Surges In Demand Research Articles

Fourth-party logistics network design with demand surge: A greedy scenario-reduction and scenario-price based decomposition algorithm

The sharing economy has offered new viable approaches for firms to tackle demand surges. In this paper, we study a novel network design problem for the fourth-party logistics network (4PLN) to cope with unpredictable demand surges, in which temporary renting of logistics resources from a sharing market is available to supplement network capacity. A chance-constrained stochastic programming model is established to minimize the overall cost for 4PLN under service level targets. To deal with the difficulties brought by the evaluation of service level, we reformulate a mixed-integer linear programming (MILP) model, that can be solved straightly, based on the sample average approximation method. Then, to address the enormous challenge posed by the coupling of the basic NP-hard network design problem and the large number of demand scenarios in the MILP version, the Scenario-Price based Decomposition Algorithm (P-DA) is designed based on the key idea of decomposing the above-coupled factors. Further, to mitigate the performance deterioration brought on by large system scale and/or sample size, we expand our base algorithm to the Greedy Scenario-Reduction and Scenario-Price based Decomposition Algorithm (GR&P-DA) through the fast processing of chance constraints by introducing a greedy method. Through computational experiments, we demonstrate the effectiveness of our proposed model and algorithms, and we analyze the impact of various model parameters, including demand level, surge magnitude, surge probability, and rental price of third-party logistics resources, on 4PLN design. Furthermore, our comparative analysis reveals that renting logistics resources to address demand surges can significantly reduce costs and discovers that deploying resources in advance does not always provide an advantage, a temporary “case-by-case” planning approach for renting will give a more cost-saving scheme when surge probability is at a low level as it avoids idle resources by not being stuck with a conservative strategy.

Selective lithium extraction from brine via chemical reduction of iron phosphate with aqueous iron compounds

Lithium production has increased with surges in demand. Iron based redox mediators were developed to extract Li+ from brine, showing promise using brines with low amounts of lithium and high concentrations of competing ions.

Design and optimization of a pharmaceutical supply chain network under COVID-19 pandemic disruption

Efficient management of pharmaceutical supply chains during the COVID-19 pandemic is critical to mitigate material and human losses. This paper addresses the design of a pharmaceutical supply chain network under pandemic disruption, introducing a novel bi-objective mathematical model. Traditional supply chain management strategies often fall short in the face of sudden disruptions, necessitating the development of resilient systems. Our model seeks to minimise economic costs and shortages as primary objectives, addressing the specific challenge of sudden surges in demand for pharmaceuticals. To enhance resilience, we propose solutions including the establishment of temporary distribution points and the creation of backup inventory. We employ a scenario-based, discrete, and linear modelling approach, solving the model using goal-planning methods and validating its efficacy through numerical examples. Additionally, we conduct a case study in the metropolitan area of Mashhad, further demonstrating the applicability and effectiveness of our approach. This research contributes to the advancement of resilient supply chain design in the pharmaceutical sector, offering insights that can inform improved management practices and bolster resilience in pharmaceutical supply chains.

Every Step Counts: Importance of Final ALD Sequences for Oxygen Evolution Reaction Catalysts

In achieving the global dream of a sustainable future, the development of new and alternative methods to generate electricity are of high importance. Current renewable energy sources are mainly dependent on sun and wind, and are thus very unpredictable. To obtain an overall reliable energy supply, we need to be able to store clean energy for extended periods of time to compensate for unfavorable weather conditions or sudden surges in energy demand.One way to store energy is by generating H2-gas, which has a set of interesting advantages as an energy carrier molecule. It has a very high energy density, is multifunctional as feedstock or energy carrier and it is not a greenhouse gas in itself. The production efficiency of green H2, however, still needs to be optimized before it can be adapted on a global scale. Electrochemical water splitting is mainly hampered by the sluggish kinetics of the oxygen evolution half-reaction (OER) of the process. This involves a transfer of four electrons, each with its own intermediary product(s) requiring catalysis, resulting in a significant overpotential[1].Atomic layer deposition (ALD) could be an interesting technique in the research, development and possibly production of such OER catalytic materials. This technique is suited to deposit thin films in a controlled layer-by-layer way with dimension control up to the Angström level. In this development, transition metals could be interesting catalyst candidates due to their availability and pricing over tradition OER catalysts based on noble metals [2]. Both oxides and phosphate variants have been explored; the latter occasionally outperforming the former[3][4].Nickel has shown to be one of the more OER-active transition metals, and doping or combining it with iron seems to further increase its performance [5]. Inspired by these results, ALD was used in this work to deposit and finetune the properties of a Ni-Fe ternary phosphate for its use as an OER-catalyst material. This material was deposited by alternating ALD cycles of Ni- and Fe-phosphate (denoted here as NiPO and FePO) based on previous works of Rongé et al.[6] and Henderick et al.[7].Ni-Fe phosphates with varying composition and thickness were deposited on a solid nickel disc and tested in a rotating-disc electrode (RDE) set-up to quantify their electrochemical performance. Applying a coating with a selected composition of 4 NiPO to 1 FePO cycles improves the performance over a bare Ni-PVD substrate (Fig 1). A current density of 91 mA/cm2 at 0.685 V vs Hg/HgO is obtained for this material which is 75% higher than the uncoated nickel disk (51 mA/cm2) which indicates that the material has catalytic properties for the OER increasing the production of H2 at an identical overpotential. An additional observation was that the pulsing order of the two sub-processes within an ALD super-cycle has an important impact on the resulting layer’s performance. If the FePO sub-cycle is pulsed last, the current density increases further to approx. 150 mA/cm2 for a total increase of 200% compared to the uncoated performance.The results of this explorative work in the use of ALD for the development of OER catalytic materials indicate that it is a promising technique due to the precise control that can be exerted over both the thickness and composition of the material. Specifically the option to exert high control over the surface of the catalyst can be a big asset in researching catalytic materials.

Stockpiling at the Onset of the COVID-19 Pandemic: An Empirical Analysis of National Prescription Drug Sales and Prices

At the onset of the coronavirus (COVID-19) pandemic, hospitals experienced a demand surge for COVID-19–related medical care while simultaneously struggling to source prescription drugs needed to manage COVID-19 patients. This is worrisome as shortfalls in the supply of essential drugs can negatively impact patient outcomes. The popular press reporting on these challenges suggests that they are caused by supply chain disruptions. However, rigorous research on the relationship between the pandemic and prescription drug supply is limited. To address this gap, we leverage a quasi-experimental design and IQVIA’s National Sales Perspectives™ data from 2018 to 2020. We focus on prescription drugs indicated for the management of COVID-19 patients and a set of control drugs (i.e., drugs not used for COVID-19). We find that in the early phases of the pandemic, hospitals stockpiled prescription drugs indicated for the management of COVID-19, making this behavior an under-recognized cause of the sourcing challenges. The sales volume of drugs indicated for COVID-19 management was concentrated in the first two months of the pandemic, after which sales decreased significantly despite a nationwide increase in COVID-19–related hospitalizations. We investigate another potential cause of stockpiling: expected price increases. Counter to concerns of price gouging, we find little evidence of price inflation for these drugs. An implication for drug manufacturers is that orders due to stockpiling by downstream buyers early on in a pandemic may need to be discounted when predicting future demand. Our results have implications for drug suppliers, hospitals, and policymakers interested in improving medical product supply chain resilience. This paper was accepted by Stefan Scholtes, healthcare management. Funding: R. M. Conti was supported by the National Science Foundation (Grant 1123649-463157) and the Sloan Foundation. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.04150 .

Residential electricity demand on CAISO Flex Alert days: a case study of voluntary emergency demand response programs

Abstract The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response (DR) tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the efficacy of CAISO Flex Alerts have produced inconsistent results and do not investigate how participation varies across sectors, regions, population demographics, or time. Evaluating the efficacy of DR tools is difficult as there is no ground truth in terms of what demand would have been in the absence of the DR event. Thus, we first define two metrics that to evaluate how responsive customers were to Flex Alerts, including the Flex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and the Ramping Response, which estimates changes in demand during the first hour of the Flex Alert period. We then analyze the hourly load response of the residential sector, based on ∼200 000 unique homes, on 17 Flex Alert days during the period spanning 2015–2020 across the Southern California Edison (SCE) utility’s territory and compare it to total SCE load. We find that the Flex Period Response varied across Flex Alert days for both the residential (−18% to +3%) and total SCE load (−7% to +4%) and is more dependent on but less correlated with temperature for the residential load than total SCE load. We also find that responsiveness varied across subpopulations (e.g. high-income, high-demand customers are more responsive) and census tracts, implying that some households have more load flexibility during Flex Alerts than others. The variability in customer engagement suggests that customer participation in this type of program is not reliable, particularly on extreme heat days, highlighting a shortcoming in unincentivized, voluntary DR programs.

Supply Chain Resilience Enhancement Strategies in the Context of Supply Disruptions, Demand Surges, and Time Sensitivity

Supply chain disruptions, including trade disputes, regional conflicts, and the COVID-19 pandemic, have highlighted the importance of supply chain resilience. This study focuses on investment decisions related to supply chain resilience, including considerations of supply disruptions, demand disruptions, and demand time sensitivity. The study uses a Stackelberg game model, with the retailer as the leader and the supplier as the follower, to analyze and compare optimal strategies for supply chain participants under different levels of risk. The results show that the severity of risk events impacts the optimal retail price, optimal wholesale price, supply chain recovery time, and expected profits for each participant. The investment in supply capacity enhancement not only expands the ability to meet demand but also encourages suppliers to expedite supply chain recovery, elevate demand fulfillment rates, and enhance the overall return on investment in supply chain resilience. As a result, the resilience of the entire supply chain is fortified. Similarly, it has been found that centralized decision-making leads to lower prices, shorter recovery times and greater supply chain resilience than decentralized decision-making. This study offers theoretical underpinning for corporations to effectively address risky occurrences, ensure supply security, enhance supply chain resilience, and imparts support for governments involved in humanitarian relief efforts.

Critical Department Analysis for Large-Scale Outpatient Systems

Identifying critical department(s) to enhance the supply in large-scale systems is beneficial to support the system efficiency. Large-scale outpatient systems (LSOSs) are usually faced with crowdedness and, hence, require critical departmental improvement to maintain patient satisfaction under a limited budget. Besides, when demand surges (DSs) or physicians are absent supply loss (SL), critical department identification becomes one of the key steps to resilient clinical management. To improve the clinical services and mitigate the risk of disruptions efficiently, we conduct critical department analysis under three scenarios: one clinical improvement scenario supply enhancement (SE) and two clinical disruption scenarios (DS and SL). These scenarios can happen in different departments in varying time sessions (e.g., am or pm). We define the criticality of a department as the change of patient satisfaction with respect to the change of departmental supply and demand. We accordingly propose a simulation-based ranking method and implement a case study in an LSOS. The simulation results show that the criticality of the department highly depends on the time session. Surprisingly, SE may reduce patient satisfaction when the supply increases in several specific departments. Key findings and managerial insights are further discussed.

Production planning with flexible manufacturing systems under demand uncertainty

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%.

Agile producers and consumer-saviours: Discourses of resilience and responsibility in Australian media coverage of artisanal food and craft

COVID-19's supply chain disruptions saw small-scale, artisanal food and craft producers experience surges in demand from consumers seeking locally made goods. This article analyses Australian news coverage promoting this ‘turn to the local’, with a focus on mainstream news outlets from March 2020 to February 2023. We identify two dominant narratives: the ‘producer pivot’ and the ‘consumer-saviour’. Using Rosalind Gill and Shani Orgad’s (2018) work on resilience as a regulatory ideal of neoliberalism, we argue that both narratives focus on individual responsibility in ways that make invisible structural and economic impediments to change. The consistent ways in which buying and producing local small-scale goods were presented and understood in the news coverage – across different products, places and stages of the pandemic – highlights the persistent ways in which neoliberal values perform particular kinds of work for capitalism by asserting the necessity of local ‘resilience’ and ‘positivity’ in times of crisis.

Consumer Valuation of Thanksgiving Items and the Role of Organic Certifications

AbstractPrevious research links organic purchasing motivations to personal and family health. We conduct a national survey to explore whether this preference intensifies when preparing family meals, especially during Thanksgiving. We find that approximately 83% of consumers change their consumption habits for Thanksgiving, with a notable preference for organic products. Results from the choice experiment indicate willingness to pay premiums for Thanksgiving-themed items, especially those with USDA-certified organic or certified naturally grown labels. These findings underscore policy initiatives that strengthen consumer understanding of organic certifications and support producers in securing them, capitalizing on the seasonal demand surge.

Building health system resilience in and through service delivery

Abstract Introduction The ability of a health system to deliver services during and in the aftermath of a shock depends on the capacity of all other health system functions (governance, health workforce, physical infrastructure and finances). Countries have differing service delivery approaches (e.g., more or less emphasis on outpatient vs. hospital care), which influences how they respond to a shock. However, despite the differences in health system organisation of service delivery and the type of shock, there are common approaches that countries can take to increase the resilience of service delivery and deal with fluctuations in demand and supply. Methods A framework for understanding the impact of a shock on service delivery was developed by the European Observatory on Health Systems and Policies, and used as an analytical tool to examine the relationship between service delivery and resilience. Findings Shocks can impact the demand for and supply of services in different ways. For example, an epidemic/pandemic shock such as Covid-19 may lead to surges in demand and crowd out routine care, while a war or conflict shock (e.g., Ukraine since February 2022) may lead to (local) supply shortages as well as surges in demand, necessitating setting up new infrastructure and transferring of patients. However, health systems also responded to shocks in ways that have increased the resilience of service delivery. These include flexible decision making, innovations in digital health and better planning and management across care settings. Conclusions Flexible policies that can increase health service supply flows, rather than one-off increases, are needed to respond to unexpected changes. Furthermore, it is the linking of the impact of shocks on service delivery that ultimately influences the final health system goal of improved health. Finally, shocks can impact service delivery even after a shock, with the opportunity to transform into a more efficient and resilient health system.

A semi-distributed charging strategy for electric vehicle clusters

Uncoordinated charging of electric vehicle (EV) clusters can lead to unanticipated surges in demand that strain the grid, hence reducing its reliability. To address this challenge, this paper proposes a semi-distributed control method for EV clusters charging where the decision-making process takes place locally, rather than relying on an external controller. The method ensures that charging demand can be fulfilled while achieving several goals, including following local renewable energy generation, pausing charging for transformer protection, and continuing charging progress in the absence of external information. The proposed method is validated through simulations and experimental tests during a public demonstration. The results demonstrate the functionality of the control method and show that it can effectively manage the charging of a cluster of EVs while reducing strain on the power grid.

A Review of Long-Term Effects of Mineral Fertilizers on Soil Microorganisms

The intricate relationship between soil microbiomes and fertilizers is central to the sustainable future of agriculture. This review delves deep into the multifaceted interactions between soil microorganisms and varying fertilizer regimens, shedding light on their immediate and long-term impacts. Soil, teeming with a rich diversity of bacteria, fungi, archaea, protozoa, and viruses, plays a pivotal role in agricultural productivity. These microbial communities are intricately linked to soil health, fertility, and resilience. Fertilizers, while essential for enhancing crop yields, have shown varied impacts on these microbial communities. Immediate post-application dynamics reveal shifts in microbial diversity and abundance, with potential cascading effects on soil processes. Longitudinal studies in real-world agricultural settings underscore the resilience and adaptability of these communities in the face of disturbances. Advanced technological tools, from metagenomics to IoT devices, offer unprecedented insights and real-time monitoring capabilities. The convergence of biology with technology holds the promise of a future where agricultural practices are fine-tuned based on real-time microbial feedback, ensuring both enhanced yields and sustained soil health. As we stand at the crossroads of a global food demand surge and environmental sustainability, understanding and harnessing the potential of soil microbiomes becomes paramount. This review underscores the need for robust, long-term research endeavors to chart a path for truly sustainable agriculture.

Park Ziyaretçilerinin Sosyo-Demografik Özellikleri ile Park Kullanımı Arasındaki İlişkinin Doğrusal Olmayan Kanonik Korelasyon Analizi ile İncelenmesi: Konya Örneği

Modern cities have seen urban parks evolve from mere green spaces into integral components supporting human well-being and environmental sustainability. These parks offer individuals serene settings to unwind and reconnect with nature, promoting mental health by alleviating stress. However, as urbanization increases and public park demand surges, designing and planning these spaces becomes more challenging. To optimize park design, understanding user profiles and utilization patterns is crucial. By examining how visitor demographics intertwine with park usage, policymakers and planners can make informed decisions for future development.
 This study employs Nonlinear Canonical Correlation Analysis (NLCC), a robust statistical tool, to explore the intricate connections between socio-demographic attributes of urban park visitors and their usage patterns. Focused on Konya, Turkey, the research delves into the non-linear relationships between socio-demographic characteristics and park usage. Survey data from 444 participants forms the basis of the analysis, unveiling inherent dataset structures and assessing correlations between attributes and usage patterns.
 Results highlight the substantial impact of occupation, income, and age as determinants of socio-demographic characteristics affecting park use at an individual level. These findings hold significant implications for urban park planning and management. The research underscores the nuanced interplay between socio-demographic factors and usage patterns, offering targeted strategies for diverse visitor needs. As urban landscapes evolve, this study contributes to refining policies and practices that cultivate sustainable, equitable, and enjoyable park experiences for urban communities.

Enhanced Method for Emergency Scheduling of Natural Gas Pipeline Networks Based on Heuristic Optimization

Safety and disturbance issues in system engineering have garnered substantial attention. This study focuses on the analysis of the distinct characteristics of emergency dispatch problems in Natural Gas Pipeline Networks (NGPS). Graph theory serves as a tool to transform the NGPS topology and establish an optimization model for NGPS emergency dispatch. The model also integrates user weights, satisfaction, and reduction factors into the user modeling approach. Its objective is to maximize overall system satisfaction while considering factors such as demand-side requirements and operational constraints. To solve this optimization model, the Particle Swarm Optimization (PSO) method is employed. An in-depth exploration of four unique disturbance scenarios provides solid evidence of the effectiveness and practicality of the PSO method. Compared to other methods, the PSO method consistently boosts overall user satisfaction and aligns more fluidly with the real-time demands of emergency scheduling, regardless of reduced supply capacity, complete supply interruptions, sudden surges in user demand, or pipeline connection failures. The developed emergency scheduling optimization method presents two key advantages. Firstly, it proficiently mitigates potential losses stemming from decreased supply capacity at local or regional levels. By adeptly adjusting natural gas supply strategies, it minimizes economic and production losses while ensuring a steady supply to critical users. Secondly, the method is superior at swiftly reducing the affected area and managing the increased demand for natural gas, thus maintaining NGPS stability. This research underscores the importance of considering user characteristics and demands during emergencies and demonstrates the effectiveness of employing the PSO method to navigate emergency scheduling challenges. By strengthening the resilience of the pipeline network and ensuring a sustainable natural gas supply, this study constitutes a significant contribution to energy security, economic development, and the promotion of clean energy utilization, ultimately propelling the achievement of sustainable development goals.

Fabrication of ZnSnO3@In2O3 core-shell based 1D microfiber heterostructure for high energy density asymmetric supercapacitors

Core-shell microfibers have witnessed a substantial demand surge in developing supercapacitors (SCs) owing to their enhanced conductivity, high surface area, and structural stability. Here, we report a facile low-cost electrospun ZnSnO3 @In2O3 core-shell microfibers heterostructure for SC application. The distinct fibrous morphology and synergistic incorporation of Zinc (Zn), Tin (Sn), and Indium (In) demonstrate significantly enhanced electrochemical performance while assembled in an asymmetric SC (ASC) by utilizing ZnSnO3 @In2O3 core-shell as positive and activated carbon (AC) as negative electrode. The ASC device exhibits an excellent specific capacitance of 160 F/g at 1 A/g along with a maximum specific energy of 40 Wh/kg. The ASC also records 86.95% capacitance retention with an exceptional 90% coulombic efficiency retention over 15,000 cycles at 15 A/g. This novel ZnSnO3 @In2O3 core-shell//AC signifies great potential in achieving high-performance electrochemical energy storage devices.

RIde-hail vehicle routing (RIVER) as a congestion game

The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-hail market form a dynamic routing strategy according to the expected reward in each zone of the market. We model this decision-making problem as a Markov decision process (MDP), and view the drivers as playing an MDP routing game, with “congestion” induced by competitive matching in a zone. The meeting probability (i.e., the probability of successfully picking up a passenger after cruising in a zone for one period) is derived from a physical model, which is specified for street-hail and e-hail service modes separately, and calibrated with simulation data. We define a Wardrop equilibrium for the MDP routing game, and then prove it exists and can be obtained by solving a fixed-point problem. We further show a system optimum – corresponding to the maximum total expected reward accumulated over time by all drivers – can be achieved if drivers make individual routing decisions according to a cooperative reward rather than the personal reward. In other words, the proposed cooperative strategy “decentralizes” the system optimal solution in the MDP routing game. The results from numerical experiments, including a case study of Chicago, indicate the service mode plays a critical role in shaping system performance. While e-hail enjoys a higher fleet utilization rate than street-hail thanks to its more advanced matching technology, it may lead to a more uneven spatial distribution of vacant vehicle supply. As expected, cooperative routing improves system performance in terms of both total reward and equal distribution of supply. Yet, its benefit is much stronger in e-hail than in street-hail, especially when the supply is overly abundant. We also find, when a local demand surge occurs, the cooperative rewards rise in sync, similar to surge pricing on the supply side. Interestingly, the impact of the price surge spreads broadly in space, propagating far beyond the epicenter of the demand surge.

Frequency and Trends of Pre-Pandemic Surge Periods in U.S. Emergency Departments, 2006-2019.

To quantify the frequency, outside of the pandemic setting, with which individual healthcare facilities faced surge periods due to severe increases in demand for emergency department (ED) care. Retrospective cohort study. U.S. EDs. All ED encounters in the all-payer, nationally representative Nationwide Emergency Department Sample from the Healthcare Cost and Utilization Project, 2006-2019. None. Frequency of surge periods defined as ED months in which an individual facility ED saw a greater than 50% increase in ED visits per month above facility-/calendar month-specific medians. During 2006-2019, 3,317 U.S. EDs reported 354,534,229 ED visits across 142,035 ED months. Fifty-seven thousand four hundred ninety-five ED months (40.5%) during the study period had a 0% to 50% increase in ED visits that month above facility-specific medians and 1,952 ED months (1.4%) qualified as surge periods and had a greater than 50% increase in ED visits that month above facility-specific medians. These surge months were experienced by 397 unique facility EDs (12.0%). Compared with 2006, the most proximal pre-pandemic period of 2016-2019 had a notably elevated likelihood of ED-month surge periods (odds ratios [ORs], 2.36-2.84; all p < 0.0005). Compared with the calendar month of January, the winter ED months in December through March have similar likelihood of an ED-month qualifying as a surge period (ORs, 0.84-1.03; all p > 0.05), while the nonwinter ED months in April through November have a lower likelihood of an ED-month qualifying as a surge period (ORs, 0.65-0.81; all p < 0.05). Understanding the frequency of surges in demand for ED care-which appear to have increased in frequency even before the COVID-19 pandemic and are concentrated in winter months-is necessary to better understand the burden of potential and realized acute surge events and to inform cost-effectiveness preparedness strategies.

Optimal pricing strategy of an on-demand platform with cross-regional passengers

Recent years have witnessed the emergence and prosperity of online ride-hailing, which empowers the traditional taxi industry with information technology. However, the ride-hailing platform has no direct control over drivers, one of the biggest challenges for the platform is how to balance the supply and the demand. Considering the cross-regional demand and the surge demand, this paper focuses on the pricing strategy of the ride-hailing platform, and further introduces strategies such as bonus incentive and information sharing. We find that when facing demand surging and supply shortage in one region, the platform can adopt surge pricing or bonus incentive in the adjacent region before the demand surges to balance the supply and the demand. Specially, the surge pricing strategy benefits the platform when the surge demand is high or the cross-regional demand is low, but the bonus incentive strategy has the opposite effect. The existence of cross-regional demand improves the performance of the bonus incentive strategy but reduces that of the surge pricing strategy. We further show that the platform prefers bonus incentive to surge pricing when only a small number of drivers are required to move. But bonus incentive always improves social welfare better than surge pricing. Extending to information asymmetry, information sharing can also encourage more drivers to move. However, the effect is limited such that the proportion of drivers who move will not exceed a threshold. The platform also has the incentive to share false information, which makes drivers question the credibility of the shared information. In this case, the surge pricing strategy can be used as a costly signal to verify the credibility of the information.