International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Sun, Jul 5, 2026
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1- Assistant professor yasouj university, Iran , h.mousavipour@yu.ac.ir
2- Ph.D university of Kurdistan, Iran
2- Ph.D university of Kurdistan, Iran
Abstract: (61 Views)
Humans are inherently learning beings. Due to learning effects, when an operation is performed repeatedly, workers gain experience, leading to a reduction in job processing times. In contemporary scheduling literature, both learning effects and restricted access to machines caused by maintenance activities are recognized as critical factors that have attracted extensive research from multiple perspectives. Despite this, relatively few studies have systematically examined the learning phenomenon within the context of the Job Shop Scheduling Problem (JSSP). Most prior research assumes that machines are continuously available, that set-up times are incorporated into processing times, and that transportation times are negligible.
This study introduces a novel JSSP framework incorporating Sequence-Dependent Set-up Times (SDSTs), classical position-based learning effects, flexible maintenance schedules, and transportation times. A corresponding mathematical model is formulated, and numerical instances of three different scales are generated. The model is solved exactly for small-sized instances using CPLEX within GAMS. To address medium- and large-sized instances, metaheuristic algorithms including Ant Colony Optimization for continuous domains (ACOR), Invasive Weed Optimization (IWO), and a hybrid Genetic–Firefly algorithm (GA-FA) are employed.
This study introduces a novel JSSP framework incorporating Sequence-Dependent Set-up Times (SDSTs), classical position-based learning effects, flexible maintenance schedules, and transportation times. A corresponding mathematical model is formulated, and numerical instances of three different scales are generated. The model is solved exactly for small-sized instances using CPLEX within GAMS. To address medium- and large-sized instances, metaheuristic algorithms including Ant Colony Optimization for continuous domains (ACOR), Invasive Weed Optimization (IWO), and a hybrid Genetic–Firefly algorithm (GA-FA) are employed.
Keywords: Job Shop Scheduling Problem, Sequence-Dependent Set-up times, learning effects, flexible maintenance, transportation times, Genetic – Firefly algorithm
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