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/ .

The optimal camera placement problem (OCP) aims to accomplish surveillance tasks with the minimum number of cameras, which is one of the topics in the GECCO 2020 Competition and can be modeled as the unicost set covering problem (USCP). This paper presents a weighting-based variable neighborhood search (WVNS) algorithm for solving OCP. First, it simplifies the problem instances with four reduction rules based on dominance and independence. Then, WVNS converts the simplified OCP into a series of decision unicost set covering subproblems and tackles them with a fast local search procedure featured by a swap-based neighborhood structure. WVNS employs an efficient incremental evaluation technique and further boosts the neighborhood evaluation by exploiting the dominance and independence features among neighborhood moves. Computational experiments on the 69 benchmark instances introduced in the GECCO 2020 Competition on OCP and USCP show that WVNS is extremely competitive comparing to the state-of-the-art methods. It outperforms or matches several best performing competitors on all instances in both the OCP and USCP tracks of the competition, and its advantage on 15 large-scale instances are over 10%. In addition, WVNS improves the previous best known results for 12 classical benchmark instances in the literature.

The p-center problem consists of choosing p centers from a set of candidates to minimize the maximum cost between any client and its assigned facility. In this paper, we transform the p-center problem into a series of set covering subproblems, and propose a vertex weighting-based tabu search (VWTS) algorithm to solve them. The proposed VWTS algorithm integrates distinguishing features such as a vertex weighting technique and a tabu search strategy to help the search to jump out of the local optima. Computational experiments on 138 most commonly used benchmark instances show that VWTS is highly competitive comparing to the state-of-the-art methods in spite of its simplicity. As a well-known NP-hard problem which has already been studied for over half a century, it is a challenging task to break the records on these classic datasets. Yet VWTS improves the best known results for 14 out of 54 large instances, and matches the optimal results for all remaining 84 ones. In addition, the computational time taken by VWTS is much shorter than other algorithms in the literature.

An UBIQUEST system provides a high level programming abstraction for rapid prototyping of heterogeneous and distributed applications in a dynamic environment. Such a system is perceived as a distributed database and the applications interact through declarative queries including declarative networking programs (e.g. routing) and/or specific data-oriented distributed algorithms (e.g. distributed join). Case-Based Reasoning is used for optimization of distributed queries when as there is no prior knowledge on data (sources) in networking applications, and certainly no related metadata such as data statistics.