Papers


López-Plata, I., Expósito-Izquierdo, C., Lalla-Ruiz, E., Melián-Batista, B., Moreno-Vega, J.M. (2022). A Greedy Randomized Adaptive Search with Probabilistic Learning for solving the Uncapacitated Plant Cycle Location Problem. International Journal of Interactive Multimedia and Artificial Intelligence.

This paper addresses the Uncapacitated Plant Cycle Location Problem, a location-routing problem aimed at determining a subset of locations to set up plants dedicated to serving customers. An optimization model is proposed to define the problem and solve it in small-size problem instances. With the aim to solve the problem in medium and big instances, a Greedy Randomized Adaptive Search Procedure with Probabilistic Learning Model is created. The computational results indicate the high performance of this algorithm in terms of the quality of solutions and on computational time.

Robinson, Y. H., Vimal, S., Julie, E. G., Khari, M., Expósito-Izquierdo, C., & Martínez, J. (2021). Hybrid optimization routing management for autonomous underwater vehicle in the internet of underwater things. Earth Science Informatics, 14(1), 441-456.

The paper proposes a routing strategy used to provide energy proficiency while performing cluster-based routing in Internet of Underwater Things. The techniques is demonstrated to be highly effective and efficient when reducing the energy consumption.

López-Plata, I., Expósito-Izquierdo, C., & Moreno-Vega, J. M. (2019). Minimizing the operating cost of block retrieval operations in stacking facilities. Computers & Industrial Engineering, 136, 436-452.

The paper solves the relocation problem in a maritime container terminal minimizing the “operating cost”, a cost proportional to the distance covered by the crane to move the containers that can be adapted to different measures like time, energy consumption, and more. An optimization model is proposed to define the problem, and an A* exact algorithm and a hybrid heuristic algorithm are created to solve it. The heuristic technique is aimed to solve the problem with good quality results in short computational times.

Castilla-Rodríguez, I., Expósito-Izquierdo, C., Melián-Batista, B., Aguilar, R. M., & Moreno-Vega, J. M. (2020). Simulation-optimization for the management of the transshipment operations at maritime container terminals. Expert Systems with Applications, 139, 112852.

An intelligent system which integrates Artificial Intelligence techniques and simulation tools is proposed to aid terminal managers. The system combines an intelligent evolutionary algorithm to generate high quality schedules for the cranes with a simulation model that incorporates uncertainty and the impact of internal delivery vehicles.

López-Plata, I., Expósito-Izquierdo, C., Lalla-Ruiz, E., Melián-Batista, B., & Moreno-Vega, J. M. (2017). Minimizing the Waiting Times of block retrieval operations in stacking facilities. Computers & Industrial Engineering, 103, 70-84.

This paper solves the relocation problem in maritime container terminals minimizing the waiting times of a customer for a container. The main aim of this problem is improving the quality of service (QoS) of the terminal. An optimization model and a heuristic algorithm are proposed to solve the problem. The heuristic algorithm returns good quality solutions in short computational times.

Expósito-Izquierdo, C., López-Plata, I., & Moreno-Vega, J. M. (2015). Problem MetaHeuristic Solver: An educational tool aimed at studying heuristic optimization methods. Computer Applications in Engineering Education, 23(6), 897-909.

In this paper the software Problem MetaHeuristic Solver is described. This tool allows to create, configure and execute heuristic algorithms in different kinds of problems using an intuitive user interface. The main goal of the software is the study of the behavior of heuristic algorithms in different situations and extract useful information about its execution, with the aim to help in education of this type of algorithms.