Deadlock Avoidance for Multiple Tasks in a Self-Organizing Production Cell

Abstract

Deadlocks represent situations in which two participants are waiting for each other to finish an activity so that neither of them will ever finish. Deadlocks can occur in complex computer-integrated systems, such as flexible and selforganizing production systems. As deadlocks bring production to halt, methods for deadlock control in production systems are widely studied. Yet most algorithms proposed are not suited for the use in decentral multi-agent systems, as they require central control or can not handle concurrency. Other algorithms can be used in a decentral fashion but assume that only one type of product will be manufactured at a time. But in times of mass customization, where customers choose a product from a variety of options, support for several product types is required. To meet both the requirements of mass customization and decentral multi-agent systems, we present a new decentralized approach for avoiding deadlocks in a self-organizing production cell, where several types of products are being manufactured in parallel. Our approach is based solely on local knowledge and does not assume central control. We evaluate our approach in terms of effectiveness and message overhead to conclude that it avoids starvation and deadlocks with a reasonable message overhead.

Distributed Constraint Optimization for Task Allocation in Self-Adaptive Manufacturing Systems

“…One limitation of our model is that it cannot produce assignments where an agent receives products it has already received before. While this modeling prevents deadlocks, it also restricts the feasible system configurations severely as it rules out cyclic configurations [21]. In our following evaluation, we additionally assume that all stationary agents like robots and storages are connected to all carts but not to other stationary agents.…”