The balance of autonomous and centralized control in scheduling problems

Abstract

The scheduling of processes in a network is a core logistic challenge with a multitude of applications in our complex industrialized world. Often, scheduling decisions are based on incomplete and unreliable information. Here, a simple rule of 'more information, better decisions' may no longer hold and heuristics balancing global and local information, or centralized and autonomous control, may yield better performance. So far, only anecdotal evidence for the potential benefit of autonomous control in scheduling exists. Here, we explore this hypothesis within a minimal model derived from scheduling principles and the phenomenology of dynamical processes on graphs. In this model, centralized and autonomous control can be represented and quantitatively assessed, performance is well defined and problem complexity can be varied. Our model shows that a balance of centralized and autonomous control can enhance the performance in networks of decision-making entities. The mechanistic insight gained from the model also reveals the limitations of hybrid control setups: We find that communication at a high hierarchy level can give an advantage to centralized control. Counter-intuitively, it arises not from a higher degree of coordination and quicker convergence towards a common solution, but rather from an accelerated sampling of candidate choices leading to a measurable increase in information flow from higher to lower hierarchical levels. Our study allows us to formulate a new view of autonomous control in industrial production and derive a set of suggestions with the potential to enhance performance under realistic conditions of scheduling heuristics of jobs in a production process.

A Framework for Lane-Change Maneuvers of Connected Autonomous Vehicles in a Mixed-Traffic Environment

“…In a complex environment where multiple vehicles interact with each other, decentralized control might yield a situation where decisions will likely be based on incomplete information. On the other hand, centralized control that combines global information in the system may yield a superior overall performance [27]. Typically, centralized control problems involve multiple constraints, and MPC has been shown to be effective for solving motion planning problems with multiple constraints [28][29][30][31][32].…”

Predictable topological sensitivity of Turing patterns on graphs

“…Theoretical studies reveal, how such patterns are enhanced by certain architectural features of the network [22][23][24] and transdisciplinary investigations help understand the implications of such patterns for network-like infrastructures [25][26][27][28][29][30][31].…”