  We introduce a new bin packing problem, termed the circle bin packing problem
with circular items (CBPP-CI). The problem involves packing all the circular
items into multiple identical circle bins as compact as possible with the
objective of minimizing the number of used bins. We first define the tangent
occupying action (TOA) and propose a constructive greedy algorithm that
sequentially packs the items into places tangent to the packed items or the bin
boundaries. Moreover, to avoid falling into a local minimum trap and
efficiently judge whether an optimal solution has been established, we continue
to present the adaptive simulated annealing with greedy search (ASA-GS)
algorithm that explores and exploits the search space efficiently.
Specifically, we offer two novel local perturbation strategies to jump out of
the local optimum and incorporate the greedy search to achieve faster
convergence. The parameters of ASA-GS are adaptive according to the number of
items so that they can be size-agnostic across the problem scale. We design two
sets of new benchmark instances, and the empirical results show that ASA-GS
completely outperforms the constructive greedy algorithm. Moreover, the packing
density of ASA-GS on the top few dense bins is much higher than that of the
state-of-the-art algorithm for the single circle packing problem, inferring the
high quality of the packing solutions for CBPP-CI.
