Dynamic postponement design for crowdsourcing in open manufacturing: A hierarchical joint optimization approach

Open manufacturing and crowdsourcing have been envisioned as a trend for industries to promote collaboration across different firms and support the sharing and exchange of knowledge and services throughout the value chain. Incorporating postponement decisions with the crowdsourcing strategy helps reduce the risk and uncertainty associated with product variety in an open manufacturing environment. The inherent coupling of product design and postponement decisions in an open manufacturing environment necessitates joint optimization of product family design and postponement planning. This article formulates a Dynamic Postponement Design (DPD) problem that considers an undefined product architecture that interacts with the postponement design according to optimal planning of open manufacturing activities. The DPD problem differs from traditional (static) postponement design models in that the latter assumes that a (fixed) product architecture is available at the outset. This article r develops a Hierarchical Joint Optimization (HJO) model based on Stackelberg game theory. The HJO model deploys a bi-level mixed 0-1 nonlinear programing decision structure to reveal the coupling of product design and postponement decisions. To solve the bi-level programing model, a nested genetic algorithm is developed and implemented. A case study of smart refrigerator design for postponement is reported to illustrate the DPD problem and the proposed HJO approach.