Automating the planning of container loading for Atlas Copco: Coping with real-life stacking and stability constraints

• The Atlas Copco* distribution center in Allen, TX, supplies spare parts and consumables to mining and construction companies across the world. For some customers, packages are shipped in sea containers. Planning how to load the containers is difficult due to several factors: heterogeneity of the packages with respect to size, weight, stackability, positioning and orientation; the set of packages differs vastly between shipments; it is crucial to avoid cargo damage. Load plan quality is ultimately judged by shipping operators. This container loading problem is thus rich with respect to practical considerations. These are posed by the operators and include cargo and container stability as well as stacking and positioning constraints. To avoid cargo damage, the stacking restrictions are modeled in detail. For solving the problem, we developed a two-level metaheuristic approach and implemented it in a decision support system. The upper level is a genetic algorithm which tunes the objective function for a lower level greedy-type constructive placement heuristic, to optimize the quality of the load plan obtained. The decision support system shows load plans on the forklift laptops and has been used for over two years. Management has recognized benefits including reduction of labour usage, lead time, and cargo damage risk. (C) 2019 Elsevier B.V. All rights reserved.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)

Nyckelord

Logistics; Packing; Decision support systems; Metaheuristics

Publikations- och innehållstyp

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