In this paper, a nonlinear model for medium-term hydro-thermal scheduling problems with transmission constraints is presented. A nonlinear formulation of hydro power production functions of discharge and storage is used for proper representation of head variation in cascaded reservoirs. Transmission networks are formulated as a direct-current power flow model with support to load attainment over multiple levels representing peak and off-peak hours, enabling varying degrees of both network topology and load balance nodes representation over time stages. Sparse matrix structures that arise from the mathematical formulation allow fast decoupled computation of search directions in a primal-dual interior-point framework. Computational tests with very large case studies are presented. Analysis of numerical algorithm convergence is provided along with insightful optimal solution interpretative commentary.

This paper formulates the 3D containership loading planning problem (3D CLPP) and also proposes a new and compact representation to efficiently solve it. The key objective of stowage planning is to minimise the number of container movements and also the ship's instability. The binary formulation of this problem is properly described and an alternative formulation called Representation by Rules is proposed. This new representation is combined with three metaheuristics - genetic algorithm, simulated annealing, and beam search - to solve the 3D CLPP in a manner that ensures that every solution analysed in the optimisation process is compact and feasible.

The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners.