent-based model of a regional food network in central Iowa that is intermediated by a food hub. The model was used to test a variety of producer selection policies that could be implemented by the food hub manager to improve operations. Results indicate that policies that protect producers from competition may have negative consequences for consumer satisfaction.

Creating sustainable, resilient cities requires integrating an understanding of human behavior and decision-making about the built environment within an expanding range of spatial, political, and cultural contexts. Resilience—the ability to survive from and adapt to extreme or sudden stresses—emphasizes the importance of participation by a broad range of stakeholders in making decisions for the future. Smart cities leverage technology and data collected from the community and its stakeholders to inform and support these decisions. Energy usage in cities starts with people interacting with their environments, such as occupants interacting with the buildings in which they live and work. To support city stakeholders as they develop policies and incentives for improved resilient energy utilization, researchers also need to consider microclimates and social dynamics in addition to building-occupant interactions. Sustainable design of the urban built environment therefore needs to expand beyond buildings to include near-building conditions. This requires investigating multiple scales and types of data to create new methodologies for design and decision-making processes. This paper presents a conceptual framework and interdisciplinary research methodology that integrates models and data-driven science with community engagement practices to create partnerships between university researchers, city officials, and residents. Our research team from design, natural sciences, data science, engineering, and the humanities presents a first example of a transformative method of data collection, analysis, design, and decision-making that moves away from hierarchical relationships and utilizes the expertise of all stakeholders.

This paper presents a development approach and design of a task-centered agent-based model (ABM) to represent the interactions of occupants with a commercial office building. The model is built with the understanding that occupant behaviour is driven by tasks the occupant performs. A contextual task analysis questionnaire explored occupant perspectives on the interactions between their tasks, their individual behaviour and comfort, and the physical characteristics of their workspace. This task-based information defines five ABM elements that represent occupants, task and workspace environment, task list, occupant actions, and the impact of the occupant-workspace interaction on tasks. An example of an occupant, performing a task, and conducting an action in response to an environmental mismatch demonstrates the ABM design. The example discusses the generation of possible actions as well as the result from those actions in terms of task performance and occupant satisfaction. As the ABM design evolves, it will aid in the understanding of occupant behaviour in buildings, and ultimately standardize the approach to occupant behaviours affecting building energy demand.

Consumer demand for regionally-produced food has increased dramatically in the past decade, in response to concerns over food safety and quality, as well as the serious environmental, economic, and social equity issues that pervade the modern industrial food supply system. As demand grows, distributors of regionally-produced food (i.e., food hubs) face many challenges in their efforts to scale up, particularly with supply chain management. They must find ways to operate more efficiently without undermining their support for the regional farming community. In this paper we describe a multi-agent simulation model of a theoretical regional food system in which multiple farmer-agents produce food, negotiate with a regional food-hub-agent, and try to sell their food at the best possible price. Using this model, we compare the effects of various food hub supplier selection policies on system outcomes, in terms of food hub profitability, the cost of quality, transaction costs, the number of farmers required to satisfy demand, and the size distribution of these farmers. Results suggest that the food hub’s selection policy significantly impacts long-term system performance.