A computer-implemented method of arranging an object (42a-42c) in a 3D space for solving the bin packing problem of efficiently arranging cargo in a target space , comprising: obtaining information on the target space and a plurality of objects (42a-42c) to be arranged in the target space ; dividing the target space into M x N subspaces extending from a first surface among planes forming the target space , the target space having a cuboid shape; dividing the target space into P x Q subspaces extending from a second surface orthogonal to the first surface among planes forming the target space ; calculating, for each of the M x N subspaces extending from the first surface, a first feature representing an object arrangement state in the subspace ; calculating, for each of the P x Q subspaces extending from the second surface a second feature representing an object arrangement state in the subspace ; inputting data representing the first feature of each of the M x N subspaces and data representing the second feature of each of the P x Q subspaces into a first artificial neural network; obtaining, from the first artificial neural network , a current arrangement state embedding vector of the target space based on information on an arrangement state of the objects (42a-42c) existing in the target space , selecting, by means of a second artificial network, a first object to be arranged in the target space among the plurality of objects (42a-42c) and determining a position where the first object will be arranged in the target space based on the information on the target space , information on the plurality of objects (42a-42c) and the current arrangement state embedding vector ; wherein the second surface is a front or side surface of the target space , and wherein the second feature for each of the P x Q subspaces extending from the second surface includes whether an object identifier displayed on a surface of an object (42a-42c) existing in the target space and arranged in the subspace is visible from the second surface. obtaining information on the target space and a plurality of objects (42a-42c) to be arranged in the target space ; dividing the target space into M x N subspaces extending from a first surface among planes forming the target space , the target space having a cuboid shape; dividing the target space into P x Q subspaces extending from a second surface orthogonal to the first surface among planes forming the target space ; calculating, for each of the M x N subspaces extending from the first surface, a first feature representing an object arrangement state in the subspace ; calculating, for each of the P x Q subspaces extending from the second surface a second feature representing an object arrangement state in the subspace ; inputting data representing the first feature of each of the M x N subspaces and data representing the second feature of each of the P x Q subspaces into a first artificial neural network; obtaining, from the first artificial neural network , a current arrangement state embedding vector of the target space based on information on an arrangement state of the objects (42a-42c) existing in the target space , selecting, by means of a second artificial network, a first object to be arranged in the target space among the plurality of objects (42a-42c) and determining a position where the first object will be arranged in the target space based on the information on the target space , information on the plurality of objects (42a-42c) and the current arrangement state embedding vector ; wherein the second surface is a front or side surface of the target space , and wherein the second feature for each of the P x Q subspaces extending from the second surface includes whether an object identifier displayed on a surface of an object (42a-42c) existing in the target space and arranged in the subspace is visible from the second surface. <EOS>