SUMMARY

Combinatorial optimal decisions are widely observed in operations engineering applications. The emerging field of quantum-inspired computation has shown some salient features of quantum information processing that are promised to excel in quantum speedup contributing to solving operational optimization problems of practical relevance more efficiently than conventional paradigms. This research aims to investigate the potential of quantum-inspired optimization modeling and solution. The objective is to create mathematical and computational models to advance operations engineering problem solving with respect to three particular technical focuses: (1) To improve computational efficiency through quantum-inspired combinatorial optimization; (2) To enhance domain context representation in operations system modeling via quantum entanglement inspired hard constraint handling; and (3) To bolster data-driven decision making by quantum neural network prediction model embedded optimization. The research will propose and validate a variety of new methods to synthesize a novel quantum-inspired decision-making framework, including a quantum-inspired genetic algorithm (QiGA) for computationally efficient combinatorial optimization, a quantum entanglement inspired (QEI) hard constraint handling approach for optimization with context related hard constraints, and a quantum-inspired neural network (QINN) model for learning and supporting corresponding data-driven optimization. The accuracy and reliability of the proposed quantum-inspired methods and algorithms will be assessed based on computational experiments, along with application to industrial case studies.