Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling

Wang, Yu-Cheng; Chen, Toly

doi:10.1016/j.eswa.2023.121369

Cited by 6 publications

(1 citation statement)

References 62 publications

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“…Numerous literature studies have consistently shown that heuristics, in general, are characterized by their relative simplicity and ease of construction. On the other hand, metaheuristics are notably more complex and challenging to design and employ effectively, particularly when it comes to intelligent random search strategies (Holland [34]; Essafi et al [35]; Nguyen et al [36]; Fan et al [37]; Tutumlu and Saraç [38]; Wang and Chen [39]). Among the various metaheuristics, the genetic algorithm (GA) stands out as a widely applied and successful approach for obtaining high-quality approximate solutions to a diverse range of combinatorial problems.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Two-Machine Learning Date Flow-Shop Scheduling Problem with Heuristics and Population-Based GA to Minimize the Makespan

Xu,

Lin,

Chang

et al. 2023

Mathematics

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This paper delves into the scheduling of the two-machine flow-shop problem with step-learning, a scenario in which job processing times decrease if they commence after their learning dates. The objective is to optimize resource allocation and task sequencing to ensure efficient time utilization and timely completion of all jobs, also known as the makespan. The identified problem is established as NP-hard due to its reduction to a single machine for a common learning date. To address this complexity, this paper introduces an initial integer programming model, followed by the development of a branch-and-bound algorithm augmented with two lemmas and a lower bound to attain an exact optimal solution. Additionally, this paper proposes four straightforward heuristics inspired by the Johnson rule, along with their enhanced counterparts. Furthermore, a population-based genetic algorithm is formulated to offer approximate solutions. The performance of all proposed methods is rigorously evaluated through numerical experimental studies.

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