Linear time ordering of bins using a conveyor system

A local food wholesaler company is using an automated commissioning system, which brings the bins containing the appropriate product to the commissioning counter, where the worker picks the needed amounts to 12 bins corresponding to the same number of orders. To minimize the number of bins to pick from, they pick for several different spreading tours, so the order of bins containing the picked products coming from the commissioning counter can be considered random in this sense. Recently, the number of bins containing the picked orders increased over the available storage space, and it was necessary to find a new way of storing and ordering the bins to spreading tours. We developed a conveyor system which (after a preprocessing step) can order the bins in linear space and time

A branch and bound approach for large pre-marshalling problems

[EN] The container pre-marshalling problem involves the sorting of containers in stacks so that there are no blocking containers and retrieval is carried out without additional movements. This sorting process should be carried out in as few container moves as possible. Despite recent advancements in solving real world sized problems to optimality, several classes of pre-marshalling problems remain difficult for exact approaches. We propose a branch and bound algorithm with new components for solving such difficult instances. We strengthen existing lower bounds and introduce two new lower bounds that use a relaxation of the pre-marshalling problem to provide tight bounds in specific situations. We introduce generalized dominance rules that help reduce the search space, and a memoization heuristic that finds feasible solutions quickly. We evaluate our approach on standard benchmarks of pre-marshalling instances, as well as on a new dataset to avoid overfitting to the available data. Overall, our approach optimally solves many more instances than previous work, and finds feasible solutions on nearly every problem it encounters in limited CPU times.The authors thank the Paderborn Center for Parallel Computation (PC2) for the use of the Arminius cluster for the computational study in this work. This work has been partially supported by the Spanish Ministry of Science, Innovation, and Universities FPU Grant A-2015-12849 and by the Spanish Ministry of Economy and Competitiveness, under projects DPI2014-53665-P and DPI2015-65895-R, partially financed with FEDER funds.Tanaka, S.; Tierney, K.; Parreño-Torres, C.; Alvarez-Valdes, R.; Ruiz García, R. (2019). A branch and bound approach for large pre-marshalling problems. European Journal of Operational Research. 278(1):211-225. https://doi.org/10.1016/j.ejor.2019.04.005S211225278