New approaches for solving the container stacking problem

Containers shipment has grown very fast during the last ten years, in Tanger Med port for example, 3Millions containers (80% in transshipment) TEUs (Twenty feet equivalent unit) has been changed during 2015 (Tanger Med Port authority ``TMPA{''} study March' 2016 {[} 1]), which correspond to an increase of 40% comparing with the last study made in 2012. Thus, our study will deal with the port management and the improvement of the operations processes. The aim of this paper is to define a new strategies to solve the container stacking problem (CSP) using an approach of optimization. Thus we define a new MIP (Mathematical Integer Program) to deal with the operational tasks in a containers terminal. In which we optimize the number of the stacks used to store a given number of inbound containers and also we minimize the related cost of the traveling distance for inbound containers between the sea side and the yard side. This paper is organized as follow: we introduce and locate first our problem, then we present the literature review of the CSP. The problem definition and the MIP introduction will be the subject of the next section, and we finish by presenting the findings and the future perspectives. As a proposed resolution approach for our MIP, we propose a developed genetic algorithm strategy (DGAS) as a metaheuristic and the Branch & Cut (B&C) as an exact method. Our main objective is to avoid reshuffles and to find out the best yard configuration to store inbound containers. The DGAS will be applied to an existing instances in the literature, and the obtained numerical results is compared with the Cplex results (B&C). The main inputs for our proposed framework are the height, weight, destination, type containers & yard bays, and the expected departure time (EDT). Our objective at the end is to have an optimized guide to the planners to easily define the unloading plan and the storage position for each container, giving an initial stacking state and a container demand.