Solving the Resource-Constrained Project Scheduling Problem (RCPSP) with Quantum Annealing
Résumé
Quantum annealing offers a promising approach for solving combinatorial optimization problems like the Resource-Constrained Project Scheduling Problem
(RCPSP). We investigated its empirical performance by formulating the RCPSP as QUBO and ISING model. Experimental results are provided using a cutting-edge
annealer (D-Wave Advantage) on RCPSP instances from the CV dataset. We conducted a thorough analysis of the solution energy landscape focusing on
diversity and quality. Results are compared with classical solvers, shedding light on the potential advantages and limitations of using quantum annealing for
RCPSP. Our findings suggest that quantum annealing delivers satisfying solutions for smaller to medium-sized problems. It also offers a high diversity of
solutions making it a viable candidate for hybrid approaches to further enhance solution quality.