Crowdsourced on-demand services offer benefits such as reduced costs, faster service fulfillment times, greater adaptability, and contributions to sustainable urban transportation in on-demand delivery contexts. However, the success of an on-demand platform that utilizes crowdsourcing relies on finding a compensation policy that strikes a balance between creating attractive offers for gig workers and ensuring profitability. In this work, we examine a dynamic pricing problem for an on-demand platform that sets request-specific compensation of gig workers in a discrete-time framework, where requests and workers arrive stochastically. The operator's goal is to determine a compensation policy that maximizes the total expected reward over the time horizon. Our approach introduces compensation strategies that explicitly account for gig worker request preferences. To achieve this, we employ the Multinomial Logit model to represent the acceptance probabilities of gig workers, and, as a result, derive an analytical solution that utilizes post-decision states. Subsequently, we integrate this solution into an approximate dynamic programming algorithm. We compare our algorithm against benchmark algorithms, including formula-based policies and an upper bound provided by the full information linear programming solution. Our algorithm demonstrates consistent performance across diverse settings, achieving improvements of at least 2.5-7.5% in homogeneous gig worker populations and 9% in heterogeneous populations over benchmarks, based on fully synthetic data. For real-world data, it surpasses benchmarks by 8% in weak and 20% in strong location preference scenarios.

翻译：众包按需服务在按需配送场景中具有降低成本、缩短服务完成时间、提升适应性以及促进可持续城市交通等优势。然而，一个利用众包模式的按需平台的成功，关键在于找到一种补偿策略，能在为临时工作者创造有吸引力的报价与确保平台盈利之间取得平衡。本文研究了一个按需平台的动态定价问题，该平台在离散时间框架下为临时工作者设定针对具体请求的补偿，其中请求与工作者均随机到达。平台运营商的目标是确定一种补偿策略，以最大化整个时间范围内的总期望收益。我们的方法引入了明确考虑临时工作者请求偏好的补偿策略。为此，我们采用多项Logit模型来表示临时工作者的接受概率，并由此推导出一个利用后决策状态的解析解。随后，我们将此解整合到一个近似动态规划算法中。我们将我们的算法与基准算法进行比较，包括基于公式的策略以及由完全信息线性规划解提供的上界。基于完全合成数据的实验表明，我们的算法在不同设置下均表现出稳定的性能：在同质临时工作者群体中，相比基准算法实现了至少2.5-7.5%的性能提升；在异质群体中，提升幅度达到9%。对于真实世界数据，在弱位置偏好场景下，其性能超越基准算法8%；在强位置偏好场景下，超越幅度达到20%。