Hybrid Heuristic Algorithm Based On Improved Rules & Reinforcement Learning for 2D Strip Packing Problem

A hybrid heuristic algorithm based on improved rules and reinforcement learning is proposed to solve the 2D strip packing problem (2DSPP). Firstly, the scoring rules based on the skyline algorithm are improved by considering the “two-step” successive items with a set of width relaxation factors. The improved scoring rules can reduce space waste efficiently. Secondly, as a reinforcement learning approach, the Deep Q-Network (DQN) is established to get the initial rectangular items sequence and at the same time as an essential supplement for the placement rules. It can improve space utilization and prevent the algorithm from falling into the local optimum. Combining the new “two-step” placement rules and DQN, the heuristic algorithm based on simple random algorithm (SRA) is proposed and finally called reinforcement learning based simple random algorithm (RSRA). Experiments on eight datasets by five algorithms have been conducted for comparison. Results show the RSRA has achieved the best performance on eight datasets (C, N, CX, NT, 2sp, NP, ZDF, BWMV) and has dropped Ave. Gap% by 45.86%, 45.16%, 30.89% and 20.56% than GRASP, SRA, IA, ISH respectively. It can be concluded that the RSRA algorithm would achieve better performance than the other four algorithms on eight datasets, especially on the larger datasets.