A GRASP algorithm for multi container loading problems with practical constraints

M. T. Alonso (), R. Alvarez-Valdes () and F. Parreño ()
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M. T. Alonso: University of Castilla-La Mancha
R. Alvarez-Valdes: University of Valencia
F. Parreño: University of Castilla-La Mancha

4OR, 2020, vol. 18, issue 1, No 2, 49-72

Abstract: Abstract We consider the multicontainer loading problem of a company that has to serve its customers by first putting the products on pallets and then loading pallets onto trucks. When a large number of units of a product have to be shipped, the company requires that homogeneous pallets, with only one product, are built first, then weakly heterogeneous pallets, in which each layer corresponds to a single product, and finally strongly heterogeneous pallets with the remaining units of the products. To be useful in practice, the solutions have to satisfy five types of constraints: geometric constraints, so that pallets are completely inside the trucks and do not overlap; weight constraints, limiting the total weight a truck can bear and the maximum weight supported by each axle; constraints limiting the position of the centre of gravity of the cargo; dynamic stability constraints, to avoid cargo displacement when the truck is moving; and constraints ensuring that the delivery dates of products are respected. We have developed a Greedy Randomized Adaptive Search Procedure, including some improvement methods tailored to the problem, among them an adaptation of ejection chains. The approach has been tested on a benchmark of real problems and it has been shown to be capable of finding high-quality, realistic solutions in short computing times. We also provide a comparison with an integer programming formulation that justifies the use of a metaheuristic algorithm.

Keywords: Container loading; Optimization; Heuristics algorithm; GRASP; 90B06; (Transportation; and; logistics) (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10288-018-0397-z

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