Ministerio | GRAFO Research Group

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.

New Advances in the Holistic Methodology for Configuration, Comparison and Evaluation of Metaheuristics

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

Principal investigators: José Manuel Colmenar Verdugo, Abraham Duarte Muñoz
Funding entity: Agencia Estatal de Investigación
External reference: PID2024-156045NB-I00
Internal reference: M4018
Duration: 01/09/2025 – 31/08/2028
Funding amount: 124,250 €

Abstract:

Metaheuristics are methods for solving combinatorial optimization problems that have proven highly effective in practice. However, their configuration, comparison, and evaluation remains a complex and poorly standardized process. This project proposes advancing a holistic methodology that coherently integrates the different aspects of experimental design with metaheuristics: parameter selection and tuning, the design of representative benchmarks, statistical comparison protocols, and performance evaluation under real-world conditions. The goal is to provide the research community with rigorous methodological tools and guidelines that allow reliable and reproducible conclusions to be drawn about the behavior of these techniques.

AI4DDS - Artificial Intelligence for Data Driven Solutions

Fri, 19 Apr 2024 00:00:00 +0000

Principal investigator: Jesús Sánchez-Oro Calvo
Funding entities: Ministerio para la Transformación Digital y de la Función Pública (Ref. TSI-100930-2023-3)
Duration: 19/04/2024 - 31/12/2026
Budget: 374.999,91

HOMERO – A new holistic methodology for configuration, comparison, and evaluation of metaheuristics

Tue, 02 Aug 2022 00:00:00 +0000

Principal investigators: Abraham Duarte, J. Manuel Colmenar. Funding entities: Ministerio de Ciencia e Innovación (PID2021-126605NB-I00). Duration: 01/01/2022 – 31/12/2024.

Abstract:

Metaheuristics (MHs) are among the most prominent and successful techniques to solve a large amount of complex and computationally hard combinatorial and numerical optimization problems arising in human activities, such as economics (e.g., portfolio selection), industry (e.g., scheduling or logistics), or engineering (e.g., routing). MHs can be seen as general algorithmic frameworks that require relatively few modifications to tackle a specific problem. While MHs do not guarantee the optimality of the obtained solutions (unlike exact algorithms), and do not define how close the obtained solutions are from the optimal ones (unlike approximation algorithms), they do provide “acceptable” solutions in reasonable computing times for hard and complex problems in science and engineering.

Fred Glover coined the term metaheuristic in 1986. He wanted to define “a master process that guides and modifies other subordinate heuristics to explore solutions beyond simple local optimality”. MHs constitute a very diverse family of optimization algorithms including methods such as Tabu Search, Greedy Randomized Adaptive Search Procedures, Scatter Search, Variable Neighborhood Search, Iterated Local Search and Multi-start Methods. In addition, bio-inspired metaheuristics began with Genetic Algorithms, and, among others, they include many different methods like Simulated Annealing, Genetic Programming, Memetic Algorithms, Ant Colony Optimization, and Grammatical Evolution.

Research in MHs has mainly focused on designing effective and efficient algorithms to solve hard optimization problems. This line of research has been definitely very successful, as witnessed by thousands of journal and conference papers, hundreds of authored and edited books, and dedicated national/international conferences. Nevertheless, despite the large amount of knowledge that has been gathered, there is no established holistic methodology to help researchers when designing procedures for optimization problems in: benchmark instance selection, parameter tunning, sensitivity analysis, runtime comparison, and reproducibility, among others. Indeed, we are lacking some of the fundamental understanding of MHs that would allow us to create such a methodology.

The main objective of this project is to develop a holistic methodology with a scientific support for the application of MHs on optimization problems. The methodology will be complemented by a set of open-source software tools that will be accessible to the whole scientific community.

Although a theoretical approach to enlarge the body of knowledge of the metaheuristic foundations will be considered, this research will not be conducted in a purely abstract manner. On the contrary, a problem-driven approach will be followed by choosing application domains in which the use of MHs is particularly promising. Specifically, during the development of the project the following families of problems will be studied: Facility Location, Facility Layout, Graph Partitioning, Energy Consumption Prediction, and Energy Distribution.

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

DIETHA – Design, Implementation and Exploitation of Advanced Heuristic Techniques

Wed, 02 Dec 2020 00:00:00 +0000

Principal investigator: Abraham Duarte Funding entities: Ministerio de Economía y Competitividad (TIN2012-35632-C02-02). Duration: 01/01/2013 – 31/12/2015,

Abstract:

There are a large amount of problems that are framed within the context of combinatorial optimization characterized by the high interest associated with their practical resolution. This project deals with five distinct families of combinatorial problems. These are:

Ordering problems: with applications in VLSI design or in the efficient resolution of systems of equations. Location problems: with interest in telecommunication applications such as distribution of signal regenerators or network design. Graph-based problems: with applications in the distribution of electronic devices in electronic boards or in image segmentation. Routing problems: by focusing on multi-objective problems with applications in the transport of hazardous materials or in recommendation systems. Selection problems: with applications in the construction of diverse groups or clustering of documents. The methodology to solve the problems described above are metaheuristics procedures, among which we can highlight evolutionary algorithms, tabu search, variable neighborhood search or GRASP, to name some of the best known. For each combinatorial problem, we will propose the most suitable metaheuristic according to their mathematical structure or model. We will mainly focus on design of novel strategies to obtain high quality solutions. Besides, it is expected to figure out general strategies that can be easily applied to other related problems. We will also focus on the efficient and flexible implementation of those strategies, taking advantage of new language programming features and multi-core microprocessors. Finally we will focus on exploitation through a management platform, which integrates the problems addressed above. Simultaneously, we will develop an application to put into production (in the companies interested in our research project) the algorithms developed during the project.

In addition to solve the problems presented above, a second objective of the project is to develop the metaheuristic methodologies themselves. To successfully meet this challenge the research team has researcher Nenad Mladenovic, which developed in conjunction with Pierre Hansen the variable neighborhood search methodology.

All these problems will be integrated into Optsicom, a software tool that allow the execution of algorithms devoted to solve optimization problems and analyze the associated results. Optsicom can be used at two levels: as an end-user or as a researcher on heuristic methods. In this line, the problems integrated in Optsicom will also be available via the Web at Optsicom Repository, a web platform for comprehensive management of optimization problems. This platform will post all the information associated with an optimization problem. For each problem, it is expected to store the description, algorithms, instances, experimental results and relevant references. Furthermore, the results obtained by executing the algorithms can be compared using different statistical tests that are available as part of the software tool.

DIETHA II – Design, Implementation and Exploitation of Advanced Heuristic Techniques

Wed, 02 Dec 2020 00:00:00 +0000

Principal investigator: Abraham Duarte. Funding entities: Ministerio de Economía y Competitividad (TIN2015-65460-C2-2-P). Duration: 01/01/2016 – 31/12/2018.

Abstract:

EMIGO – Efficient Metaheuristics for Graph Optimization

Tue, 01 Dec 2020 00:00:00 +0000

Principal investigator: Abraham Duarte y José Manuel Colmenar Verdugo. Funding entities: Ministerio de Ciencia Innovación y Universidades. (Ref. PGC2018-095322-B-C22). Duration: 01/01/2019 – 31/12/2021.

Abstract:

Information systems are nowadays commonly represented with a graph, which makes them easier to interpret and understand. A graph is an abstract data structure consisting of nodes and edges, where the nodes, sometimes also referred to as vertices, represent entities, and the edges are links that connect the nodes representing relationships among them. From a theoretical perspective a graph is an ordered pair G = (V, E), where V is the set of vertices and E a set of edges, where each edge associates two vertices in V. This type of graphs is usually referred to as undirected and unweighted graphs. Those graphs where edges have orientation (i.e., given to vertices u, v in V there exist an arc between u and v but not the reverse one) are known as directed graphs. Finally, weighted graphs are those where a number (weight) is assigned to each edge. These weights might represent costs, lengths or capacities, depending on the problem at hand. Some authors call such a graph a network.

Graphs are the basic modeling unit in a wide variety of areas within CS and OR, like project management, production scheduling, line balancing, business plans, or software visualization. Graph theory offers a rich source of problems and techniques for optimization, programming and data structure development. The design of good heuristics or approximation algorithms for optimization problems often requires significant specialized knowledge and trial-and-error over a graph. In many real-world applications, it is typically the case that the same optimization problem is solved again and again on a regular basis, maintaining the same problem structure but differing in the data. This provides an opportunity for learning heuristic algorithms that exploit the structure of such recurring problems. We study the reinforcement learning and graph embedding to address this challenge. The learned greedy policy behaves like a meta-algorithm that incrementally constructs a solution, and the action is determined by the output of a graph embedding network capturing the current state of the solution.

The main objective of this project is to solve difficult optimization problems, NP-hard in scientific terms, see Garey and Johnson, (1990), on a graph modeling a real situation. A difficult optimization problem is one for which it is not possible to guarantee that the optimal solution will be found within a “reasonable” computational time. There are many difficult problems in practice which trigger the development of efficient procedures capable of finding “good” solutions even when these solutions could not be proven optimal. These methods, for which solution quality is as important as the computing time needed to find it, are known as heuristics. Nowadays, a family of modern and intelligent heuristics, usually known as metaheuristics (Glover, 1986), are master strategies that guide subordinate algorithms to produce solutions beyond those that are normally obtained in a quest for local optimality. Metaheuristics are among the most prominent and successful techniques to solve a large amount of complex and computationally hard combinatorial and numerical optimization problems. Both research groups have a long experience on the development and application of these methodologies. In this project, we propose the application of the metaheuristic methodologies to solve difficult graph-based problems arising in business and industry.