A decomposition heuristics for the container ship stowage problem

Abstract

In this paper we face the problem of stowing a containership, referred to as the Master Bay Plan Problem (MBPP); this problem is difficult to solve due to its combinatorial nature and the constraints related to both the ship and the containers. We present a decomposition approach that allows us to assign a priori the bays of a containership to the set of containers to be loaded according to their final destination, such that different portions of the ship are independently considered for the stowage. Then, we find the optimal solution of each subset of bays by using a 0/1 Linear Programming model. Finally, we check the global ship stability of the overall stowage plan and look for its feasibility by using an exchange algorithm which is based on local search techniques. The validation of the proposed approach is performed with some real life test cases.

Robust storage loading problems with stacking and payload constraints

“…Due to (4) the stacking constraints s ij are satisfied and no item is placed to a location where no item is stacked below. Inequalities (5) ensure that the payload violations v ql are computed correctly.…”

“…For example, such stacking constraints appear in the context of the master bay plan problem (MBPP) (cf. [4,5,40]). Formally, this problem is to determine a plan of minimum operating time for stowing a set of containers of different types into available locations of a containership, with respect to some structural and operative constraints (e.g., a restriction on the maximum weight of the containership, containers retrieved later may not be stored on top of containers that are retrieved earlier).…”

“…In [4] the authors propose a binary linear programming model for the problem, present a heuristic approach, and give some prestowage rules for being able to solve the model. Another solution approach for the same problem is presented in [5] based on a three-phase algorithm using the idea of splitting the ship into different parts and assigning the containers to them on the basis of the containers' destinations. In [40] a heuristic method based on a relationship of the MBPP to three-dimensional bin packing problems is proposed.…”

Berth allocation planning in Seville inland port by simulation and optimisation

“…Ports are very important to the transport logistic networks, therefore all of its operations must be optimised, according to Ambrosino et al [13]. Some of the main operations are: container pre-marshalling problem, landside transport, stowage planning problem, yard allocation problem, etc in Voß S and Stahlbock R (2004) [14] and Steenken D and, Voß S. (2008) [15] which has been one of the most complete reviews and one of the most important papers.…”

Solution of the 3D Stochastic Stowage Planning for Con-tainer Ships through Representation by Rules

“…Ambrosino [1,2] also introduced and tested the notion of stability by considering that each container had a weight in the model. Ambrosino also tested ant colony optimization (ACO) in a real problem for the SECH Terminal of Genoa, Italy, and a container ship whose capacity was 1800 TEUs for instances with 2 or 3 destinations, 4920 TEUs for 4 destinations, and 5510 TEUs for 5 destinations.…”