We consider the problem of optimal assortment design for a platform by imposing fairness constraints that guarantees exposures of all products to consumers. We show that optimal solution can be found in polynomial time and the algorithm is easy to implement in practice. We investigate the welfare change of imposing the fairness constraints and find that it is possible to achieve a win-win-win solution where the platform, sellers, and consumers are all better off.

Problem: Allocating perishable resources online with non-linear utilities and complementarities. Methodology/Results: An algorithm achieves optimal envy-efficiency Pareto frontier by adapting to perishing order predictions and desired envy bounds. Numerical performance is demonstrated using data from a food bank. Managerial Implications: Accurate perishing predictions are crucial for fairness in practical non-profit settings.

We study the marketplace platform's problem that decides the assortment of sellers and how much fee to apply to each seller. In our model, each seller has its own outside option, which depends on customer traffic and product preference. The seller's compensation is determined based on the competition with the other joining sellers. We characterize the platform's optimal fee and assortment decisions and further study how the different fee contracts affect the optimal assortment.