Abstract

This study explores a specific variant of the classic two-dimensional bin-packing problem, known as the Circle Bin-Packing Problem with Rectangular Items (CBPP-RI). This problem involves the orthogonal packing of rectangular items into the fewest possible circular bins and has significant practical implications. We propose a novel and efficient Goal-Driven Iterated Local Search (GDILS) approach for solving CBPP-RI, which integrates a customized method that effectively addresses cold starts and prevents entrapment in local optima. To avoid unnecessary searches, we use lower bounds, which are improved by accounting for the inevitable waste produced by rectangular items at the edges of circular bins. To achieve good performance in single-bin packing, we propose a maximal-space-based heuristic, which introduces the widely used concept of maximal-space from other rectangle packing problems. The experimental results demonstrate that GDILS performs well and show that our method is not only applicable to CBPP-RI but also effective for other related packing problems. To establish a valid benchmark for future research, we also generate a new dataset for CBPP-RI and conduct extensive experiments.