An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System

摘要

In this paper,a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented.The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS.A scheduling process from 'initialized macro-scheduling' to 'repeated micro-scheduling' is designed for largescale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP).Under a set of theoretic strategies in the GPGP which is summarized in detail,E2GPGP is also proposed further.The GPGPcooperation-mechanism is simulated by using simulation software DECAF for the JSSP.The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness,but also reduces the resource cost.