1938. A Matheuristic Framework for the k-Group p-Dispersion Problem
Invited abstract in session TA-15: Heuristic Search 1, stream Combinatorial Optimization.
Tuesday, 8:30-10:00Room: Esther Simpson 1.08
Authors (first author is the speaker)
| 1. | Anna Martínez-Gavara
|
| Estadística i Investigació Operativa, Universitat de València | |
| 2. | Antonio Rodriguez Uguina
|
| Universitat Politècnica de València | |
| 3. | Manuel Laguna
|
| Leeds School of Business, University of Colorado Boulder |
Abstract
Forming diverse, balanced teams is essential for effective decision-making in fields such as workforce management, consulting, and interdisciplinary research, among others. This talk introduces the k-Group p-dispersion Problem ((k,p)-GDP), a novel extension of the classical p-dispersion problem (p-DP). The (k,p)-GDP seeks to form k teams of p individuals while maximizing intra-team dispersion, ensuring that diversity is achieved and distributed across multiple teams, an aspect often overlooked in traditional models. Given the computational complexity of the problem, we propose an advanced solution methodology that integrates Integer Programming formulations with heuristic techniques. Specifically, we develop a novel matheuristic based on the Biased Greedy Randomized Adaptive Search Procedure (B-GRASP) to efficiently handle large-scale instances. This talk will present key findings from extensive computational experiments, highlighting the effectiveness of our approach in generating high-quality diverse teams. Finally, we will explore open challenges and future research directions in diversity maximization and combinatorial optimization.
Keywords
- Combinatorial Optimization
- Metaheuristics
Status: accepted
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