Yanlu ZHOU - ESSEC Business School
September 02nd, 12pm - 1pm / room will be communicated soon - Campus Cergy
in the food processing times. Traditional methods in this field include greedy (online matching) and batching (matching in periodic intervals) policies. We introduce a new level-k policy to delay the assignment until we have no more than k matching options left, and we show numerically that this policy performs exceedingly well on real data. Numerical experiments based on a real dataset from Meituan show that our policy can reduce total costs by 42% compared to matching policies that are currently in use. To explain the advantage of the level-k policy, we evaluate its performance on a simplified model and derive several analytical properties - the total costs are quasiconvex in the level of market thickness, implying that an intermediate level of thickness is optimal. We also find that the food processing time is key information for the matching decisions. Finally, our findings indicate that if the unit delay penalty cost is high, the decision-maker of the platform should match conservatively.