Abstract

Due to the special role of empty containers in the container transportation process, empty container repositioning is a focal point in the shipping industry. For this problem, highly efficient and feasible optimization models are critical in improving the benefit for shipping companies and increasing their market competitiveness. Operational decisions are affected by tactical ones. Aimed at this point, we propose a tactical and operational cooperative empty container repositioning optimization model. To cut the search space and obtain the optimal solution quickly, several initial solutions generation rules are extracted, based on business flow. Furthermore, the reachable shipping distance may change when the calling sequence is different. An algorithm which calculates the reachable shipping distance matrix between ports is presented to solve this problem. Simulated cases are used to test the proposed model and algorithm. The results show that the cases can cope with the tactical and operational cooperative empty container repositioning optimization model. Moreover, some interesting conclusions also are deduced about the relationships among number of calling ports, total profits, leasing cost, calling port fee, number of Empty Containers Repositioned (ECR), and laden containers. All these can guide and assist the various decisions to be made. According to the homepage of Symmetry, its subject areas include Mathematics, Computer Science, Theory, and Methods. Their branches include information theory, computer-aided design, and so on. The topic of our paper is to solve this engineering application problem by using a mathematical optimization model and computer methods. That is, applying mathematical theory and computer methods to make decision results for the empty container repositioning problem in the shipping industry. It has certain economic value and practical significance. Obviously, it is consistent with the theme of Symmetry.

Highlights

  • Container transportation has become a widely-used mode of transportation, due to its advantages of efficient and reliable operation, comprehensive service, low cost, and easy intermodal transport.due to the imbalance of national trade and other complex factors, such as the location of container maintenance ports, different ports of production and use, and so on, the distribution of empty containers in ports is almost unbalanced

  • There are huge differences in the various costs consisting of laden container transportation, empty container repositioning (ECR), calling ports, and calling sequence of vessels, as well as the operating costs of liners

  • The Algorithm 1 was written in Java and the model from Section 3.4 was solved by means of CPLEX 12.5

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Summary

Introduction

Container transportation has become a widely-used mode of transportation, due to its advantages of efficient and reliable operation, comprehensive service, low cost, and easy intermodal transport.due to the imbalance of national trade and other complex factors, such as the location of container maintenance ports, different ports of production and use, and so on, the distribution of empty containers in ports is almost unbalanced. There are huge differences in the various costs consisting of laden container transportation, empty container repositioning (ECR), calling ports, and calling sequence of vessels, as well as the operating costs of liners. Moveover, since these above problems may by interrelated, it is necessary to consider them simultaneously. Since these above problems may by interrelated, it is necessary to consider them simultaneously This problem has become one of the key problems to be dealt with urgently for shipping companies to consider the calling port set, calling sequence, and the transportation of two typical containers together, so as to make the overall cost as low as possible. One of the research streams about this kind of optimization solution is heuristic algorithms, which mainly imitate natural body algorithms, such as genetic algorithms (GA), ant colony algorithms (ACA), simulated annealing (SA), artificial neural networks (ANN), list search algorithms (ST), and so on

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