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1. Laboratory Testing of Resilience Effects of Water Microgrids for Sustainable Water Supply

   https://doi.org/10.3390/su17083339  

   Ale_Magar, Binod; Hasnat, Arif; Ghanaatikashani, Amirmahdi; Acharya, Kriti; Shin, Sangmin (April 2025, Sustainability)

   Angelakis, Andreas (Ed.)

   Traditional centralized water systems are facing sustainability challenges due to climate and socioeconomic changes, extreme weather events, and aging infrastructure and their uncertainties. The energy sector has addressed similar challenges using the microgrid approach, which involves decentralized energy sources and their supply, improving system resilience and sustainable energy supply. This study investigated the resilience effects of water microgrids, which feature operational interactions between centralized and local systems for sustainable water supply. A lab-scale water distribution model was tested to demonstrate centralized, decentralized, and microgrid water systems under the disruption scenarios of pump shutdown, pump rate manipulation, and pipe leaks/bursts. The water microgrids integrate centralized and local systems’ operations, while the decentralized system operates independently. Then, functionality-based resilience and its attributes were evaluated for each disruption scenario. The results reveal that, overall, the microgrid configuration, with increased water supply redundancy and flexible operational adjustment based on system conditions, showed higher resilience, robustness, and recovery rate and a lower loss rate across disruption scenarios. The resilience effect of water microgrids was more evident with longer and more severe disruptions. Considering global challenges in water security under climate and socioeconomic changes, the findings suggest insights into a hybrid water system as a strategy to enhance resilience and water use efficiency and provide adaptive operations for sustainable water supply. 

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2. A two-stage stochastic aggregate production planning model with renewable energy prosumers,” in Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Virtual Conference, May 22-25, 2021, pp. 223-228.

   Islam, S.; Novoa, C.; Jin, T. (June 2021, Proceedings of the 2021 Institute of Industrial and Systems Engineers (IISE) Conference)

   Ghate, A.; Krishnaiyer, K.; Paynabar, K. (Ed.)

   This study presents a two-stage stochastic aggregate production planning model to determine the optimal renewable generation capacity, production plan, workforce levels, and machine hours that minimize a production system’s operational cost. The model considers various uncertainties, including demand for final products, machine and labor hours available, and renewable power supply. The goal is to evaluate the feasibility of decarbonizing the manufacturing, transportation, and warehousing operations by adopting onsite wind turbines and solar photovoltaics coupled with battery systems assuming the facilities are energy prosumers. First-stage decisions are the siting and sizing of wind and solar generation, battery capacity, production quantities, hours of labor to keep, hire, or layoff, and regular, overtime, and idle machine hours to allocate over the planning horizon. Second-stage recourse actions include storing products in inventory, subcontracting or backorder, purchasing or selling energy to the main grid, and daily charging or discharging energy in the batteries in response to variable generation. Climate analytics performed in San Francisco and Phoenix permit to derive capacity factors for the renewable energy technologies and test their implementation feasibility. Numerical experiments are presented for three instances: island microgrid without batteries, island microgrid with batteries, and grid-tied microgrid for energy prosumer. Results show favorable levelized costs of energy that are equal to USD48.37/MWh, USD64.91/MWh, and USD36.40/MWh, respectively. The model is relevant to manufacturing companies because it can accelerate the transition towards eco-friendly operations through distributed generation. 

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3. Co-Optimization of Supply Chain Reconfiguration and Assembly Process Planning for Factory-in-a-Box Manufacturing

   https://doi.org/10.1115/1.4054519  

   Nwodu, Arriana; Pasha, Junayed; Jiang, Zhengqian; Guo, Weihong; Dulebenets, Maxim; Wang, Hui; Minor, Kayla (October 2022, Journal of Manufacturing Science and Engineering)

   Abstract Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services. 

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4. Multi-Agent Artificial Intelligence to Self-Organize Machine Networks for Resilient Manufacturing

   https://doi.org/10.1115/MSEC2025-155761  

   Lee, Hankang; Yang, Hui (June 2025, American Society of Mechanical Engineers)

   Modern manufacturing is increasingly challenged by larger product varieties, shorter product life cycles, and unexpected production disruptions. Examples of such disruptions include market uncertainty, machine failures, and delivery backlogs. These disturbances are intricately interrelated, exacerbating system complexity and necessitating the adaptive organization (or re-configuration) of machine networks within factory layouts. However, traditional factory layouts are often stationary and lack the flexibility to rearrange or adjust machine networks in response to volatile markets and unexpected disruptions. Also, layout planning typically emphasizes offline design and configuration of machine networks and resources within a facility to optimize process flow and production performance, but tends to overlook the self-organizing arrangement of machines in a dynamic environment. Therefore, to address this gap, this paper presents a novel Self-organizing Machine Network (SOMN) model that optimizes the spatial layout of machine positions and queue configurations, thereby enhancing the manufacturing system’s resilience to unexpected disruptions. First, as opposed to traditional fixed machine positions, we design intelligent machine agents that communicate and autonomously reorganize in real-time to optimize key performance indicators (KPIs). Second, we develop the machine network model in a Digital Twin (DT) environment, facilitating cyber-physical interactions and capturing variations of state-action space in machine agents. Third, multi-agent reinforcement learning (MARL) algorithms empower these networked machine agents to adapt layouts and minimize the impact of disruptions on production performance. We evaluate and validate the proposed SOMN model through computer experiments, benchmarking it against random search and simulated annealing approaches. Experimental results show that the SOMN model significantly improves material handling efficiency, reduces computational overhead, and maintains productivity in different scenarios of manufacturing disruptions. This research holds strong potential for enabling distributed intelligence within self-organizing machine networks for resilient manufacturing. 

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5. Media coverage of a pandemic's impacts on farmers and implications for agricultural resilience and adaptation

   https://doi.org/10.5304/jafscd.2021.102.039  

   Jackson-Smith, Douglas; Veisi, Hadi (January 2021, Journal of Agriculture, Food Systems, and Community Development)

   null (Ed.)

   The COVID-19 crisis has revealed weaknesses and placed great stress on the agri-food system in the U.S. Many believe that it could be a catalyst event that leads to structural changes to improve the food system’s resilience. We use a sample of 220 articles published in prominent national newspapers and agricultural trade journals from March to May 2020 to explore the extent to which farmer responses to COVID-19 covered in the media represent examples of resistant, adaptive, or transformative strategies. The pandemic disrupted the U.S. food system and impacted farmers by reducing access to markets, lowering commodity prices, restricting access to farmworker labor, and shifting consumer demand. Media coverage of farmer responses to these stressors were coded into three alternative pathways: (i) reactive or buffering responses, (ii) adaptive responses; and (iii) transformative responses. Most news media coverage focused on the pandemic’s disruptive impacts on the U.S. food system, related negative impacts on farmers, and short-term responses by institutional actors, including policy-makers and food supply chain industry actors. Farmer responses to pandemic stressors were mentioned less frequently than farmer impacts and responses by institutional actors. The most common examples of farmer responses highlighted in the media reflected farmer reactive and buffering behaviors, which were mentioned significantly more frequently than adaptive or transformative responses. National newspapers were more likely to cover farmer responses and present examples of adaptive and transformative strategies compared to agricultural trade journals. Our findings suggest that news media coverage in the early months of the pandemic largely characterized the event as a rapid onset ‘natural’ disaster that created severe negative impacts. Media devoted more attention to short-term policy responses designed to mitigate these impacts than to farmer responses (in general) or to discussion of the deeper structural causes of and potential solutions to the vulnerabilities revealed by the pandemic. In this way, both national newspaper and agricultural trade journal coverage seems to promote frames that reduce the likelihood of the pandemic becoming the seed of a more resilient system. 

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