The diverse constraints of industrial applications lead to variants of two-dimensional (2D) irregular packingproblems that require tailored solution methods. This paper addresses a real-world industrial challenge byproposing a new problem definition, the maximum reusable contiguous area problem (MRCAP), and a novelmetric, the maximum contiguous area, to measure and maximize the contiguous unused area in a layout,thereby facilitating the reuse of remnant material. This study proposes an approach focused on optimizingplacement policies. We develop a decoder, implemented within a new version of the random-key optimizer(RKO) framework, that dynamically assigns the best placement rule from an 11-heuristic portfolio. We vali-date our methodology on established literature benchmarks. Among the 15 benchmark 2D Irregular Knap-sack Problem instances evaluated, RKO matched the best-performing existing algorithm on 11 and achievedbetter solutions on the remaining 4. These results suggest that RKO is competitive with, and potentially su-perior to, this algorithm. RKO’s results indicate that current benchmarks no longer adequately represent theproblem, motivating the introduction of extended benchmark instances. RKO also outperforms leading 2Dirregular strip packing problem (SPP) methods relying on constructive sequence search. Finally, comparingour method against the top SPP algorithm, which minimizes layout overlap, on real-world MRCAP instancesshows that RKO yields superior remnant quality in almost all problem cases. This demonstrates that minimiz-ing layout width in SPP does not ensure effective remnant valorization, highlighting the distinction betweenthese two optimization objectives.