Dynamic Optimal Pricing of Ridesharing Platforms under Network Externalities with Stochastic Demand

Ridesharing two-sided platforms link the stochastic demand side and the self-scheduling capacity supply side where there are network externalities. The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic demand with network externalities in order to maximize platforms’ revenue and social welfare. We propose dynamic pricing strategies under two demand scenarios that minimize order loss in the surge demand period and maximize social welfare in the declining demand period. The numerical simulation results show that dynamic pricing strategies could stimulate the supply to reduce delayed orders in the surge demand scenario and adjust the demand to maximize social welfare under declining demand scenario. Additionally, we further find that the direct network externalities positively influence the platforms’ revenue, and the indirect network externalities have a negative effect on social welfare in the declining demand scenario, and a higher wage ratio cannot enhance the platforms’ revenue.

Electrochemical product engineering towards sustainable recovery and manufacturing of critical metals

Critical metal products play an irreplaceable role in all aspects of modern society, however, uncertain supply risk pushed their sustainable recycling and manufacturing falling in the central focus. Compared to...

Retraction Note: The impact of supply chain quality management on firm performance: Empiri-cal evidence from Vietnam

The editors of Uncertain Supply Chain Management retract this article [1] based on the request made by authors since they had found some significant issues about the results which could mislead the readers.

Emissions Performance of Staged Premixed and Diffusion Combustor Concepts for an NH3/AIR Flame with and Without Reactant Humidification

Renewably generated ammonia offers a form of carbon-free chemical energy storage to meet the differences between uncertain supply and fluctuating demand and has the potential to support future energy requirements. The storage/transportation characteristics of NH3 are favourable compared with H2, however there are combustion research challenges to enhance fuel reactivity whilst reducing harmful emissions production. The purpose of this work was to evaluate different fuel delivery concepts for a representative GT combustor. An experimental and numerical comparison was made between swirl-stabilized premixed and diffusion NH3-air flames at elevated inlet temperature (473K). The exhaust NOx and NH3 emissions generated from each concept were quantified to optimize combustor performance. High-speed OH* and NH2* chemiluminescence was employed to characterize the change in flame topology with variation in fuel-air equivalence ratio, and the resultant influence on measured emission concentrations. Chemiluminescence intensities were shown to elucidate changes in sampled exhaust emissions, enabling detailed analysis of intermediate chemistry. A comparison was made between experimental data and kinetic simulations, demonstrating the sensitivity of NOx emissions to premixed fuel-air equivalence ratio. A comparison was also made between exclusive primary airflow, and the staged introduction of secondary air, to quantify the change in NOx production between each configuration and improve fuel burnout. Secondary air loadings were incrementally increased through the combustor. Finally, reactant humidification was employed as a secondary process for NOx reduction, having shown favourable performance with NH3-H2 mixtures, with the efficacy compared for both premixed and diffusion configurations.