Abstract
The increasing impacts of globalization and the COVID-19 pandemic underscore the critical role of the logistics sector in global trade and national economies. Effective container loading plans enhance logistics and shipping efficiency while boosting customer satisfaction. The container loading, a complex combinatorial optimization problem, significantly affects the economy, environment, and safety. In modern logistics, developing solutions that ensure high volume utilization and meet a range of practical constraints is crucial. This paper introduces a hybrid tabu search algorithm that employs an iterative use of tabu search and heuristic methods to solve three-dimensional container loading problems, considering high volume utilization and practical constraints. The approach focuses on two main stages of the problem. In the first stage, the tabu search algorithm determines the loading sequence of box types; in the second stage, the loading heuristic is used for placing boxes into the container based on the established sequence. The developed hybrid tabu search algorithm has been coded and implemented using the Python programming language. The performance of the proposed algorithm has been evaluated using test problems provided by the OR-library. The algorithm aims to maximize volume utilization by minimizing the spaces between boxes during loading and optimizing the arrangement of box stacking. The results demonstrate that the algorithm provides high-quality solutions by achieving high volume utilization in problems with heterogeneous structures. The successful outcomes obtained from test sets characterized by strong heterogeneity and various practical constraints highlight the potential of the hybrid tabu search algorithm to enhance efficiency in logistic processes. This underscores the algorithm’s ability to offer valuable practical applications in the logistics sector, particularly in reducing transportation costs and optimizing loading processes.
References
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Details
| Primary Language | English |
|---|---|
| Subjects | Engineering Economy |
| Journal Section | 2024 4/2 (October) |
| Authors | |
| Publication Date | October 31, 2024 |
| Submission Date | February 13, 2024 |
| Acceptance Date | May 7, 2024 |
| Published in Issue | Year 2024 Volume: 4 Issue: 2 |
