In all experiments we executed we see that the main objective, delivering and picking-up con- tainers in time, is always met. The secondary objective to maximize utilization is achieved too. The performance on minimizing trip duration is varying strongly.
When comparing the method with real plan, we notice strong deviations in the terminals that are visited and the number of containers delivered and picked-up. The main reason for this is that planners have more information they take into consideration than the method does. This is also the reason why the method results in more terminal visits. Compared to a simple FiFo rule, the method performs better on minimizing the number of terminal visits and we even notice that the rules of the method influence future plans.
A flaw in the data is that the method uses a number of import containers with priority 1 that is larger than it actually is. That is because it is hard to tell the difference between import containers that are ready to be picked-up, are planned to be picked-up by another barge, or are located on another barge and are on their way to CTT.
An advantage of our method compared to plans made by CTT’s barge planners and by methods such as FiFo, is the fact that the method tries to combine loading import containers with unloading export containers at the same terminal.
Chapter 6
Conclusions and Recommendations
This chapter states the conclusions of this study in Section 6.1. Section 6.2 discusses the value and limitations of the study and Section 6.3 the recommendations for further research.
6.1
Conclusions
The main goal of this study is to:
Design a method to support CTT’s barge planners to improve the quality of CTT’s transportation flows.
In order to reach our research goal we study the current situation at CTT and the container sector. We describe the planning and decision process of the planners at CTT to find the bottleneck where we were able to help. After that, we studied literature to learn more on loading, planning and scheduling in the container sector and other fields. With the gathered knowledge a decision method was developed which is incorporated in a decision support tool. Finally, we tested the tool on different sets of data to determine the performance compared to more simple methods and the CTT’s planners.
From this study, we learned that the container sector is a growing sector and that CTT rec- ognizes that in the growing number of bookings. We also learned that planners have a hard time with data management. A lot of data must be obtained from different sources that vary in reliability. The large amount of data must not only be collected but also taken into consid- eration when making plans. The amount of data grows with the growing number of bookings. The most important problem of a planner is to keep an overview of all important data.
The literature review shows that a lot of research is done in the field of planning and schedul- ing. Less literature is found on the topic of container barge loading and scheduling problems. Furthermore, we searched for performance measures that indicate the quality of transportation. Little literature was found that discussed this as a main topic. From our literature research we learn that decision support is an effective way of supporting planners in their planning process.
To support planners in their planning process we propose a model in Chapter 4. The output of the model provides planners with a plan that suggests which container to ship with which barge. We use a combination of heuristics that makes sure that containers are picked-up and delivered in time and minimizes the number of terminals visits. We use an architecture for the combination of heuristics, which ensures that flexibility is provided. New heuristics are easily added and new combination are easily made.
When testing the combination of heuristics, we learn which combination give the best results. The integrated combination of heuristics performs similar to the best setting when concerning one barge and it performs better when concerning several barges. Comparing the method with more simple methods and what CTT’s planners execute, we discovered that the two strengths of the method are that the method combines loading and unloading containers at the same terminals and it takes future planning into account by trying to avoid visits a the same terminals multiple days after each other. The disadvantage is that the model still misses some crucial factors to be able to compare it with the plans made by barge planners plans.
Overall, we conclude that the prototype tool has the potential to become a decision support tool that assists planners in their planning. At this point it provides planners with insights and a foundation is made to built upon. With further development, the tool will be a valuable addition to the planners insights and common sense, because it initially considers all information at once, instead of making smaller subproblems. Furthermore, planning becomes easier when using the tool because it also considers future planning.