Single-period dynamic assortment planning involves the retailer's decision of selecting a set of products to offer and determining their initial inventory levels, considering stochastic demand and dynamic substitution. The objective is to maximize the expected revenue, subject to a capacity...

We present a new mixed-integer program for a multi-depot, multi-vehicle capacitated location-allocation routing problem (LARP). The model simultaneously selects the locations of facilities, allocates customers to the intermediate facilities, and identifies the optimal number of capacitated...

Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research on pricing algorithms shows that they can exhibit collusive behavior, but it has so far remained an open question whether they can do so in a reasonably short time. I develop a deep reinforcement...

Large e-commerce retailers and department stores function as marketplaces for millions of sellers, and consumers rely on the platform's assortment and display decisions to examine different sellers and make purchase decisions. Traditionally, the primary objective of these marketplaces for...

Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of...

The Weapon-Target Assignment (WTA) problem seeks to compute an optimal assignment of m weapons to n targets such that the expected total survival possibility of the targets is minimized (or equivalently, the expected total damage of the targets is maximized). The WTA problem is known to be...

Artificial Intelligence (AI) automates human decisions. Algorithmic pricing, a form of AI, sets prices by a computer. It is now common currency in ride-hailing, travel, drugs, gasoline, online goods---And great price variability characterizes all those settings. However, little is known about...

With algorithmic targeting getting increasingly common, the issue of fairness is quickly coming the fore. For example, financial firms have been accused of targeting minorities with pricey loans. Likewise, online job advertisements have disproportionately been targeted to male candidates,...