In the process of steelmaking and continuous casting production, an optimized ladle schedule will greatly reduce energy consumption and improve production. The ladle scheduling problem can be modeled as vehicle routing problem with time windows (VRPTW) and extra constraints. The main extra constraint is that components of the ladle, at the right time, have to be repaired no later than serving certain number of heats. The objective of ladle scheduling problem is minimizing the number of serving ladles and reducing the waiting time between serving two adjacent heats. According to the serving process of ladles, a mathematical model is established to solve this specific problem. In this paper, a three-step heuristic algorithm with time complexity of O(n 2 ) is proposed, which is based on characteristics of the model and some preliminary experiments. The algorithm has been tested by several practical instances from a steel plant in China. Comparing with the schedules used in actual production, the computational results show that our algorithm optimizes the ladle schedules and solves the problem in less than 1 second, which proves the algorithm's efficiency.

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Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering true support and scalability with large datasets. The current theoretical analysis of IHT assumes the use of centralized IID data. In realistic large-scale scenarios, however, data are distributed, seldom IID, and private to edge computing devices at the local level. Consequently, it is required to study the property of IHT in a federated environment, where local devices update the sparse model individually and communicate with a central server for aggregation infrequently without sharing local data. In this paper, we propose the first group of federated IHT methods: Federated Hard Thresholding (Fed-HT) and Federated Iterative Hard Thresholding (FedIter-HT) with theoretical guarantees. We prove that both algorithms have a linear convergence rate and guarantee for recovering the optimal sparse estimator, which is comparable to classic IHT methods, but with decentralized, non-IID, and unbalanced data. Empirical results demonstrate that the Fed-HT and FedIter-HT outperform their competitor—a distributed IHT, in terms of reducing objective values with fewer communication rounds and bandwidth requirements.