This paper is concerned with the data collaboration (DC) analysis, a privacy-preserving method for analyzing decentralized datasets held by multiple parties. In this method, privacy-preserving intermediate representations of original datasets are collected from multiple parties and then converted into collaboration representations for collaborative data analysis. However, conventional methods for creating collaboration representations suffer from several challenges; namely, the optimization problem being considered is not well defined, and the process of solving it is very difficult to understand. We thus propose a new solution for creating high-quality collaboration representations for the DC analysis. Specifically, we formulate a revised optimization problem for creating collaboration representations and then transform this optimization problem into a generalized eigenvalue problem. We also propose a reduction of the generalized eigenvalue problem to a singular value decomposition through the QR decomposition. Computational experiments using publicly available datasets demonstrate that our method can outperform the conventional methods for the DC analysis in terms of both prediction accuracy and computational efficiency. • Privacy-preserving data collaboration for analyzing decentralized datasets. • Generalized eigenvalue problem for high-quality collaboration representations. • Reduction of the generalized eigenvalue problem to a singular value decomposition. • Superiority of our method evaluated through computational experiments.

In one-way station-based shared mobility systems, where system users share vehicles for making trips between vehicle stations, the accumulation of one-way trips inevitably causes vehicle imbalances between stations. To correct these imbalances, we focus on the effective use of linear control policies for calculating online vehicle relocations from a history of user trips. Our scenario-based optimization model for computing linear control policies is formulated as a linear optimization problem. Computational results using a real-world dataset demonstrate that our method provides high relocation performance with short online computation times.

This paper is concerned with a feature subset selection problem for the multinomial logit (MNL) model. There are several convex approximation algorithms for this problem, but to date the only exact algorithms are those for the binomial logit model. In this paper, we propose an exact algorithm to solve the problem for the MNL model. Our algorithm is based on a mixed-integer optimization approach with an outer approximation method. We prove the convergence properties of the algorithm for more general models including generalized linear models for multiclass classification. We also propose approximation of loss functions to accelerate the algorithm computationally. Numerical experiments demonstrate that our exact and approximation algorithms achieve better generalization performance than does an L1-regularization method.

The cost estimation process, carried out by the contractor before the start of a project, is a critical activity for the contractor in accepting profitable EPC projects in competitive bidding situations. Thus, the contractor should devote significant time and resources to the accurate cost estimation of project orders from clients. However, it is impossible for any contractor to devote enough time and resources to all the orders because such resources are usually limited. For this reason, the contractor must dynamically decide bid or no-bid on the orders at each order arrival, and allocate the limited resources to the chosen orders. To maximize the contractor's profits, this study devises a heuristic scheduling method for dynamically selecting orders and allocating the limited resources to them, on the basis of the resource requirement of the order, the contractor's resource utilization, and the expected profit from the order. The effectiveness of our method is demonstrated through simulation experiments using a project cost estimation process model.