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 investigates the potential of quantum-inspired optimal decision-making modeling and solution with the objective of creating mathematical and computational models to advance operations engineering problem solving with respect to three particular technical focuses: (1) The improvement of computational efficiency through quantum-inspired combinatorial optimization; (2) Theenhancement of domain context representation in operations system modeling via quantum entanglement inspired hard constraint handling; and (3) The elevation of data-driven decision making by quantum inspired neural network prediction model. The research proposes and validates a variety of new methods to synthesize a novel quantum-inspired decision-making framework, including a twofold update quantum-inspired genetic algorithm (TU-QIGA) for computationally efficient combinatorial optimization, a quantum entanglement inspired hard constraint handling approach (QEI-GA) for optimization with context related hard constraints, and a hybrid quantuminspired semantic neural network (HQISNN) model for learning and supporting data-driven decision making. The accuracy and reliability of the proposed quantum-inspired methods and algorithms are assessed based on computational experiments, along with application to industrial case studies.