Aerial Swarm Defense by StringNet Herding: Theory and Experiments

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

Competence-based assignment of tasks to workers in factories with demand-driven manufacturing

The paper is motivated by real problems concerning tasks assignment to workers in medium-sized upholstered furniture plants managed using the Demand-Driven Manufacturing. Although the methodology was developed for furniture plants it can be applied to other types of production plants. We involve competence coefficients, which describe the level of the worker’s skills or capabilities to perform a specific task. The competence coefficients are also used to block the possibility of assigning the given task to a worker that has no skills to do it. Additionally, we involve a dummy worker to the model which guarantees the existence of a solution to the problem. We present and discuss Integer Linear Programming Models for the posted problem that are closely related to the Generalized Assignment Problem. We also discuss the potential use of the presented methodology to solve real-life problems related to production management.