Eduardo García Pardo | GRAFO Research Group

A Competitive Platform for Automated Evaluation of Optimization Algorithms in Computing Education

Wed, 01 Jul 2026 00:00:00 +0000

Optimizing the two-dimensional bandwidth problem under the maximum norm using exact and heuristic approaches

Mon, 01 Jun 2026 00:00:00 +0000

Optimization in Asset Selection for Asset-Backed Structured Finance Operations

Tue, 20 Jan 2026 00:00:00 +0000

Principal investigators: J. Manuel Colmenar, Eduardo García Pardo
Department: Computer Science and Statistics
Funding entity: Confidential (not disclosed)
Period: January 2026 – July 2026

Description

This R&D contract project addresses the optimization of asset selection in the context of asset-backed structured finance operations. Optimization models and algorithms are developed to efficiently select asset portfolios while satisfying the financial and risk criteria specific to this type of operation.

For confidentiality reasons, no further information is provided about the contracting entity or the specific details of the project.

Heurísticas contra el caos aéreo: un caso real de resolución de incidencias operativas en una aerolínea

Thu, 01 Jan 2026 00:00:00 +0000

Mathematical formulation and heuristic methods for the Min--Max Sitting Arrangement Problem in the Cycle

Thu, 01 Jan 2026 00:00:00 +0000

Multi-objective Variable Neighborhood Descent for the Inference of Test Models from User Bug Reports in Software Systems

Thu, 01 Jan 2026 00:00:00 +0000

Variable Formulation Search for the Cyclic Min-Max Sitting Arrangement Problem

Thu, 01 Jan 2026 00:00:00 +0000

Computer Science for Supply Chain Optimization

Mon, 01 Sep 2025 00:00:00 +0000

Principal investigators: Jesús Sánchez-Oro Calvo, Eduardo García Pardo
Funding entity: Agencia Estatal de Investigación
External reference: PID2024-160226OB-C22
Internal reference: M4027
Duration: 01/09/2025 – 31/08/2028

Abstract:

Supply chain optimization is one of the major challenges of modern logistics. This project addresses this problem from a computer science perspective, applying advanced combinatorial optimization and metaheuristic techniques to improve the planning, coordination, and efficiency of the different links in the chain: procurement, production, distribution, and inventory management. The goal is to develop models and algorithms capable of obtaining high-quality solutions in dynamic environments with multiple constraints, contributing to cost reduction and improved sustainability of logistics operations.

Multi-armed bandit for the cyclic minimum sitting arrangement problem

Tue, 01 Jul 2025 00:00:00 +0000

Graph Layout Problems

Wed, 01 Jan 2025 00:00:00 +0000

Heurı́sticas multiarranque para el problema de CMMSA

Wed, 01 Jan 2025 00:00:00 +0000

Método constructivo para la generación de modelos de comportamiento a partir de reportes de usuario en sistemas software

Wed, 01 Jan 2025 00:00:00 +0000

Search-Based Software Engineering Problems

Wed, 01 Jan 2025 00:00:00 +0000

Multi-objective general variable neighborhood search for software maintainability optimization

Mon, 01 Jul 2024 00:00:00 +0000

Strategic oscillation tabu search for improved hierarchical graph drawing

Sat, 01 Jun 2024 00:00:00 +0000

WORDLE IN THE CLASSROOM: A GAME-CHANGING APPROACH TO ACTIVE LEARNING

Fri, 01 Mar 2024 00:00:00 +0000

General Variable Neighborhood Search for the optimization of software quality

Thu, 01 Feb 2024 00:00:00 +0000

Optimization of Production through Lot-Sizing and Scheduling on Parallel Production Lines

Thu, 01 Feb 2024 00:00:00 +0000

Principal investigators: Sergio Cavero, Eduardo G. Pardo
Type: R&D Contract Project
Funding entity: Confidential (not disclosed)
Period: February 2024 – December 2024

Description

This R&D contract project addresses the optimization of industrial production processes, specifically the joint problem of lot-sizing and scheduling on parallel production lines. Models and algorithms are designed to minimize production costs and times while satisfying capacity constraints and delivery deadlines.

For confidentiality reasons, no further information is provided about the contracting entity or the specific details of the project.

Solving a short sea inventory routing problem in the oil industry

Thu, 01 Feb 2024 00:00:00 +0000

A Variable Formulation Search Approach for Three Graph Layout Problems

Mon, 01 Jan 2024 00:00:00 +0000

A Variable Neighborhood Search Approach for the S-labeling Problem

Mon, 01 Jan 2024 00:00:00 +0000

Exploring the Integration of General Variable Neighborhood Search with Exact Procedures for the Optimization of the Order Batching Problem

Mon, 01 Jan 2024 00:00:00 +0000

Optimization of Fairness and Accuracy on Logistic Regression Models

Mon, 01 Jan 2024 00:00:00 +0000

The time-window strategy in the online order batching problem

Sun, 01 Oct 2023 00:00:00 +0000

BVNS for the Minimum Sitting Arrangement Problem in a Cycle

Sun, 01 Jan 2023 00:00:00 +0000

Multi-objective Variable Neighborhood Search for Improving Software Modularity

Sun, 01 Jan 2023 00:00:00 +0000

Order Batching Problems: taxonomy and literature review

Sun, 01 Jan 2023 00:00:00 +0000

Variable Neighborhood Descent for Software Quality Optimization

Sun, 01 Jan 2023 00:00:00 +0000

Efficient iterated greedy for the two-dimensional bandwidth minimization problem

Thu, 01 Sep 2022 00:00:00 +0000

SCOOP – Computer Science for Supply Chain OptimizatiOn Problems

Fri, 24 Jun 2022 00:00:00 +0000

Principal investigator: Eduardo García Pardo y Jesús Sánchez-Oro Calvo Funding entities: Ministerio de Economía y Competitividad (PID2021-125709OA-C22). Duration: 2022 – 2024.

Summary:

This scientific proposal focuses on solving hard optimization problems belonging to the supply chain management, by using an efficient combination of Operations Research and Artificial Intelligence techniques. The Supply Chain refers to the series of resources, activities, and organizations that move materials and products through, on their journey from initial suppliers to final customers. The Supply Chain is the core of the operations that take place in industry.

Particularly, we center our attention in the processes and systems related to warehouses, including the inbound and outbound logistics. The optimization of the processes within this context might suppose a large reduction of the costs and therefore an increase in the profits. Specifically, in this project, we propose the study of four families of optimization problems:

Order Batching: this family of problems is focused on the activities related to the picking of orders in a warehouse when the picking policy follows an order batching strategy (i.e., several orders are grouped into a batch before starting the picking route). Many optimization problems are classified under this family: offline (i.e., static)/online (i.e., dynamic); single-picker/multiple-pickers; minimizing different objective functions (time, route length, working balance, costs, etc.).
Routing: these problems hold a central place in the supply chain. In this family we can find node routing problems (simply called vehicle routing problems) in which customers can be represented by nodes in a network; and arc routing problems, in which the service is performed on the arcs or edges of a network. In this project, we target three realistic variants of routing problems that appear in the context of supply chain management: close-enough, stochastic, and multi-objective routing models.
Monitoring: networks are, an essential part of every activity in professional scenarios. In the supply chain, there are several networks whose security must be guaranteed such as: communication networks, transportation networks or surveillance networks. In this context, many optimization problems appear such as: selecting a number of points that maximize an area under surveillance, determining which network connections must be reinforced, or choosing which warehouses dominate/supply others.
Storage and location: in this research line, we are interested in both, studying the location of warehouses (within the strategic decision of a company) and also studying the right location of the products within the warehouse (from a tactical point of view). These applications can be modeled as, the so-called, capacitated and generalized dispersion problems (CDP).

To solve the aforementioned optimization problems, we will make use of heuristic and metaheuristic procedures. Belonging to the Artificial Intelligence field, those techniques are able to provide high-quality approximate solutions (some times even optimal) in short computing times. These techniques are suitable for tackling hard optimization tasks in real scenarios where the quality of the solutions is almost as important as the time needed to find them.

This project is built upon the strong and successful collaboration of the two complementary research groups involved in this proposal: mathematicians (Univ. de Valencia) coordinated by Profs. Martí and Martínez-Gavara, and computer scientists (Univ. Rey Juan Carlos) coordinated by Profs. Pardo and Sanchez-Oro

A variable neighborhood search approach for cyclic bandwidth sum problem

Fri, 01 Apr 2022 00:00:00 +0000

An efficient heuristic algorithm for software module clustering optimization

Fri, 01 Apr 2022 00:00:00 +0000

Optimization of code caves in malware binaries to evade Machine Learning detectors

Tue, 01 Feb 2022 00:00:00 +0000

A general variable neighborhood search for the cyclic antibandwidth problem

Sat, 01 Jan 2022 00:00:00 +0000

A heuristic approach for the online order batching problem with multiple pickers

Fri, 01 Jan 2021 00:00:00 +0000

BVNS Approach for the Order Processing in Parallel Picking Workstations

Fri, 01 Jan 2021 00:00:00 +0000

Influence of the Alternative Objective Functions in the Optimization of the Cyclic Cutwidth Minimization Problem

Fri, 01 Jan 2021 00:00:00 +0000

Multi-Objective GRASP for Maximizing Diversity

Fri, 01 Jan 2021 00:00:00 +0000

Un enfoque multi-objetivo para el problema de la máxima diversidad

Fri, 01 Jan 2021 00:00:00 +0000

Basic variable neighborhood search for the minimum sitting arrangement problem