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.
Highlights
Popular web platforms, such as Airbnb and Turo, provide owners the ability to list resources available for sharing, prices, and contract-length limits
We refer to the owner’s problem as the online reservation problem (ORP) because our motivating application is the reservation of resources on sharing economy platforms
We do so because this paper is motivated by practical applications and an owner in a real-life setting will choose to use an algorithm for ORP/SORP only if the algorithm’s competitive ratio (CR) is small for his/her particular n and ∆, regardless of the limiting behavior of the CR
Summary
Popular web platforms, such as Airbnb and Turo, provide owners the ability to list resources available for sharing, prices, and contract-length limits. Market forces may result in a de facto upper bound on the contract length This may happen on platforms such as Getaround whose competitive advantage comes from offering short-duration contracts that are not offered by conventional rental agencies. We refer to the owner’s problem as the online reservation problem (ORP) because our motivating application is the reservation of resources on sharing economy platforms Even in those instances where the owners have access to some information about future requests, but the information is incomplete, it may be desirable to use an online algorithm because they are robust against forecast errors [8] and distributional misspecifications [9].
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