Optimization of Empty Container Repositioning in Liner Shipping

Abstract

Empty container repositioning (ECR), which arises due to imbalances in world trade, causes extra costs for the container liner carrier companies. Therefore, one of the main objectives of all liner carriers is to reduce ECR costs. Since ECR decisions involve too many parameters, constraints and variables, the plans based on real-life experiences cannot be effective and are very costly. For this purpose, this study introduces two mathematical programming models in order to make ECR plans faster, more efficient and at the lowest cost. The first mathematical programming model developed in this study is a mixed-integer linear programming (MILP) model and the second mathematical programming model is a scenario-based stochastic programming (SP) model, which minimize the total ECR costs. Unlike the deterministic model, the SP model takes into account the uncertainty in container demand. Both models have been tested with real data taken from a liner carrier company. The numerical results showed that, in a reasonable computational time, both models provide better results than real-life applications of the liner carrier company.

Optimizing Shipping Operations through Real-Time Monitoring and Control

“…To effectively manage its container assets and optimize turnaround time, a shipping company requires a tool or a system that can monitor and control container stripping processes in a practical and realtime manner. Examples can be derived from Martius et al [5] who used machine learning to forecast the worldwide empty container availability, and Gençer and Demir [6] who used mixed-integer linear programming and scenario-based stochastic programming to optimize the empty containers. In addition, Budipriyanto et al [7] used a simulation approach to solve the empty container problem.…”