A ship-construction dataset for resource leveling optimization in large project management problems

Resource leveling is a highly complex optimization problem corresponding to adjusting a project's timeline (start and end dates) with the aim of matching resource allocation demands. The problem is particularly complex when a project is large and involves hundreds or even thousands of activities. Its successful solution is equivalent to considerable profits for the involved construction groups through the efficient management of their resources. In literature usually can be found only small-size benchmark problems consisting of a few activities (i.e., ten to twenty) mainly aiming to demonstrate that a new proposed method can operate correctly identifying the optimum (or a near-optimum) solution. This data article provides resource leveling data suitable for testing, corresponding to a very large real-world problem of ship construction (consisting of 1178 activities). According to recent literature, the majority of the proposed methods for solving resource-leveling optimization problems are based on algorithmic approaches, usually artificial intelligence-oriented (evolutionary programming). The reason is that intelligent approaches manage to solve complex problems, producing approximate solutions of high accuracy and thus attractive (profitable) for practical application. The provided data have been tested in the past with intelligent techniques using different evaluation functions. Nevertheless, the specific dataset has never been published before elsewhere and now there is a clear opportunity to provide these data for testing and benchmark experimentation to interested researchers.