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

A constraint programming approach for the premarshalling problem

[EN] The enormous amount of containers handled at ports hampers the efficiency of terminal operations. The optimization of crane movements is crucial for speeding up the loading and unloading of vessels. To this end, the premarshalling problem aims to reorder a set of containers placed in adjacent stacks with a minimum number of crane movements, so that a container with an earlier retrieval time is not below one with a later retrieval time. In this study, we present a series of constraint programming models to optimally solve the premarshalling problem. Extensive computational comparisons show that the best proposed constraint programming formulation yields better results than the state-of-the-art integer programming approach. A salient finding in this paper is that the logic behind the model construction in constraint programming is radically different from that of more traditional mixed integer linear programming models.Acknowledgements This study has been partially supported by the Spanish Ministry of Science and Innovation under predoctoral grant PRE2019-087706 and the project 'OPTEP-Port Terminal Operations Opti-mization' (No. RTI2018-094940-B-I00) financed with FEDER funds.Jiménez-Piqueras, C.; Ruiz, R.; Parreño-Torres, C.; Alvarez-Valdes, R. (2023). A constraint programming approach for the premarshalling problem. European Journal of Operational Research. 306(2):668-678. https://doi.org/10.1016/j.ejor.2022.07.042668678306

Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem

In container terminals, containers are often moved to other stacks in order to access containers that need to leave the terminal earlier. We propose a new optimization model in which the containers can be moved in two different phases: a pre-processing and a relocation phase. To solve this problem, we develop an optimal branch-and-bound algorithm. Furthermore, we develop a local search heuristic because the problem is NP-hard. Besides that, we give a rule-based method to estimate the number of relocation moves in a bay. The local search heuristic produces solutions that are close to the optimal solution. Finally, for instances in which the benefits of moving containers in the two different phases are in balance, the solution of the heuristic yields significant improvement compared to the existing methods in which containers are only moved in one of the two phases

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Development of Visualization-Animation Software for Learning Transportation Algorithms

Recognizing the steady decline in US Science Technology Engineering Mathematics (STEM) interests and enrollments, the National Science Foundation (NSF) and the White House have developed national strategies and provided significant budget resources to STEM education research [1-2] in the past years, with the ultimate goals being to improve both the quality and number of highly trained US educators, student workforce in STEM topics, in today’s highly competitive global markets. With the explosion of the internet’s capability and availability, it is even more critical to effectively train this future USA-STEM work-force and/or to develop effective STEM related teaching tools to reach a maximum possible number of “distance learners/audiences”. Various teaching philosophies have been proposed, tested and documented by educational research communities, such as video lectures (YouTube), “flipped” class lectures (where students are encouraged to read the lecture materials on their own time, and problem solving and/or question/answer sessions are conducted in the usual classroom environments), STEM summer camps, game-based-learning (GBL) [3-5], virtual laboratories [6] and concept inventory [7]. The goal of this study is to develop useful, user friendly Java computer animation for “teaching” these basic/important STEM algorithms that will not only help both the students and their instructors to master this technical subject, but also provide a valuable tool for obtaining the solutions for homework assignments, class examinations, and self-assessment tools. Java software tools were developed for this research which include the Unloading and Pre-Marshalling algorithms for Terminal Yard Operations, the Hungarian algorithm for worker to job optimum assignment, the Dijkstra algorithm for solving the shortest-path of a transportation network, and the Cholesky Decomposition algorithm for solving simultaneous linear equations. This “educational version” of the Java-based application were implemented with several desirable features, such as: A detailed, precise and clear step-by-step algorithm will be displayed in text and human voice during the animation of the algorithm. Options to hear animated voice in several major languages (English, Chinese and Spanish). Options to input/output data (CVS file), or manually edit the data using an editor, or “randomly generating” data. Output of the “final/optimal” results can be exported to text so that the users/learners can check/verify their “hand-calculated” results, which is an important part of the learning process