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Motivated by a widely practiced strategy to combine the growth of the solar energy sector with poverty mitigation, we propose stylized models of households selling extra solar energy back to the grid, which generates a steady stream of income to overcome adoption barriers for solar panels, that is, high adoption cost and generation variability. By considering households' strategic consumption shifting behavior in response to varying intertemporal market electricity prices, we have presented the equilibrium adoption number and households' equilibrium profit. We have also demonstrated the government's optimal subsidy to reach a socially optimal adoption level. Furthermore, we investigate the popular Public Private Partnership (PPP) model in developing countries to promote the investment of solar panels. Under the PPP program, the private firms share both upfront cost and electricity revenue with households, while the government may provide subsidy to further encourage adoption. Despite the popularity of the PPP scheme, our model documents an unexpected negative implication by inducing a lower overall adoption level than the traditional scheme: As the PPP scheme relieves budgetary burden of households in adopting solar panels (infrastructure cost), anticipating the long‐term revenue loss, they are less likely to participate in the first place. However, combined with the government intervention via adoption subsidy, the PPP scheme can lead to a win‐win solution for all. [ABSTRACT FROM AUTHOR]

Alibaba Group pioneered integrated online and offline retail models to allow customers to place online orders of e-commerce and grocery products at its participating stores or restaurants for rapid delivery—in some cases, in as little as 30 minutes after an order has been placed. To meet these service commitments, quick online routing decisions must be made to optimize order picking routes at warehouses and delivery routes for drivers. The solutions to these routing problems are complicated by stringent service commitments, uncertainties, and complex operations in warehouses with limited space. Alibaba has developed a set of algorithms for vehicle routing problems (VRPs), which include an open-architecture adaptive large neighborhood search to support the solution of variants of routing problems and a deep learning-based approach that trains neural network models offline to generate almost instantaneous solutions online. These algorithms have been implemented to solve VRPs in several Alibaba subsidiaries, have generated more than $50 million in annual financial savings, and are applicable to the broader logistics industry. The success of these algorithms has fermented an inner-source community of operations researchers within Alibaba, boosted the confidence of the company's executives in operations research, and made operations research one of the core competencies of Alibaba Group. [ABSTRACT FROM AUTHOR]

Problem definition: Many cities worldwide are embracing electric vehicle (EV) sharing as a flexible and sustainable means of urban transit. However, it remains challenging for the operators to charge the fleet because of limited or costly access to charging facilities. In this paper, we focus on answering the core question—how to charge the fleet to make EV sharing viable and profitable. Academic/practical relevance: Our work is motivated by the setback that struck San Diego, California, where car rental company car2go ceased its EV-sharing operations. We integrate charging infrastructure planning and vehicle repositioning operations that were often considered separately. More interestingly, our modeling emphasizes the operator-controlled charging operations and customers' EV-picking behavior, which are both central to EV sharing but were largely overlooked. Methodology: Supported by the real data of car2go, we develop a queuing network model that characterizes how customers endogenously pick EVs based on energy levels and how the operator implements a charging-up-to policy. The integrated queuing-location model leads to a nonlinear optimization program. We then propose both lower and upper bound formulations as mixed-integer second-order cone programs, which are computationally tractable and result in a small optimality gap when the fleet size is adequate. Results: We learn lessons from the setback of car2go in San Diego. We find that the viability of EV sharing can be enhanced by concentrating limited charger resources at selected locations. Charging EVs either in a proactive fashion or at the 40% recharge threshold (rather than car2go's policy of charging EVs only when their energy level drops below 20%) can boost the profit by more than 15%. Moreover, sufficient charger availability is crucial when collaborating with a public charger network. Increasing the charging power relieves the charger resource constraint, whereas extending per-charge range or adopting unmanned repositioning improves profitability. Finally, we discuss how EV sharing operations depend on the urban spatial structure, compared with conventional car sharing. Managerial implications: We demonstrate a data-verified and high-granularity modeling approach. Both the high-level planning guidelines and operational policies can be useful for practitioners. We also highlight the value of jointly managing demand fulfillment and EV charging. [ABSTRACT FROM AUTHOR]

Cellulose nanofibrils (CNFs) were exposed to the same levels of potassium chloride (KCl) and sodium chloride (NaCl) before being subjected to spray drying. The effect of NaCl and KCl on the size of atomized droplets and the hydrogen bond retardation between CNFs was investigated by characterizing product morphology, particle size distribution, dispersion stability in aqueous system, and surface chemistry. The results showed that the CNF suspensions treated with KCl could be atomized into smaller droplets during spray drying, and then CNF powder with smaller sizes could be obtained. As the agglomeration was less, and the CNF with KCl addition had good dispersion stability after redispersion compared with CNF treated by NaCl. Therefore, KCl treatment was an effective method to reduce the agglomeration of CNF during spray drying. [ABSTRACT FROM AUTHOR]