نشریه بین المللی مهندسی صنایع و تحقیقات تولید
دانشگاه علم و صنعت ایران
دوشنبه 19 مرداد 1405
[Archive]
جلد 37، شماره 3 - ( 7-1405 )
جلد 37 شماره 3 صفحات 144-134 |
برگشت به فهرست نسخه ها
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
mousavipour S, Farughi H. A Genetic-Firefly Algorithm for a sequence - dependent setup times Job Shop Scheduling Problem, applying learning effects and flexible maintenance activities.. IJIEPR 2026; 37 (3) :134-144
URL: http://ijiepr.iust.ac.ir/article-1-2645-fa.html
URL: http://ijiepr.iust.ac.ir/article-1-2645-fa.html
A Genetic-Firefly Algorithm for a sequence - dependent setup times Job Shop Scheduling Problem, applying learning effects and flexible maintenance activities.. نشریه بین المللی مهندسی صنایع و تحقیقات تولید. 1405; 37 (3) :134-144
چکیده: (174 مشاهده)
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.
نوع مطالعه: پژوهشي |
موضوع مقاله:
تحقیق در عملیات
دریافت: 1404/9/16 | پذیرش: 1405/4/10 | انتشار: 1405/6/17
دریافت: 1404/9/16 | پذیرش: 1405/4/10 | انتشار: 1405/6/17
| بازنشر اطلاعات | |
 | این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است. |
