A hybrid approach for the container loading problem enhancing the dynamic stability representation
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A Hybrid Approach for the Container Loading Problem Enhancing the Dynamic Stability Representation.pdf
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In the Container Loading Problem (CLP), the construction of packing patterns is driven by the maximization of the volume occupied, which compromises several constraints such as loading feasibility, weight balance, load stability, operational safety, material handling, and the prevention of cargo damage during container shipping. Previous works introduced dynamic stability indicators using simulation or statistical approaches. However, the first exponentially increases the computational burden, and the second misrepresents the essential kinetic mechanical aspects. This paper presents a new scheme to solve the CLP by embedding a mechanical model into a reactive GRASP Algorithm leading to two main novelties, namely the substitution of the physics simulation engine to find the dynamic stability of the packing patterns; and a modified structure of the metaheuristic guaranteeing specified minimum load stability and reaching efficient packing patterns. The mechanical model dynamically analyses the forces and accelerations acting on the load to predict the loss of support, overturning, or reaching the critical velocity that would damage it. At the same time, the reactive GRASP algorithm includes the dynamic stability indicators in the improvement steps. The stability indicators are obtained from the mechanical model allowing the user to know the percentage of dropped and damaged boxes from a packing pattern. The effectiveness of the proposed approach has been tested using a classical set of benchmark instances, obtaining adequate accuracy solutions within a short computational time. The resulting scheme integrates real-world problem conditions and achieves dynamic stability solutions with an acceptable computational effort using C++ programming language instead of simulation tools. In future work, it is suggested to include the proposed metaheuristic within a commercial or open decision support system to corroborate its daily performance in logistics-based companies.