Hospital and nursing leaders should provide disaster training opportunities that included topics such as compiling resource inventories, formulating disaster drills and simulations, managing emergencies, and using emergency communication methods. Health policy-makers should be required to prioritize capacity-building training for front-line nurses as well as to develop and implement disaster management plans to better prepare nurses for future disasters.

This study discusses a dual‐channel supply chain in which a manufacturer sells a regular‐priced product through dual channels in the normal sales period and only sells overstocked products through the direct channel in the discounted sales period in the presence of strategic consumers. The manufacturer acts as a Stackelberg leader to adopt a preannounced pricing policy. This study first proposes demand functions for a two‐period dual‐channel model by incorporating consumer utility functions. Based on the demand functions, optimal pricing strategies for both manufacturer and retailer are established. The results show that the manufacturer prefers to raise prices in both periods for consumers with a short delivery lead time. However, counterintuitively, the selling prices set by the manufacturer do not decrease as the degree of consumer patience increases. Finally, there is a Pareto zone under a certain condition where both the manufacturer and the retailer in the two‐period dual‐channel model outperform their counterparts in terms of profit in the single‐period dual‐channel model.

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As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.

Problem definition: Shared micromobility vehicles provide an eco-friendly form of short-distance travel within an urban area. Because customers pick up and drop off vehicles in any service region at any time, such convenience often leads to a severe imbalance between vehicle supply and demand in different service regions. To overcome this, a micromobility operator can crowdsource individual riders with reward incentives in addition to engaging a third-party logistics provider (3PL) to relocate the vehicles. Methodology/results: We construct a time-space network with multiple service regions and formulate a two-stage stochastic mixed-integer program considering uncertain customer demands. In the first stage, the operator decides the initial vehicle allocation for the regions, whereas in the second stage, the operator determines subsequent vehicle relocation across the regions over an operational horizon. We develop an efficient solution approach that incorporates scenario-based and time-based decomposition techniques. Our approach outperforms a commercial solver in solution quality and computational time for solving large-scale problem instances based on real data. Managerial implications: The budgets for acquiring vehicles and for rider crowdsourcing significantly impact the vehicle initial allocation and subsequent relocation. Introducing rider crowdsourcing in addition to the 3PL can significantly increase profit, reduce demand loss, and improve the vehicle utilization rate of the system without affecting any existing commitment with the 3PL. The 3PL is more efficient for mass relocation than rider crowdsourcing, whereas the latter is more efficient in handling sporadic relocation needs. To serve a region, the 3PL often relocates vehicles in batches from faraway, low-demand regions around peak hours of a day, whereas rider crowdsourcing relocates a few vehicles each time from neighboring regions throughout the day. Furthermore, rider crowdsourcing relocates more vehicles under a unimodal customer arrival pattern than a bimodal pattern, whereas the reverse holds for the 3PL. Funding: This work was supported by the Research Grants Council of Hong Kong [Grants 15501319 and 15505318] and the National Natural Science Foundation of China [Grant 71931009]. Z. Jin was supported by the Hong Kong PhD Fellowship Scheme. Y. F. Lim was supported by the Lee Kong Chian School of Business, Singapore Management University [Maritime and Port Authority Research Fellowship]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1199 .