Optimal crowdsourcing contracting for reconfigurable process planning in open manufacturing: A bilevel coordinated optimization approach
Abstract Recent advances in the industrial Internet of Things and smart data analytics have empowered companies to shift towards an open manufacturing paradigm to crowdsource and share manufacturing resources based on demand and capacities across the value chain. Reconfigurable process planning (RPP) reconstructs the optimal process planning for each batch according to different part batches based on commonality. In combination with the optimal crowdsourcing contracting (OCC) strategy, RPP helps release fixed capital and increase the flexibility of the enterprise under the requirement of diversified parts. The coupling of process planning and crowdsourcing contracting decisions is inherent in open manufacturing. This paper focuses on coordinated optimization underlying OCC with RPP. An OCC decision making mechanism considering RPP based on Stackelberg game theory is established, which provides a solution for trade offs between the RPP decision maker and the OCC decision maker. A coordinated optimization model is proposed to reveal the hierarchical relationships and is solved by a nesting genetic algorithm. A case study of a group of rotating shaft parts is taken to illustrate the effectiveness of the bilevel coordinated optimization (BCO) model. The results present that the commonality has an apparent impact on the OCC and RPP decisions in open manufacturing, and integrating it into process planning activities is advisable for platform enterprises to increase production efficiency and competitive advantages. Our proposed model can deal with the conflict and coordination between OCC and RPP and balances the benefits of platform enterprise with the optimal contractor impacts triggered by planning activities.