یک روش تلفیقی جدید جهت تخصیص افزونگی در سیستم‌های تولیدی با استفاده از NSGA-II و MOPSO اصلاح شده

نویسنده

استادیار دانشگاه آزاد اسلامی، واحد قزوین، گروه مدیریت صنعتی، قزوین، ایران

چکیده

این مقاله به معرفی یک مدل ریاضی چند‌هدفه جهت تخصیص افزونگی در سیستم‌های تولیدی می پردازد. در بسیاری از خطوط تولید و مونتاژ در صنعت، توابع توزیع ورود قطعات، مدت زمان‌های پردازش، مدت زمان تابازمانی ماشین‌ها و مدت زمانهای تعمیر از توابع توزیع عمومی تبعیت میکنند. روش پیشنهادی این مقاله با استفاده از رویکرد تلفیقی شبیهسازی کامپیوتری و متدولوژی سطح پاسخ، قابلیت درنظرگیری پارامترهای زمانی مبتنی بر توابع توزیع عمومی در خطوط تولید را داراست. در مدل ریاضی این مقاله سه هدف حداکثرکردن نرخ تولید، حداقل‌کردن هزینه‌ها و حداکثر‌کردن کیفیت محصولات در‌نظر‌گرفته شده‌است. جهت حل مدل ریاضی چند‌هدفه پیشنهادی، از دو الگوریتم فراابتکاری تکاملی الگوریتم ژنتیک با مرتب‌سازی نامغلوب و بهینه‌سازی ازدحام ذرات چند‌هدفه استفاده شده‌است. نتابج محاسباتی مؤثر‌بودن دو الگوریتم فوق در تولید جواب‌های نامغلوب برای مسئله تخصیص افزونگی در سیستم‌های تولیدی نامطمئن را نشان می‌دهد. به‌علاوه، نتایج حاصل از مقایسه این دو الگوریتم نشان‌دهنده کیفیت بالاتر جواب‌های الگوریتم ژنتیک با مرتب‌سازی نامغلوب در این مسئله است. 

کلیدواژه‌ها


عنوان مقاله [English]

A New Hybrid Method for Redundancy Allocation in Production Systems using Modified NSGA-II and MOPSO Algorithm

نویسنده [English]

  • Ali Mohtashami*
Assistant Professor, Department of industrial management, Faculty of management, Qazvin branch, Islamic Azad University, Qazvin, Iran
چکیده [English]

This paper presents a multi-objective mathematical model for redundancy allocation in production systems. In many of the production and assembly lines, process times, time between failures and repaired times are generally distributed. The proposed method of this paper is able to consider time dependent parameters as general distribution functions by using the hybrid approach of simulation and response surface methodology. The objectives of the mathematical model are maximizing production rate, minimizing total cost and maximizing quality. In order to solve the proposed mathematical model, non-dominated sorting genetic algorithm and multiple objective particle swarm optimization are used. Numerical results indicate the effectiveness of both algorithms for generating non-dominated solutions. Moreover, comparative results indicate the superiority of the Non-dominated sorting genetic algorithm.

کلیدواژه‌ها [English]

  • Production line
  • Response Surface Methodology
  • Simulation
  • Non-dominated sorting genetic algorithm
  • Multiple objective particle swarm optimization

 

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