The Ship Stowage Planning Problem, that is determining the stowage position of containers in a contain- ership, is usually faced by the shipping line to optimize vessel related objectives (i.e. to maximize the ship utilization and minimize the number of on-board shifts, or re-stows, during the port rotation). However also the terminal management can play an active role in planning the stowage of containerships, while optimizing the operative costs related to the ship-loading. This planning process has received a lot of attention by researchers, but the wide variety of settings, assumptions, and objectives considered in the literature high- lights the lack of a commonly accepted view of the problem. For these reasons we propose a classification scheme, based on the most significant features of the problem addressed, and review some relevant contri- butions to the scientific literature accordingly. We believe that our effort contributes to close a gap, given that analogous classifications have recently been proposed, for example, for the Berth Allocation, the Quay Crane Assignment, the Quay Crane Scheduling problems and their integration ([1], [2]). Then we focus on the Terminal-oriented Ship Stowage Planning Problem at a DTS terminal that, to our knowledge, has not been investigated in the literature. We propose a Binary Integer Model whose objective is to minimize the yard-to-quay transportation times of the containers and the yard-shifts. We also present a Tabu Search Algorithm for finding sub-optimal solutions of our problem. The Tabu Search algorithm has been tested on a set of real instances from the Gioia Tauro container terminal, and on further instances generated from the real ones by modifying the distribution of the export containers in the yard so as to force their pick-up to possibly cause many yard-shifts. The computational results show the superiority of the heuristics over CPLEX. Actually the Tabu Search Algorithm is able to find optimal or near-optimal solutions with a little computational burden on all the test instances, even when CPLEX fails to find a feasible solution within the time-limit ([3]).
The Ship Stowage Planning Problem: survey of the literature and focus on the terminal management view
2014
Abstract
The Ship Stowage Planning Problem, that is determining the stowage position of containers in a contain- ership, is usually faced by the shipping line to optimize vessel related objectives (i.e. to maximize the ship utilization and minimize the number of on-board shifts, or re-stows, during the port rotation). However also the terminal management can play an active role in planning the stowage of containerships, while optimizing the operative costs related to the ship-loading. This planning process has received a lot of attention by researchers, but the wide variety of settings, assumptions, and objectives considered in the literature high- lights the lack of a commonly accepted view of the problem. For these reasons we propose a classification scheme, based on the most significant features of the problem addressed, and review some relevant contri- butions to the scientific literature accordingly. We believe that our effort contributes to close a gap, given that analogous classifications have recently been proposed, for example, for the Berth Allocation, the Quay Crane Assignment, the Quay Crane Scheduling problems and their integration ([1], [2]). Then we focus on the Terminal-oriented Ship Stowage Planning Problem at a DTS terminal that, to our knowledge, has not been investigated in the literature. We propose a Binary Integer Model whose objective is to minimize the yard-to-quay transportation times of the containers and the yard-shifts. We also present a Tabu Search Algorithm for finding sub-optimal solutions of our problem. The Tabu Search algorithm has been tested on a set of real instances from the Gioia Tauro container terminal, and on further instances generated from the real ones by modifying the distribution of the export containers in the yard so as to force their pick-up to possibly cause many yard-shifts. The computational results show the superiority of the heuristics over CPLEX. Actually the Tabu Search Algorithm is able to find optimal or near-optimal solutions with a little computational burden on all the test instances, even when CPLEX fails to find a feasible solution within the time-limit ([3]).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
