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2D and 3D bin packing
This project forked from jamesbremner/pack
2D and 3D bin packing
Copyright (c) 2016 by James Bremner Copyright (C) 2012 by Arash Sharif Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. The code is based on Arash Sharif's 2012 packit4me, which is no longer supported. I have modified the modified Sharif's code to: - build on windows using codeblocks - utilize C++11 features, particularly shared pointers - merge fragmented unused space in the bins - record locations of items in bins - output 2D cutting list - optional limit to exactly one bin - positional constraints An approximation algorithmis used, called first-fit decreasing (FFD). The elements are sorted in decreasing order of size, and the bins are kept in a fixed order. Each element is placed into the first bin that it fits into, without exceeding the bin capacity. How good is the approximation? The algorithm is guaranteed to return a solution that uses no more than 11/9 of the optimal number of bins (Johnson 1973). For example, on problems of 90 items, where the items are uniformly distributed from zero to one million, and the bin capacity is one million, the algorith uses an average of 47.732 bins. On these same problem instances, the optimal solution averages 47.680 bins. The FFD solution is optimal 94.694% of the time on these problem instances. (Korf 2002 http://www.aaai.org/Papers/AAAI/2002/AAAI02-110.pdf) ------------------------------------------------------------------------------------------------ This project is a library for best-fit bin packing. It is designed to be one native call and it will figure out the best way to pack 1d, 2d or 3d bin(s) with item(s). The bins should be passed in the following format: "id:dim_unit:quantity:size1xsize2xsize3:weight" The items passed like so: "id:dim_unit:constraints:quantity:size1xsize2xsize3:weight" id could be a stock or order number. example: 53443:5x5x5 constraints are for each item and if the item can be rotated: constraint = position_constraint + rotation_constraint position constraint 0 : anywhere 100 : bottom only 200 : top only rotation constraint 0 : Any 1 : Length axis only 2 : Width axis only 3 : Height axis only 7 : No ratiation allowed Dependencies: 1. To compile the algorithm you need to make sure you have boost for c++ installed run ./packit4me or ./packit4me -help for options
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