Due to the increase in the frequency and impact of disruptive events in our society, designing and managing resilient infrastructure networks is of critical importance. In this project, we are investigating a unique interdisciplinary approach that combines mathematical modeling with human-in-the-loop experiments to prove that complex multi-objective resiliency problems are best optimized by having humans and algorithms collaborate for optimization. Our research focuses on the debris collection problem, which is a common problem after a disaster strikes where all the debris on the roads is collected by multiple contractors. The problem revolves around assigning contractors to different regions such that they will operate on geographically contiguous and distinct regions. Additionally, operating time and revenue for each contractor must be similar. We conjecture that humans are better than algorithms at assessing trade-offs between these multiple objectives and are competent in finding geographically contiguous and compact regions for different contractors. We are leveraging simulation games in lab experiments to investigate this contribution of humans to hard optimization problems and improve the performance of optimization algorithms. To test our conjecture, we deployed an Excel-based prototype and collected player decisions and observations in a within-subjects experimental pilot study. With the encouraging pilot test results we are currently developing a graphical user interface where the human player can visually assign the contractors. Building on all the experiments, our objective is to create a digital tool which leverages both human input and optimization algorithms to make infrastructure networks more resilient.