Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.

Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from surveys on local evacuation expectations and evacuation drills into an agent-based model of evacuation behavior for two Cascadia subduction zone (CSZ) communities that would be inundated within 20–40 min after a CSZ earthquake. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from least-cost distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and the evacuation participation rate have significant nonlinear impacts on tsunami mortality estimates. When people walk faster than 1 m s−1, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and the beat-the-wave (BtW) model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans.

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Disasters vary in many characteristics, but their amount of forewarning-the amount of time remaining until the disaster strikes-is a crucial factor affecting the dissemination of emergency warnings. People can be warned by public safety officials through broadcast channels, such as commercial TV and radio, that transmit simultaneous warnings to mass audiences. In addition, however, warnings are also transmitted by peers through informal warning networks that operate through contagion from one person to another. This paper establishes an interdisciplinary agent-based model with Monte Carlo simulations to assess the relative effects of these broadcast and contagion processes in a multiplex social network. This multiplex approach models multiple channels of informal communication-phone, word-of-mouth, and social media-that vary in their attribute values. Each agent is an individual in a threatened community who, once warned, has a probability of warning others in their social network using one of these channels. The probability of an individual warning others is based on their warning source and the time remaining until disaster impact, among other variables. We model warning dissemination using simulation parameter values chosen from empirical studies of disaster warnings along with the spatial aspects of the Coos Bay, OR, USA and Seaside, OR, USA communities. Results indicate that the initial broadcast size has a negative correlation with the critical percolation threshold, which varies from approximately 1-5%, depending on the size of an initial broadcast. A sensitivity analysis on the model parameters indicates that, along with initial broadcast size and sharing probability, forewarning and confidence in the warning significantly affect the total number of warning recipients. The results generated from this study identify areas for future research and can inform community officials about the effects of event and community characteristics on the dissemination of emergency warnings in their communities.

Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from local evacuation expectations surveys and evacuation drills into an agent-based model of evacuation behavior for a Cascadia Subduction Zone community. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from Least-cost-distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and evacuation participation rate have significant non-linear impacts on tsunami mortality estimates. When people walk faster than 1 m/s, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and BtW model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans.