Enhancing Maritime Container Logistics through Optimized Repair Scheduling

Container repair workshops are crucial for sustaining global supply chains, yet they face significant challenges, including capacity shortages and repair complexities, which lead to delays in container availability and disrupt the seamless flow of goods. This study addresses these issues by introducing a mixed integer linear programming model to optimize task scheduling and worker assignments in such workshops, aiming to minimize the makespan while adhering to technological precedence constraints. Computational experiments using the AIMMS software on various test instances, designed based on realistic datasets, highlight the model's effectiveness, demonstrating robust performance in small to moderate problem sizes, with solution times ranging from 0.01 seconds for small instances (5 tasks, 3 workers) to 0.98 seconds for moderate instances (20 tasks, 7 workers). This study underscore the potential for cost savings and enhanced supply chain resilience through improved operational efficiency, such as minimizing idle times and optimizing resource allocation to achieve efficient throughput, as illustrated in the case study for a 35 task scenario. This work advances container logistics research and equips supply chain executives with a data driven tool to address capacity shortages and streamline repair operations.