Caroline C. Krejci

Modeling food supply chains using multi-agent simulation

In light of the pressures of increasing demands on earths resources, society faces serious challenges in food production and distribution. Food supply chain (FSC) models are critically important, providing decision-makers with tools that allow for the evaluation and design of FSCs, en route to ensuring sustainable FSC productivity. Multi-agent simulation (MAS) is well-suited to modeling FSCs for this purpose, enabling capture of decision-making, interactions, and adaptations of autonomous FSC actors. However, certain characteristics of FSCs are particularly difficult to model in detail, as data requirements can be intensive. In this paper we highlight some of the challenges modelers face in deciding the most appropriate methods for representing the elements of an FSC in an MAS model. We provide examples from the literature that show how other modelers have chosen to address these challenges. Finally, we discuss benefits and limitations of each examples approach, in terms of realism and data requirements.

Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling Integrating Financial and Social Drivers

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.

A hybrid simulation model of inbound logistics operations in regional food supply systems

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.

Hybrid simulation modeling for humanitarian relief chain coordination

Purpose – The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time. Design/methodology/approach – The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior. Findings – Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling. Research limitations/implications – This paper describes a framework for a h...

A hybrid simulation modeling framework for regional food hubs

As demand for regionally produced food has increased, regional food hubs have helped to facilitate connections between consumers and small-scale food producers. However, food hubs often struggle to achieve the logistical and operational efficiencies that characterize conventional large-scale food distribution. In many cases, implementation of innovations adopted by conventional food distributors has proved to be challenging and even counterproductive for food hubs, due to their distinct business structure and mission. To address this problem, an empirical agent-based and discrete-event hybrid simulation model was developed to determine the effects of incorporating various efficiency-enhancing practices into food hub warehousing operations. The model was validated using data from a food hub in central Iowa. Experimental results demonstrate the potential usefulness of this model in supporting food hub managers’ operational planning decisions, as well as the effectiveness of incorporating agent-based and discrete-event simulation modeling paradigms to study warehousing operations.

Best Practices for Engaging Underserved Populations

This work developed and applied a set of “best practices” when engaging marginalized populations to collect data, attitudes, and opinions around a research topic. To support city stakeholders making decisions to create more sustainable and equitable cities, data-driven simulation models are being developed. To ensure that these models are equitable, the needs of marginalized populations must be included. The challenge, however, in understanding these needs is that researchers have often struggled to reach and engage underserved populations. The best practices were developed by reviewing the literature from areas such as psychology, communication, and community planning. These best practices (Earn Trust Through Partnership, Be Multilingual & Inclusive, Communicate for Understanding, Respect Work Schedules and Cultural Norms, and Offer Something Useful) were then applied to the design of a data collection exercise for the study of weatherization decision making and behaviors of urban residents in an economically disadvantaged community. The results of the process were positive with high levels of participation and engagement. The use of the best practices allowed the researchers to better engage with the population, to the benefit of both groups. The development of these best practices will aid researchers in better engaging underserved populations across many areas of study.

Using hybrid simulation modeling to assess the dynamics of compassion fatigue in veterinarian general practitioners

Veterinarians have experienced disturbing trends related to workplace-induced stress. This is partly attributed to high levels of compassion fatigue, the emotional strain of unalleviated stress from interactions with those suffering from traumatic events. This paper presents a three-stage hybrid model designed to study the dynamics of compassion fatigue in veterinarians. A discrete event simulation that represents the work environment is used to generate client and patient attributes, and the veterinarians utilization throughout the day. These values become inputs to a system dynamics model that simulates the veterinarians interpretation of the work environment to produce quantifiable emotional responses in terms of eight emotions. The emotional responses are mapped to the Professional Quality of Life Scale, which enables the calculation of compassion satisfaction, burnout, and secondary traumatic stress measures. A pilot study using the hybrid model was conducted to assess the viability of the proposed approach, which yielded statistically significant results.

Energy Use and Weatherization Practices: Applications for Agent-Based Modeling to Support Vulnerable Populations

This work surveyed residents of an economically disadvantaged community on their attitudes toward weatherization and their energy use behaviors. To support urban leaders making decisions to mitigate the effects of large-scale climate change, data-driven simulation models are being developed. To ensure that these models are equitable, the needs of all citizens must be included, especially those most vulnerable to the impacts of climate change. The results of this survey indicate that residents are taking steps to weatherize and conserve energy, but they are hindered by a lack of resources and knowledge of available assistance programs. These results are being applied to agent-based models (ABM) to model emergent community behavior. These models will be used to design decision support systems for city stakeholders.

Incorporation of Future Building Operating Conditions into the Modeling of Building–Microclimate Interaction: A Feasibility Approach

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.