Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import

Maritime terminals need more efficiency in their handling operations due to the phenomenal evolution of world container traffic, and to the increase of the container ship capacity. In this work, we propose a new integrated modeling considering the optimization of maritime container terminals using straddle carriers. The problem is considered at import. We study a combination between two known problems, the first is the storage location assignment problem, and the second is the straddle carrier scheduling problem. This approach, which combines between two chronologically successive problems, leads to the use of multi‐objective optimization. In fact, we study the multi‐objective integrated problem of location assignment and Straddle carrier Scheduling (IPLASS) in maritime container terminal at import. We prove that the problem is NP‐Complete. The objective is to minimize the operating cost which we evaluate according to eight components: the date of last task called makespan, the total vehicle operating time, the total storage bay occupation time, the number of vehicles used, the number of storage bays used, the number of storage locations used, and two different costs of storage location assignment. The location assignment costs are evaluated in order to facilitate the containers transfer for deliveries. We assume that the operating cost is a function of these components and that the influence of each component is variable and dependent on different parameters. These parameters are essentially: the number of quays in the terminal, the straddle carrier traffic layout, the number of container ships to serve in the terminal, the influence of concurrent operations in the terminal, the storage space configuration, the number of free storage bays, the number of free straddle carriers, the number of free quay cranes, the mobility of quay cranes, etc. To solve IPLASS efficiently, we propose an adapted multi‐objective Tabu Search algorithm. Lower‐bound evaluations are introduced to perform approximation of Pareto Front. To explore efficiently the non‐convex Pareto Front Region, we evaluate also a maximized distance adapted to the set of objectives. Indicators of efficiency are developed to propose distinguished solutions to operator. 2D‐projections of approximated Pareto Frontier are given to more understand the efficiency of proposed solutions.