کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10281758 501792 2014 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Disassembly sequence planning using a Simplified Teaching-Learning-Based Optimization algorithm
ترجمه فارسی عنوان
الگوریتم بهینه سازی آموزش مبتنی بر یادگیری ساده شده را با استفاده از روش ساده تر به کار برده است
کلمات کلیدی
جداسازی، برنامه ریزی توالی انحلال، متا اورویری، آموزش بهینه سازی مبتنی بر یادگیری، آموزش ساده شده بهینه سازی یادگیری،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the Teaching-Learning-Based Optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified Teaching-Learning-Based Optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching-learning-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a Teaching Phase Operator (TPO) and a Learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Advanced Engineering Informatics - Volume 28, Issue 4, October 2014, Pages 518-527
نویسندگان
, , , ,