English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Over the past few years, ride-sharing has emerged as an effective way torelieve traffic congestion. A key problem for these platforms is to come upwith a revenue-optimal (or GMV-optimal) pricing scheme and an induced vehicledispatching policy that incorporate geographic and temporal information. Inthis paper, we aim to tackle this problem via an economic approach.  Modeled naively, the underlying optimization problem may be non-convex andthus hard to compute. To this end, we use a so-called "ironing" technique toconvert the problem into an equivalent convex optimization one via a cleanMarkov decision process (MDP) formulation, where the states are the driverdistributions and the decision variables are the prices for each pair oflocations. Our main finding is an efficient algorithm that computes the exactrevenue-optimal (or GMV-optimal) randomized pricing schemes. We characterizethe optimal solution of the MDP by a primal-dual analysis of a correspondingconvex program. We also conduct empirical evaluations of our solution throughreal data of a major ride-sharing platform and show its advantages over fixedpricing schemes as well as several prevalent surge-based pricing schemes.

Optimal Vehicle Dispatching Schemes via Dynamic Pricing