A Heuristic Guided Genetic Algorithm Applied to Dual Resource Job Shop Scheduling

Ricardo Magalhães (), João M. C. Sousa and Susana M. Vieira
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Ricardo Magalhães: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
João M. C. Sousa: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Susana M. Vieira: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal

Mathematics, 2025, vol. 13, issue 19, 1-13

Abstract: This paper introduces the Knowledge-Guided Genetic Algorithm (KGGA), a hybrid metaheuristic that reimagines crossover as a form of genetic engineering rather than random recombination. By embedding knowledge-guided exploitation principles directly into the crossover operator, KGGA selectively amplifies high-quality genetic material, intensifying the search around promising regions of the solution space. Experimental results on a large scale DRC-FJSSP benchmark show that KGGA outperforms state-of-the-art alternatives—including the Classic Genetic Algorithm (GA), Knowledge-Guided Fruit Fly Optimization Algorithm (KGFOA), and Hybrid Artificial Bee Colony Algorithm (HABCA)—consistently achieving superior solution quality.

Keywords: scheduling; job shop; dual resource; genetic algorithm; genetic engineering; knowledge-guided crossover (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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