This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a large number of products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even if the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as other state-of-the-art methods, as empirical results show.

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We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem.

A startup is transforming polluted air into apparel. At least 15 major brands, including H&M and Walmart, are testing new technology for carbon neutral clothing. Breakthroughs, discoveries, and DIY tips sent six days a week. It might not seem like it when you nonchalantly click a Buy Now button while online shopping, but that new t-shirt is part of a complex global web of commerce taking a toll on the environment . Consulting giant McKinsey estimates that the fashion industry alone accounts for as much as 4 percent of total global climate emissions.

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With 49 patents and over 100 inventions, Henry built an empire catering to women and children. Beulah Louise Henry invented everything from ice cream makers to radio dolls--despite a world that didn't take her seriously. Breakthroughs, discoveries, and DIY tips sent six days a week. Beulah Louise Henry was just nine years old when she came up with her first invention in 1896, a device that allowed a man to tip his hat without ever putting down his newspaper. By her death in 1973, at the age of 85, she'd come up with so many more--a doll with eyes that changed color with the press of a button, a sewing machine without a bobbin (a threaded spool that slowed down work because it had to be frequently refilled), a clock designed to help kids learn to tell time, and others--that the press even dubbed Henry "Lady Edison."

After OpenAI's Instant Checkout feature fell short, Walmart is instead embedding its Sparky chatbot directly into ChatGPT and Google Gemini. Since November, Walmart has let some ChatGPT users order a limited selection of products without ever leaving OpenAI's chatbot interface. Sales have been disappointing, a Walmart executive vice president exclusively tells WIRED. The results suggest that a future where chatbots and AI agents take over ecommerce is still a way off, if it ever materializes. Last year, OpenAI made a bet that it could boost revenue by charging a commission on purchases made through ChatGPT.

Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction \alpha of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c / (1 - \alpha) for each item, but it is not the optimal solution on maximizing the profit of the intermediary.

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