Combinatorial optimization problems are an important class of problems often encountered in the real world involving a combinatorially growing set of feasible solutions as the problem size increases. Since exact approaches can be computationally expensive, practitioners often use approximate approaches such as metaheuristics. However, sophisticated approximate methods that yield high-quality solutions require expert help to handcraft or fine-tune the solution process to suit a given problem distribution. In recent years, artificial intelligence (AI) approaches that involve learning from data without being explicitly programmed have shown tremendous success at various challenging tasks, like natural language processing and autonomous driving. Therefore, solving combinatorial optimization problems is an ideal use case for AI approaches. In this dissertation, we find answers to two key questions considering recent AI developments. 1) How to use deep reinforcement learning (DRL) approaches to solve complex multi-vehicle combinatorial optimization problems. 2) Can combining machine learning, metaheuristics, and mixed integer-linear optimization solvers under a hybrid framework help quickly obtain certifiable high-quality solutions for combinatorial optimization problems? The answer to these questions broadly builds on two key directions: DRL and hybrid approaches to tackle challenging multi-vehicle combinatorial optimization problems considering the recent advancements, gaps, and drawbacks. Specifically, in Part I of this dissertation, DRL-based approximate approaches are developed to learn from complex edge features, reason over uncertain edges, and handle multi-vehicle decoding and collaboration to solve complex multi-vehicle combinatorial optimization problems. Additionally, we develop approaches to generate large-scale complex data on the fly for training. Upon experimental evaluation, we learn that DRL-based approaches can quickly generate high-quality solutions to complex scenarios and, in the best case, yields a minimum improvement of 3.44% and, in the worst case, at par with traditional and other baseline DRL methods. Next, in Part II of this dissertation, a hybrid-learning optimization framework (HyLOS), combining machine learning, metaheuristics, and mixed integer-linear optimization, is developed to solve challenging combinatorial optimization problems quickly. Experimental studies reveal that the HyLOS framework can collaboratively and effectively utilize metaheuristics, machine learning, and MILP approaches to generate high-quality solutions quickly and, on average, yield a minimum improvement of 48% against all individuals and subsets of solvers.

Library of Congress Subject Headings

Combinatorial optimization; Deep learning (Machine learning); Reinforcement learning

Publication Date


Document Type


Student Type


Degree Name

Mechanical and Industrial Engineering (Ph.D)

Department, Program, or Center

Industrial and Systems Engineering (KGCOE)


Katie McConky

Advisor/Committee Member

Ruben Proano

Advisor/Committee Member

Christopher Kanan


RIT – Main Campus

Plan Codes