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 133-121 |
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Ben REBAH O, Elloumi A, Mellouli R. A Mixed-Integer Programming and Genetic Algorithm Approach for a Two-Stage Hybrid Flow Shop with Shared Machines in Cardboard Cutting Operations. IJIEPR 2026; 37 (3) :121-133
URL: http://ijiepr.iust.ac.ir/article-1-2601-fa.html
URL: http://ijiepr.iust.ac.ir/article-1-2601-fa.html
A Mixed-Integer Programming and Genetic Algorithm Approach for a Two-Stage Hybrid Flow Shop with Shared Machines in Cardboard Cutting Operations. نشریه بین المللی مهندسی صنایع و تحقیقات تولید. 1405; 37 (3) :121-133
چکیده: (191 مشاهده)
This paper addresses pattern scheduling within cutting stock problems, treating it as a specialized instance of a two-stage hybrid flow shop (HFS) problem with unrelated parallel machines at each stage. Originating from a real industrial case in the corrugated cardboard-cutting industry (UNIPACK), the problem incorporates machine eligibility restrictions, shared bi-functional machines across stages, and a novel positional constraint that prevents job splitting on shared machines. To address these complexities, we propose a mixed-integer programming (MIP) model that minimizes production costs comprising weighted job flow-time costs and makespan-related labor costs. We complement this with a genetic algorithm (GA) metaheuristic for larger instances. The MIP achieves optimality for instances with up to 20 jobs; for instances with 22–32 jobs, it returns the best feasible solution found within a 225-second time limit. For all instances where an MIP reference exists (up to 32 jobs), the GA solutions deviate from the MIP reference by an average of 5.2%. For larger instances (up to 200 jobs), the GA produces near-optimal solutions in under 120 seconds, demonstrating strong scalability. Computational experiments on 16 benchmark instances confirm the effectiveness of both approaches and highlight their complementary strengths.
نوع مطالعه: پژوهشي |
موضوع مقاله:
Optimization Techniques
دریافت: 1404/8/8 | پذیرش: 1405/4/10 | انتشار: 1405/6/17
دریافت: 1404/8/8 | پذیرش: 1405/4/10 | انتشار: 1405/6/17
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