Random-key metaheuristics for the block relocation problem

• Grant number: 25/11059-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2025
• End date: July 31, 2026
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Mateus Pereira Martin
• Grantee: Guilherme Reis Nahass
• Host Institution: Centro de Ciências em Gestão e Tecnologia (CCGT). Universidade Federal de São Carlos (UFSCAR). Campus de Sorocaba. Sorocaba , SP, Brazil
• Associated research grant: 22/05803-3 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM

Abstract

This project proposes the development of random-key-based metaheuristics to solve the restricted version of the unloading block relocation problem (UBRP), a relevant challenge in industrial environments such as steel plant storage yards and in logistics settings like port terminals. The UBRP is a combinatorial optimization problem that consists of determining the sequence of movements for stacked blocks in order to minimize the number of relocations required for their retrieval, while respecting operational constraints such as stack height limits and the prescribed order of block dispatch. To address this problem, the Random-Key Optimizer (RKO), proposed in Chaves et al. (2024), will be employed. RKO is a flexible framework that enables the implementation of various metaheuristics, both population-based (e.g., genetic algorithms) and single-solution-based (e.g., simulated annealing), by encoding solutions as vectors of random keys and using problem-specific decoders. The proposed approaches will be validated through computational experiments using benchmark instances from the literature and, whenever possible, real-world data from industry partners. The expected outcomes include advancing the state of the art in logistics operations optimization, promoting efficiency gains and cost reduction.