Self-Organizing Migrating Algorithm to Minimize Module Changes at Machine-Level in Reconfigurable Manufacturing
A reconfigurable manufacturing system (RMS) is designed at the outset with the capability of rapid adjustment of production capacity and functionality in response to fluctuations in product demand. This paper is presenting a model of RMS containing reconfigurable/modular machines assembled from sets of basic and auxiliary modules to exhibit two key characteristics: a defined range of functionality and scalable capacity. By suitable selection of modules, different operation capabilities with a varying degree of capacity can be developed. Products with alternative process plans and two discrete levels (low and high) of capacity requirements are considered for the modular machines. The objective of the work is to identify the best production sequence and respective process plans in order to minimize the total number of module changes while fulfilling the capacity constraint. Self-organizing migrating algorithm (SOMA) an evolutionary migration algorithm-based search is applied to find the near-optimal solution for the NP-hard combinatorial optimization problem. The approach is illustrated through a numerical problem along with computational results as applied to a hypothetical RMS model.
KeywordsReconfigurable machine tools Reconfigurable manufacturing system Modular machines Basic and auxiliary modules SOMA
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