A goal programming model for optimal resource allocation in large-scale quantum computing semiconductor manufacturing under structural complexity and sectoral constraints

Rapid developments in quantum computing technology call for the creation of advanced semiconductor manufacturing systems able to handle structural complexity, limited resources, and multi-sectoral needs. Combining technical, economic, and operational constraints, this paper presents a goal programming (GP) model for best resource allocation in large-scale quantum computing semiconductor manufacturing. The model tackles issues including material scarcity, production yield variation, cross-sector resource competition, and multi-objective optimization (cost minimization, throughput maximization, and defect reduction). The GP framework allows for balanced decision-making under uncertainty by including structural complexity measures (process interdependencies, quantum error correction criteria) and sectoral constraints (automotive, healthcare, and defense applications). A case study shows how well the model balances competing goals under real-world manufacturing constraints. The findings show increases in resource use efficiency (15-22%), yield rates (8-12%), and cost savings (10-18%) relative to conventional linear programming methods. This paper provides insights for legislators and business leaders negotiating the semiconductor supply chain as well as helps to sustainably scale quantum hardware manufacture.