In this paper, we study the two-level ski-rental problem,where a user needs to fulfill a sequence of demands for multiple items by choosing one of the three payment options: paying for the on-demand usage (i.e., rent), buying individual items (i.e., single purchase), and buying all the items (i.e., combo purchase). Without knowing future demands, the user aims to minimize the total cost (i.e., the sum of the rental, single purchase, and combo purchase costs) by balancing the trade-off between the expensive upfront costs (for purchase) and the potential future expenses (for rent). We first design a robust online algorithm (RDTSR) that offers a worst-case performance guarantee. While online algorithms are robust against the worst-case scenarios, they are often overly cautious and thus suffer a poor average performance in typical scenarios. On the other hand, Machine Learning (ML) algorithms typically show promising average performance in various applications but lack worst-case performance guarantees. To harness the benefits of both methods, we develop a learning-augmented algorithm (LADTSR) by integrating ML predictions into the robust online algorithm, which outperforms the robust online algorithm under accurate predictions while ensuring worst-case performance guarantees even when predictions are inaccurate. Finally, we conduct numerical experiments on both synthetic and real-world trace data to corroborate the effectiveness of our approach.

In this paper, we propose a decentralized, privacy-friendly energy trading platform (PFET) based on game theoretical approach - specifically Stackelberg competition. Unlike existing trading schemes, PFET provides a competitive market in which prices and demands are determined based on competition, and computations are performed in a decentralized manner which does not rely on trusted third parties. It uses homomorphic encryption cryptosystem to encrypt sensitive information of buyers and sellers such as sellers$'$ prices and buyers$'$ demands. Buyers calculate total demand on particular seller using an encrypted data and sensitive buyer profile data is hidden from sellers. Hence, privacy of both sellers and buyers is preserved. Through privacy analysis and performance evaluation, we show that PFET preserves users$'$ privacy in an efficient manner.

This paper outlines the ideas behind developing a graph-based heuristic-driven routing algorithm designed for a particular instance of a goods transportation problem with a single good type. The proposed algorithm solves the optimization problem of satisfying the demand of goods on a given undirected transportation graph with minimizing the estimated cost for each traversed segment of the delivery path. The operation of the routing algorithm is discussed and overall evaluation of the proposed problem solving technique is given. HE transportation problem is one of the well-known and hot topics both in mathematics and economics. It was first conceptualized by the French mathematician Gaspard Monge back in 1781 [1].

We're excited to announce that you can now measure the accuracy of your forecasting model to optimize the trade-offs between under-forecasting and over-forecasting costs, giving you flexibility in experimentation. Costs associated with under-forecasting and over-forecasting differ. Generally, over-forecasting leads to high inventory carrying costs and waste, whereas under-forecasting leads to stock-outs, unmet demand, and missed revenue opportunities. Amazon Forecast allows you to optimize these costs for your business objective by providing an average forecast as well as a distribution of forecasts that captures variability of demand from a minimum to maximum value. With this launch, Forecast now provides accuracy metrics for multiple distribution points when training a model, allowing you to quickly optimize for under-forecasting and over-forecasting without the need to manually calculate metrics.

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.

Real-time pricing (RTP) is an effective scheme for reducing peak demand, but it can lead to load synchronization , where a large amount of consumption is shifted from a typical peak time to a non-peak time, without reducing the peak demand. To address this issue, this paper presents a demand management method under RTP for the smart grid, that solves a large-scale of energy scheduling problem for households in an area. This is a distributed optimization method that finds the optimal consumption levels to minimize the total electricity cost while meeting the demands and preferences of households. Moreover, we propose to compute the probability distributions of start times for tasks, with which smart meters can quickly schedule tasks in practice, while matching the aggregate demand to the optimal consumption levels. The complexity of the optimization method is independent of the number households, which allows it to be applied to problems with realistic scales.