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The benefits of dynamic pricing methods have long been known in industries, such as airlines, hotels, and electric utilities, where the capacity is fixed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: (1) the increased availability of demand data, (2) the ease of changing prices due to new technologies, and (3) the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations.Dynamic Pricing;, E-Commerce;, Revenue Management;, Inventory

Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single "ensemble" forecast can increase the robustness of forecasts. Here we evaluate the real-time application of an open, collaborative ensemble to forecast deaths attributable to COVID-19 in the U.S. Methods Beginning on April 13, 2020, we collected and combined one- to four-week ahead forecasts of cumulative deaths for U.S. jurisdictions in standardized, probabilistic formats to generate real-time, publicly available ensemble forecasts. We evaluated the point prediction accuracy and calibration of these forecasts compared to reported deaths. Results Analysis of 2,512 ensemble forecasts made April 27 to July 20 with outcomes observed in the weeks ending May 23 through July 25, 2020 revealed precise short-term forecasts, with accuracy deteriorating at longer prediction horizons of up to four weeks. At all prediction horizons, the prediction intervals were well calibrated with 92-96% of observations falling within the rounded 95% prediction intervals. Conclusions This analysis demonstrates that real-time, publicly available ensemble forecasts issued in April-July 2020 provided robust short-term predictions of reported COVID-19 deaths in the United States. With the ongoing need for forecasts of impacts and resource needs for the COVID-19 response, the results underscore the importance of combining multiple probabilistic models and assessing forecast skill at different prediction horizons. Careful development, assessment, and communication of ensemble forecasts can provide reliable insight to public health decision makers.

Each year, about 500 natural disasters kill approximately 70,000 people and affect more than 200 million people worldwide. In the aftermath of such events, large quantities of supplies are needed to provide relief aid to the affected. CARE International is one of the largest humanitarian organizations that provide relief aid to disaster survivors. The most vital issues in disaster response are agility in mobilizing supplies and effectiveness in distributing them. To improve disaster response, a research group from Georgia Institute of Technology collaborated with CARE to develop a model to evaluate the effect that pre-positioning relief items would have on CARE's average relief-aid emergency response time. The model's results helped CARE managers to determine a desired configuration for the organization's pre-positioning network. Based on the results of our study and other factors, CARE has pre-positioned relief supplies in three facilities around the world.

Abstract-Recently auction methods have been investigated as effective, decentralized methods for multi-robot coordination. Experimental research has shown great potential, but has not been complemented yet by theoretical analysis. In this paper we contribute a theoretical analysis of the performance of auction methods for multi-robot routing. We suggest a generic framework for auction-based multi-robot routing and analyze a variety of bidding rules for different team objectives. This is the first time that auction methods are shown to offer theoretical guarantees for such a variety of bidding rules and team objectives. I. INTRODUCTIONRobot teams are increasingly becoming a popular alternative to single robots for a variety of difficult robotic tasks, such as planetary exploration or planetary base assembly. Robot teams offer many advantages over single robots: robustness (due to redundancy), efficiency (due to parallelism), and flexibility (due to reconfigurability). However, an important factor for the success of a robot team is the ability to coordinate the team members in an effective way. Coordination involves the allocation and execution of individual tasks through an efficient, decentralized mechanism.In this paper, we focus on multi-robot routing, a class of problems where a team of mobile robots must visit a set of locations for some purpose (e.g., delivery or acquisition) with routes that optimize certain criteria (e.g., minimization of consumed energy, completion time, or average latency). Examples include search-and-rescue in areas hit by disasters, surveillance of a facility, placement of sensors in a sensor network, delivery of parts, and measurements over a wide area. Such routing problems, including Vehicle Routing Problems (VRPs) and several variants of the Traveling Salesman Problem (TSP), have been widely studied from a centralized point of view in the operations research literature, and more recently in robotics with a focus on decentralized approaches.Even in decentralized multi-robot coordination, some information should be communicated between the robots to facilitate efficient performance; it is desirable to enable good decision making while communicating as little information as possible. One promising approach of this type is the use of market-based mechanisms, in particular, auction-based methods, where the communicated information consists of bids robots place on various tasks and coordination is achieved by a process similar to winner determination in auctions.The efficiency of auction-based methods has been demonstrated experimentally [1]-[9], but there has been little theoretical study [8]. In this paper we make the following contributions: (1) we suggest a generic framework for auctionbased multi-robot routing, and (2) we derive and analyze six bidding rules for three team objectives (minimizing total cost, maximum cost, or average service cost), specifically, we provide lower and upper bounds on their performance relative to optimal performance. This is the first time that auction...