In this paper, we present Alkaid-SDVRP, an open-source C++ package for efficiently solving the Vehicle Routing Problem with Split Deliveries (SDVRP), a classical combinatorial optimization problem which is a variant of the Capacitated Vehicle Routing Problem where the same customer can be served by multiple vehicles. The core algorithm of Alkaid-SDVRP is designed based on the Iterated Local Search and Randomized Variable Neighborhood Descent frameworks, which are highly configurable and extensible. Specifically, we implement a number of predefined neighborhoods, including Swap(p, q), [Formula: see text], SD-[Formula: see text], Cross, Exchange, and Reinsertion, which can be arbitrarily enabled, disabled, and permuted. Moreover, it is easy to develop and integrate new neighborhoods into the current framework. The primary goal of this package is to provide an effective implementation and integration of the state-of-the-art techniques for the SDVRP. Tested on the 12th Implementation Challenge held by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Alkaid-SDVRP took first place in the SDVRP track and has been shown beyond any doubt. In addition, we hope that the package can facilitate the research for vehicle routing-related problems by providing high-quality baselines and off-the-shelf implementations. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: Financial support from the National Natural Science Foundation of China [Grant 72101094]; the Special Project for Knowledge Innovation of Hubei Province [Grant 2022013301015175]; and Interdisciplinary Research Program of Huazhong University of Science and Technology [Grant 5003300129] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0606 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0606 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

With the diversification of cloud services, cloud providers offer flexible access control by letting users apply fine-grained cloud access control policies to secure their cloud resources. However, flexibility comes with the cost that configuring cloud access control policies is error-prone. Therefore, cloud providers have developed SMT-based tools to formally analyze the user-defined policies. Unfortunately, we find these analyzers slow, due to the complex regular expression matching conditions in policies. To this end, this paper introduces Relia, a general method to speed up the analysis of cloud access control policies. The key idea of Relia is to pre-compute a set of String Equivalence Classes (SECs) based on the regular expressions in a policy, assign a unique integer to each SEC, and rewrite the regular constraints into equivalent integer constraints, which are easier to solve. We implement Relia as a transparent layer between our in-house access analyzer and off-the-shelf SMT solvers. Based on real policies from a large public cloud provider, we show that: when enabling Relia, our in-house portfolio solver (consisting of Z3, Cvc4, and Cvc5) can speed up the analysis process for nearly 95% of all cases, with an average speedup of 8.21×.

Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, Song-Chun Zhu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.