Sequencing for the MCLP

Abstract

Labour shortage in the logistic sector and the poor work conditions as a baggage handler are a major and highly relevant problem for airports around the world. A solution is provided by Vanderlande Industies: a baggage loading robot. Since the efficiency of the stacking process is sufficient, operators that overlook the robot must intervene often. To minimise these interventions, better packings have to be made. To make better packings, Vanderlande Industries can adapt the sequence in which the items are presented to the robot. However, literature and the company are not aware of the effects of sequencing. Thereby in this thesis, we conducted research into sequencing items in various ways for the Multiple Container Loading Problem (MLCP). We attempted to find out which methods enhance the fill rate of the packings. In order to understand the academic background of the MCLP, we first looked at the literature. Additionally, state-of-the-art techniques have been investigated. A trip to Schiphol has been undertaken to observe the real robot that is resolving the MCLP as a pilot project for Schiphol in order to put the issue in its proper respective. From this research, we concluded that there are various methods for solving the MCLP, however, sequencing methods applied to so far are mostly kept simple. Moreover, in practice, baggage items are placed in the order where size and weight are decreasing. The knowledge gap found was that sequencing for the MCLP is not researched. We presenttwo different types of algorithms to provide a well performing sequence. The first algorithm, the genetic algorithm, makes use of multiple generations to optimise the sequence. The other algorithm, the heuristic algorithm, makes use of a set of rules. The resulting packings formed with different sequencing methods show to be different. According to the simulation performed, the heuristic sequencing strategy produces the best results for the main scenario in terms of the layer fill rate. The performance of the packings can with the heuristic sequencer be increased by average with 11 % in comparison with packings generated with a random item sequence. Additionally, the heuristic sequencer’s packings are fairly consistent because it consistently manages to place 8 items in one layer of the container. Results in this study point out that sequencing the items with size decreasing performs worse than the packings formed from non-sequenced items, with a mean fill rate of 0.71 for the size decreasing items and 0.87 for the heuristically sequenced items. The alternate scenario produces fewer evident outcomes, but we can point out that simple strategies, such as sorting the items by short side, perform effectively. The results from this applied method: an increase of 7% in comparison with non-sequenced item packings. We assume the simple sorting approach worked well since it grouped all similar shaped objects, resulting in tight packings. The genetic algorithm produced slightly better results, with average outcomes being similar (0.87), but much more consistent packings showing a lower standard deviation of 0.00917 (GA) versus a standard deviation of (0.01747). The results from the heuristic algorithm were the least efficient. This demonstrates that sequencing using a heuristic is effective, but it is not a reliable strategy in all circumstances. The genetic algorithm, in comparison, generates generally worse results, but it can adjust to various situations and configurations. Although the results seem to be convincing, there rise discussion points. One of the major drawbacks is the fact that in this study, simulations are only done in two dimensions. This results in a few assumption regarding stacking behaviour. Thereby, a significant point of future work can be done by introducing the sequencing algorithm to three-dimensional simulations.