Daniela Ambrosino

A decomposition heuristics for the container ship stowage problem

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.

A heuristic based on multi-exchange techniques for a regional fleet assignment location-routing problem

We deal with a distribution network design problem that involves location, fleet assignment and routing decisions. Specifically, the distribution network under investigation is characterized by one central depot, a set of customers split into regions, and a heterogeneous fleet of vehicles. The goal is to locate one regional depot in each region, to assign some vehicles to each region, and to design the vehicles routes, each starting and ending at the central depot, in such a way that the regional depot is visited once by all vehicles assigned to the corresponding region, the vehicle capacities are not exceeded, the customer demands are satisfied and the overall distribution cost is minimized. The study has been motivated by a real life application related to a company operating in the North of Italy. We propose a two-phase heuristic for this problem which first determines an initial feasible solution, and then improves it by using very large neighborhood search techniques. We characterize a local search neighborhood in terms of path and cyclic exchanges of customers among routes, the so-called multi-exchanges. We also extend the definition of multi-exchange in such a way to allow fleet assignment modifications. We then complement the multi-exchange methodology with a more classic relocation mechanism, designed to perform depot location adjustments. The proposed approach has been validated with the real life case study as well as with several randomly generated instances. The results of the extensive computational experimentation show that the proposed approach is very promising. In the case of instances characterized by a small or a medium size the heuristic was able to compute very good quality solutions in a limited amount of time. In the case of very large instances, including the real case study, the heuristic proved to be the only tool for determining feasible solutions to the problem, whereas a commercial solver such as CPLEX generally was not able to discover any feasible solution within a time limit of 25h.

An experimental comparison of different heuristics for the master bay plan problem

Different heuristics for the problem of determining stowage plans for containerships, that is the so called Master Bay Plan Problem (MBPP), are compared. The first approach is a tabu search (TS) heuristic and it has been recently presented in literature. Two new solution procedures are proposed in this paper: a fast simple constructive loading heuristic (LH) and an ant colony optimization (ACO) algorithm. An extensive computational experimentation performed on both random and real size instances is reported and conclusions on the appropriateness of the tested approaches for the MBPP are drawn.

Modeling and solving the train load planning problem in seaport container terminals

In this paper we present two mathematical formulations and a heuristic approach for the train load planning problem of import containers at a seaport container terminal. The problem consists of determining how to assign a set of containers of different length and weight to the wagons of a train in order to satisfy capacity constraints of both the wagons and the train, while minimizing the rehandling operations in the stocking area where containers are waiting for being loaded on trains and maximizing the train utilization. Some computational results will be reported in the paper in which the heuristic approach is compared with the solution of the mathematical programming formulation.

Computational evaluation of a MIP model for multi-port stowage planning problems

In this paper, we consider the problem of determining stowage plans for containers into ships having to visit a given number of ports in their circular route. The problem is denoted Multi-Port Master Bay Plan Problem (MP-MBPP). In practice, the MP-MBPP consists in determining how to stow a given set of containers, split into different groups, according to their size, type, class of weight and destination, into bay locations, either on the deck or in the stow. Some structural and operational constraints, related to the containers, the ship and the maritime terminals, have to be satisfied. The single port MBPP is a NP-hard optimization problem, and has been proposed in the literature from 2001. From then, some variants of the problem have been presented, together with the related solution methods, mainly aimed at including in the corresponding models realistic features, required as a consequence of the naval gigantism. As a novel issue, in the present work, we look for stowage plans where the set of containers to be loaded on board at each port of the route consists of standard, reefer and open top ones. Hatches positions in the ships are considered too. We present a new mixed integer programming (MIP) model for the MP-MBPP able to manage realistic scenarios and find stowage plans for containerships up to 18,000 TEUs. The model is finalized to be solved with a commercial MIP solver. The reported computational experimentation shows that the model is very efficient and could be fruitfully used for facing real-size instances of the problem.

An Integrated Simulation And Optimization Approach For Seaside Terminal Operations.

In this paper we focus our attention on the operational decision problems related to the seaside area of maritime container terminals. In particular, we face the Quay Crane Assignment Problem (QCAP) and Quay Crane Scheduling Problem (QCSP) with an integrated simulation-optimization approach. A 0/1 MIP model is developed in order to determine the optimal assignment, on a shift basis, of QCs to bays of each ship served by the terminal during a given planning horizon, referred as Bay_QCAP. The optimization model solutions are used as input parameters for a Discrete Event Simulation (DES) model able to reproduce the system behaviour taking into account its stochastic nature and complexity. The framework can be used for evaluating the impact on the seaside terminal performance of the optimized solutions and the effects of different operative decisions related to the scheduling of QCs. The framework is going to be applied to a real case study pertaining to the Southern European Container Hub (SECH), sited in the Port of Genoa, Italy.

A capacitated hub location problem in freight logistics multimodal networks

In this paper we deal with a capacitated hub location problem arising in a freight logistics context; in particular, we have the need of locating logistics platforms for containers travelling via road and rail. The problem is modelled on a weighed multimodal network. We give a mixed integer linear programming model for the problem, having the goal of minimizing the location and shipping costs. The proposed formulation presents some novel features for modelling capacity bounds that are given both for the candidate hub nodes and the arcs incident to them; further, the containerised origin-destination (

Using a Bin Packing Approach for Stowing Hazardous Containers into Containerships

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