A hybrid framework using a QUBO solver for permutation-based combinatorial optimization

• Main Authors: GOH, Siong Thye, GOPALAKRISHNAN, Sabrish, BO, Jianyuan, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Machine learning; quadratic unconstrained binary optimization solver; large-scale permutation-based combinatorial problems; MITB student; Numerical Analysis and Scientific Computing; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5978; https://ink.library.smu.edu.sg/context/sis_research/article/6981/viewcontent/Goh__S._T.__Gopalakrishnan__S.__Bo__J.____Lau__H._C.__2020_._A_Hybrid_Framework_Using_a_QUBO_Solver_For_Permutation_Based_Combinatorial_Optimization.pdf
• Institution: Singapore Management University

In this paper, we propose a hybrid framework to solve large-scale permutation-based combinatorial problems effectively using a high-performance quadratic unconstrained binary optimization (QUBO) solver. To do so, transformations are required to change a constrained optimization model to an unconstra...