Article deals with two-dimensional orthogonal cutting-packing optimization which is the most often in practice. 1.5-DBPP (1.5-Dimensional Bin Packing Problem) and 2-DBPP (2-Dimensional Bin Packing Problem) were considered. Difference between these two problems is following. In 1.5-DBPP we are given half-infinite strip of material while in 2-DBPP sheet of material has fixed length. Algorithm-decoder was proposed, this algorithm was called group decoder. To find group of objects which should fill current block proposed algorithm uses linear cutting methods based on dynamic programming. Choose of precisely group but not single object let us more effectively spend space on the sheet and reduce the amount of waste. Computational experiments were held in which proposed algorithm was compared to existing ones; use of simulated annealing methods to find better priority lists was also considered. In these experiments group decoder applied together with simulated annealing showed the best results. Moreover results group decoder without simulated annealing weren't worse than existing algorithms with simulated annealing. Experiments were hold on two data sets. First data set was test consisted of randomly generated rectangles. For second data set tests were generated so as to one be able to place rectangles on a sheet in a way to sheet was used for 100 %. On several tests from second data set proposed algorithm was able to find optimal solution.

orthogonal cutting-packing, group decoder, dynamic programming, optimization, linear cutting, simulated annealing, greedy.