Herein, we report distal amidoketone
and nitrogen-functionalized
ketone syntheses from alcohols and N–H nucleophiles enabled
by hypervalent iodine dimethyl benziodoxoles, BIm. Dimethyl
benziodoxoles BIm dually activate alcohols and various
N–H nucleophiles by forming key BIm–O and
BIm–N complexes in which the BIm–N
complex is characterized by X-ray crystallography and computationally
investigated. Readily available N–H nucleophile imides, sulfonamides,
carbamates, triazoles, indazoles, and sulfoximines engage in photoredox/copper
catalysis to synthesize distal amidoketones and nitrogen-functionalized
ketones with excellent regioselectivity and chemoselectivity. This
reaction scales up to grams, applies to late-stage complex molecule
modification, and streamlines synthetic routes.
We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected profit over a finite horizon by matching the inventory level with a random demand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm’s sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is [Formula: see text], where T is the number of periods, and ϵ is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and show that the proposed data-driven algorithm solves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms. This paper was accepted by J. George Shanthikumar, big data analytics.
Motivated by logistical problems faced by a large supply chain software company, the paper, “A New Approach for Vehicle Routing with Stochastic Demand: Combining Route Assignment with Process Flexibility,” studies a vehicle routing problem where some routes are allowed to overlap. The paper proposes a class of simple and effective strategies to design overlapped routes with customer sharing, which combines ideas from the process flexibility in manufacturing and traditional vehicle routing literatures. Through theoretical analysis and numerical simulations, the paper illustrates the advantage of an overlapped routing strategy with a small amount of customer overlaps. In particular, it shows that such a strategy can provide consistent route assignments to drivers, while achieving a similar expected travel distance as the theoretical benchmark in the fully reoptimized setting. The strategy is in contrast to the traditional fixed routing strategy, which provides consistent route assignments to drivers, but incurs a much higher expected travel distance.
We study an inventory placement optimization problem where demand is sensitive to service response time, under the online retailing setting. The main challenge is to achieve the optimal trade‐off between revenue benefits from shorter delivery time and the increase in inventory cost associated with placing inventory closer to market demand. To predict the effects of modified demand under service response time variations, we introduce a demand prediction and elasticity model to quantify the sensitivity in demand for particular product categories. Of course, shortening response time by positioning products close to market demand may increase inventory costs. Hence, the team developed a novel data‐driven two‐stage stochastic programming approach complementing the demand prediction and elasticity model, which optimally trades safety stock with service response time and hence revenue increase. We then illustrate the impact of our approach through data provided by an e‐commerce retailer in North America. Our approach offers supply chain managers a general‐purpose decision support tool that optimizes the inventory network to generate recommended stocking levels for stores, distribution centers, and warehouses on a daily basis.
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