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This paper presents a multi-agent structure and optimization algorithm to enhance energy utilization in a flexible jobshop environment under Time-of-Use (TOU) electricity pricing, in the presence of system changes. The proposed control structure based on multi-agent systems (MAS) takes into account re-scheduling in the workshop under TOU pricing. A multi-objective rescheduling model was developed to minimize due dates, makespan, total power consumption, and energy consumption costs. An improved genetic algorithm (GA) was designed to solve this model by converting the multi-objective into a single objective by optimizing each objective individually and combining the results. Simulation results demonstrate the effectiveness of the proposed algorithm and the application of the MAS structure.

A genetic algorithm (GA) with parallel chromosome coding scheme is proposed to solve the integrated modular product scheduling and manufacturing cell configuration problem in Reconfigurable Manufacturing Systems (RMSs). In the proposed GA, each chromosome contains two parallel sets of genes. The first set of genes represents job sequences and the second stands for cell configurations. The job sequence dependent work-inprocess (WIP) cost and cell configuration dependent manufacturing cost are minimized to enhance the performance of a RMS. The effectiveness of the approach is demonstrated by comparing the computational results with that achieved using LINGO and by applying it to both small size and large size problems. 0-7803-9363-5f05/$20.00 ©2005 IEEE.