A data-driven optimization approach for mixed-case palletization and three-dimensional bin packing

data analytics;mixed-case palletization;three-dimensional bin packing;mixed-integer programing;3DBPP;MIP

de Carvalho, Paulo R. V.affiliation1:Faculty of Engineering ; advisor:Elhedhli, Samir ; Elhedhli, Samir ;

University of Waterloo

关键词: three-dimensional bin packing;    mixed-integer programing;    data analytics;    Master Thesis;    MIP;    mixed-case palletization;    3DBPP;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/12891/3/deCarvalho_PauloRV.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Palletization is the most standard method of packaging and transportation in the retail industry. Their building involves the solution of a three-dimensional packing problem with side practical constraints such as item support and pallet stability, leading to what is known as the mixed-case palletization problem. Motivated by the fact that solving industry-size instances is still very challenging for current methods, we propose a new solution methodology that combines data analysis at the instance level and optimization to build pallets. Item heights are first analyzed to identify possible layers and to derive relationships between item positions. Items are stacked in pairs and trios to create super items, which are then arranged to create layers of even height. The resulting layers are finally stacked to create pallets. The layers are constructed using a reduced-size mixed integer program as well as a two-dimension placement heuristic. Computational tests on industry data show that the solution approach is extremely efficient in producing high-quality solutions in fast computational times.