Robust and Adaptable Job Shop Scheduling Using Multiple Agents: Further Experimental Justification

The work presented in this paper is a continuation for efforts to devise a complete multi-agent framework for dynamic job shop scheduling, which considers robustness and adaptability. Previous work reported in N. Liu, et al. (2004) and N. Liu et al. (2005), gave a theoretical basis and experimental justification of the framework. Computational experiments on dynamic job arrivals were provided for the experimental justification. It was concluded that the framework worked very good and stably both in static job shop scheduling and dynamic job shop scheduling with unpredictable job arrivals, and robustly in dynamic job shop scheduling with unpredictable job arrivals. The framework inherits the advantages of decentralized models, such as flexibility, robustness, and high fault tolerance. The framework is actually a job dispatching procedure - a completely reactive scheduling approach combining real time decision making with predictive decision-making based on optimization. It can solve various disruptions as flexibly as dispatching rules. This paper presents a further experimental justification of the arguments about the framework using computational experiments on processing time variations. By computational results, it is to show the effectiveness and efficiency of the framework and the effects of making full use of available information of disruptions on the framework