کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382507 660765 2014 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems
ترجمه فارسی عنوان
یک مدل چند منظوره چند حالته جدید برای حل زمان پیشگیرانه هزینه های برنامه ریزی پروژه
کلمات کلیدی
مشکل برنامه ریزی پروژه مشکل زمانبندی گسسته گسسته، الگوریتم تکاملی چند هدفه، پیش شرط فعالیت، روابط مقدماتی مقدماتی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Considering time–cost–quality trade-offs in project scheduling is an important problem.
• A customized, dynamic and self-adaptive NSGA-II is proposed to solve these problems.
• We consider preemption and generalized precedence relations.
• The proposed NSGA-II is compared with an efficient ε-constraint method.
• The results show the relative dominance of our NSGA-II over the ε-constraint method.

Considering the trade-offs between conflicting objectives in project scheduling problems (PSPs) is a difficult task. We propose a new multi-objective multi-mode model for solving discrete time–cost–quality trade-off problems (DTCQTPs) with preemption and generalized precedence relations. The proposed model has three unique features: (1) preemption of activities (with some restrictions as a minimum time before the first interruption, a maximum number of interruptions for each activity, and a maximum time between interruption and restarting); (2) simultaneous optimization of conflicting objectives (i.e., time, cost, and quality); and (3) generalized precedence relations between activities. These assumptions are often consistent with real-life projects. A customized, dynamic, and self-adaptive version of a multi-objective evolutionary algorithm is proposed to solve the scheduling problem. The proposed multi-objective evolutionary algorithm is compared with an efficient multi-objective mathematical programming technique known as the efficient ε-constraint method. The comparison is based on a number of performance metrics commonly used in multi-objective optimization. The results show the relative dominance of the proposed multi-objective evolutionary algorithm over the ε-constraint method.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 41, Issue 4, Part 2, March 2014, Pages 1830–1846
نویسندگان
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