【主题】概率产品收益管理策略Choice-based Revenue Management for Probabilistic Goods
【摘要】Probabilistic selling provides a new dimension to segment a market and benefits a seller from price discrimination. This paper extends the idea behind probabilistic selling to a more general setup, by developing a menu of probabilistic goods (PGs) based on multiple physical products. In particular, the seller has a fixed inventory level for each physical products at the beginning of a finite selling horizon. Faced with sequential customer arrivals, the seller dynamically controls the offering of multiple PGs in order to maximize expected revenue. Incorporating customer choice model, we formulate the seller’s problem as a continuous-time, discrete-state, finite-orizon dynamic program (DP). Due to the complexity of solving this multi-dimensional DP, we resort to heuristics. We first study the deterministic version of the problem (i.e., the fluid control problem) and develop a time-based fluid policy. The policy is shown to be asymptotically optimal for the original stochastic problem. Further, the static nature of the fluid policy and its lack of flexibility in matching supply with demand motivate us to develop decomposition approximation which generates a dynamic control policy. Numerical experiments are conducted to evaluate the performance of the two heuristics and the benefits of selling a menu of PGs.