A cognitive approach to real-time rescheduling using SOAR-RL

Ensuring flexible and efficient manufacturing of customized products in an increasing dynamic and turbulent environment without sacrificing cost effectiveness, product quality and on-time delivery has become a key issue for most industrial enterprises. A promising approach to cope with this challenge is the integration of cognitive capabilities in systems and processes with the aim of expanding the knowledge base used to perform managerial and operational tasks. In this work, a novel approach to real-time rescheduling is proposed in order to achieve sustainable improvements in flexibility and adaptability of production systems through the integration of artificial cognitive capabilities, involving perception, reasoning/learning and planning skills. Moreover, an industrial example is discussed where the SOAR cognitive architecture capabilities are integrated in a software prototype, showing that the approach enables the rescheduling system to respond to events in an autonomic way, and to acquire experience through intensive simulation while performing repair tasks.XIV Workshop agentes y sistemas inteligentes.Red de Universidades con Carreras en Informática (RedUNCI

Automated Task Rescheduling using Relational Markov Decision Processes with Logical State Abstractions

Generating and representing knowledge about heuristics for repair-based scheduling is a key issue in any rescheduling strategy to deal with unforeseen events and disturbances. Resorting to a feature-based representation of schedule states is very inefficient and generalization to unseen states is highly unreliable whereas the acquired knowledge is difficult to transfer to similar scheduling domains. In contrast, first-order relational representations enable the exploitation of the existence of domain objects and relations over these objects, and enable the use of quantification over objectives (goals), action effects and properties of states. In this work, a novel approach which formalizes the rescheduling problem as a Relational Markov Decision Process integrating first-order (deictic) representations of (abstract) schedule states is presented. The proposed approach is implemented in a real-time rescheduling prototype, allowing an interactive scheduling strategy that may handle different repair goals and disruption scenarios. The industrial case study vividly shows how relational abstractions provide compact repair policies with less computational efforts.Sociedad Argentina de Informática e Investigación Operativ

Improving iterative repair strategies for scheduling with the SVM

Gersmann K, Hammer B. Improving iterative repair strategies for scheduling with the SVM. Neurocomputing. 2005;63:271-292

Improving iterative repair strategies for scheduling with the SVM

Gersmann K, Hammer B. Improving iterative repair strategies for scheduling with the SVM. In: Verleysen M, ed. ESANN 2003, 10th European Symposium on Artificial Neural Networks. Proceedings. Evere: D-side publication; 2003: 235-240

Improving iterative repair strategies for scheduling with the SVM. Neurocomputing 63:271–292

Abstract. Resource constraint project scheduling (RCPSP) is an NPhard benchmark problem in scheduling which takes into account the limitation of resources ’ availabilities in real life production processes. We here present an application of machine learning to adapt simple greedy strategies. The rout-algorithm of reinforcement learning is combined with the support vector machine (SVM) for value function approximation. The specific properties of the SVM allow to reduce the size of the training set and show improved results even after a short period of training. 1