Eduardo Lalla-Ruiz

Artificial intelligence hybrid heuristic based on tabu search for the dynamic berth allocation problem

This paper considers the Dynamic Berth Allocation Problem, in which vessels are assigned to discrete positions in berths. This problem, whose goal is to minimize the total time the vessels stay at the port, constitutes one of the most important processes at any containers terminal. We propose a hybrid metaheuristic that combines Tabu Search with Path Relinking, T^2S^@?+PR. The results reached by this hybrid algorithm are compared with the optimal values given by the best mathematical model that appears in the literature for this problem, GSPP, and with a tabu search algorithm from the literature, T^2S. For small instances, the algorithm T^2S^@?+PR is able to obtain most of the optimal solutions in an amount of computational time that is lower than the time required to solve the GSPP model. For medium and large size instances, GSPP cannot be solved to optimality, whereas the proposed hybrid algorithm outperforms T^2S. Moreover, the computational experiments carried out in this paper confirm the robustness of the proposed algorithm with respect to both the parameters governing the procedure and the problem size.

Biased random key genetic algorithm for the Tactical Berth Allocation Problem

The Tactical Berth Allocation Problem (TBAP) aims to allocate incoming ships to berthing positions and assign quay crane profiles to them (i.e. number of quay cranes per time step). The goals of the TBAP are both the minimization of the housekeeping costs derived from the transshipment container flows between ships, and the maximization of the total value of the quay crane profiles assigned to the ships. In order to obtain good quality solutions with considerably short computational effort, this paper proposes a biased random key genetic algorithm for solving this problem. The computational experiments and the comparison with other solutions approaches presented in the related literature for tackling the TBAP show that the proposed algorithm is applicable to efficiently solve this difficult and essential container terminal problem. The problem instances used in this paper are composed of both, those reported in the literature and a new benchmark suite proposed in this work for taking into consideration other realistic scenarios.

A cloud brokerage approach for solving the resource management problem in multi-cloud environments

The Cloud Resource Management Problem in multi-clouds is discussed and tackled.A Biased Random-Key Genetic Algorithm for solving the problem is proposed.Our proposal allows to find high-quality solutions within short computational times providing the basis for real-time cloud brokerage.New best solutions for some of the well-defined problem instances are obtained. Cloud computing is increasingly becoming a mainstream technology-delivery model from which companies and research aim to gain value. As different cloud providers offer cloud services in various forms, there is a huge potential of optimizing the selection of those services to better fulfill user-, i.e., consumer- and application-related requirements. Recently, multi-cloud environments have been introduced thus making it possible to execute applications not only on single-provider resources, but also by using resources from multiple cloud providers. Due to the growing complexity in cloud marketplaces, a cloud brokerage mechanism, interacting on behalf of the consumers with various cloud providers, can be used to provide decision support for consumers. In this paper, we address the Cloud Resource Management Problem in multi-cloud environments that is a recent optimization problem aimed at reducing the monetary cost and the execution time of consumer applications using Infrastructure as a Service of multiple cloud providers. Due to the fact that consumers require real-time and high-quality solutions to economically automate cloud resource management and corresponding deployment processes, we propose an efficient Biased Random-Key Genetic Algorithm. The computational experiments over a large benchmark suite generated based on real cloud market resources indicate that the performance of our approach outperforms the approaches proposed in the literature.

POPMUSIC as a matheuristic for the berth allocation problem

The Berth Allocation Problem aims at assigning and scheduling incoming vessels to berthing positions along the quay of a container terminal. This problem is a well-known optimization problem within maritime shipping. In order to address it, we propose two POPMUSIC (Partial Optimization Metaheuristic Under Special Intensification Conditions) approaches that incorporate an existing mathematical programming formulation. POPMUSIC is an efficient metaheuristic that may serve as blueprint for matheuristics approaches once hybridized with mathematical programming. In this regard, the use of exact methods for solving the sub-problems defined in the POPMUSIC template highlight an interoperation between metaheuristics and mathematical programming techniques, which provide a new type of approach for this problem. The computational experiments reveal excellent results.

An improved formulation for the multi-depot open vehicle routing problem

The multi-depot open vehicle routing problem (MDOVRP) is a recent hard combinatorial optimization problem that belongs to the vehicle routing problem family. In the MDOVRP, the vehicles depart from several depots and once they have delivered the goods to the last customers in their routes they are not required to return to the depots. In this work, we propose a new mixed integer programming formulation for the MDOVRP by improving some constraints from the literature and proposing new ones. The computational experience carried out over problem instances from the literature indicates that our proposed model outperforms the existing one.

A Set-Partitioning-based model for the Berth Allocation Problem under Time-Dependent Limitations

This paper addresses the Berth Allocation Problem under Time-Dependent Limitations. Its goals are to allocate and schedule the available berthing positions for the container vessels arriving toward a maritime container terminal under water depth and tidal constraints. As we discuss, the only optimization model found in the literature does not guarantee the feasibility of the solutions reported in all the cases and is limited to a two-period planning horizon, i.e., one low tide and one high tide period. In this work, we propose an alternative mathematical formulation based upon the Generalized Set Partitioning Problem, which considers a multi-period planning horizon and includes constraints related to berth and vessel time windows. The performance of our optimization model is compared with that of the mathematical model reported in the related literature. In this regard, the computational experiments indicate that our model outperforms the previous one from the literature in several terms: (i) it guarantees the feasibility and optimality of the solutions reported in all the cases, (ii) reduces the computational times about 88 percent on average in the problem instances from the literature, and (iii) presents reasonable computational times in new large problem instances.

A hybrid GRASP-VNS for ship routing and scheduling problem with discretized time windows

This paper addresses the Ship Routing and Scheduling Problem with Discretized Time Windows. Being one of the most relevant and challenging problems faced by decision makers from shipping companies, this tramp shipping problem lies in determining the set of contracts that should be served by each ship and the time windows that ships should use to serve each contract, with the aim of minimizing total costs. The use of discretized time windows allows for the consideration of a broad variety of features and practical constraints in a simple way. In order to solve this problem we propose a hybridization of a Greedy Randomized Adaptive Search Procedure and a Variable Neighborhood Search, which improves previous heuristics results found in the literature and requires very short computational time. Moreover, this algorithm is able to achieve the optimal results for many instances, demonstrating its good performance.

Modeling the Parallel Machine Scheduling Problem with Step Deteriorating Jobs

This paper addresses the Parallel Machine Scheduling Problem with Step Deteriorating Jobs. This problem arises from real environments in which processing a job later than at a specific time may require an extra processing time. This time-dependent variation is known in the literature as step deterioration and has several practical applications (production planning, computer programming, medicine treatment, equipment maintenance, etc.). In the problem tackled in this work we aim to minimize the total completion time on identical parallel machines where each job has a deteriorating date and observes a step function for the processing time. For solving it, we propose two novel mathematical models based on the Set Partitioning Problem (SPP) that improve the unique model proposed in the literature. The computational performance of these models implemented in a general purpose solver allows to compete with the best algorithms proposed in the literature. Finally, we provide some insights for managing similar SPP formulations when large-sized instances have to be addressed.

A Machine Learning-based system for berth scheduling at bulk terminals

Abstract The increasing volume of maritime freight is presented as a challenge to those skilled terminal managers seeking to maintain or increase their market share. In this context, an efficient management of scarce resources as berths arises as a reasonable option for reducing costs while enhancing the productivity of the overall terminal. In this work, we tackle the berth scheduling operations by considering the Bulk Berth Allocation Problem (Bulk-BAP). This problem, for a given yard layout and location of the cargo facilities, aims to coordinate the berthing and yard activities for giving service to those vessels arriving at the terminal. Considering the multitude of scenarios arising in this environment and theNo Free Lunch theorem, the drawback concerning the selection of the best algorithm for solving the Bulk-BAP in each particular case is addressed by a Machine Learning-based system. It provides, based on the scenario at hand, a ranking of algorithms sorted by appropriateness. The computational study shows an increase in the quality of the provided solutions when the algorithm to be used is selected according to the features of the instance instead of selecting the best algorithm on average.

Fuzzy Optimization Models for Seaside Port Logistics: Berthing and Quay Crane Scheduling

The service time of container vessels is the main indicator of the competitiveness of a maritime container terminal. Vessels have to be berthed along the quay, a subset of quay cranes must be assigned to them and work schedules have to be planned for unloading the import containers and loading the export containers. This work addresses the Tactical Berth Allocation Problem, in which the vessels are assigned to a given berth, and the Quay Crane Scheduling Problem, for which the work schedules of the quay cranes are determined. The nature of this environment gives rise to inaccurate knowledge about the information related to the incoming vessels. Therefore, the aforementioned optimization problems can be tackled by considering fuzzy arrival times for the vessels and fuzzy processing times for the loading/unloading operations. Two fuzzy mathematical models are provided to solve the problems at hand. The computational experiments carried out in this work corroborate the effectiveness of the proposed methodologies.