The Online Reservation Problem

Abstract

Many sharing-economy platforms operate as follows. Owners list the availability of resources, prices, and contract-length limits. Customers propose contract start times and lengths. The owners decide immediately whether to accept or decline each proposal, even if the contract is for a future date. Accepted proposals generate revenue. Declined proposals are lost. At any decision epoch, the owner has no information regarding future proposals. The owner seeks easy-to-implement algorithms that achieve the best competitive ratio (CR). We first derive a lower bound on the CR of any algorithm. We then analyze CRs of all intuitive “greedy” algorithms. We propose two new algorithms that have significantly better CRs than that of any greedy algorithm for certain parameter-value ranges. The key idea behind these algorithms is that owners may reserve some amount of capacity for late-arriving higher-value proposals in an attempt to improve revenue. Our contribution lies in operationalizing this idea with the help of algorithms that utilize thresholds. Moreover, we show that if non-optimal thresholds are chosen, then those may lead to poor CRs. We provide a rigorous method by which an owner can decide the best approach in their context by analyzing the CRs of greedy algorithms and those proposed by us.

Digital Business Models in the Hospitality Sector: Comparing Hotel Bookings with Yacht Charter Bookings

“…Indeed, online channels have become crucial for booking tourist accommodation. Likewise, the sharing economy, as an important concept for tourism and hospitality, has grown substantially: in mid-2019, AngelList [5], a U.S. startup website, listed 1546 sharing economy startups with an average valuation of USD 3 million [6]. According to Botsman and Rogers [7,8], consumers are now willing to participate in the sharing economy economic concept and exchange all kinds of resources they own with strangers, including renting out their homes to tourists.…”