دانلود مقاله ISI انگلیسی شماره 46151
ترجمه فارسی عنوان مقاله

یک الگوریتم کلونی مورچه (ACA) برای حل مدل یکپارچه جدید برنامه ریزی تولید کارگاهی و مسیریابی بدن تزاحم AGV ها

عنوان انگلیسی
An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
46151 2015 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Computers & Industrial Engineering, Volume 86, August 2015, Pages 2–13

ترجمه کلمات کلیدی
برنامه ریزی تولید کارگاهی - مسیریابی بدن تزاحم - الگوریتم کلونی مورچه - خودرو با هدایت خودکار - تجزیه و تحلیل اقتصادی
کلمات کلیدی انگلیسی
Job shop scheduling; Conflict-free routing; Ant Colony Algorithm; Automated guided vehicle; Economic analysis
پیش نمایش مقاله
پیش نمایش مقاله  یک الگوریتم کلونی مورچه (ACA) برای حل مدل یکپارچه جدید برنامه ریزی تولید کارگاهی و مسیریابی بدن تزاحم AGV ها

چکیده انگلیسی

This paper concerns with the Job Shop Scheduling Problem (JSSP) considering the transportation times of the jobs from one machine to another. The goal of a basic JSSP is to determine starting and ending times for each job in which the objective function can be optimized. In here, several Automated Guided Vehicles (AGVs) have been employed to transfer the jobs between machines and warehouse located at the production environment. Unlike the advantages of implemented automatic transportation system, if they are not controlled along the routes, it is possible that the production system encounters breakdown. Therefore, the Conflict-Free Routing Problem (CFRP) for AGVs is considered as well as the basic JSSP. Hence, we proposed a mathematical model which is composed of JSSP and CFRP, simultaneously and since the problem under study is NP-hard, a two stage Ant Colony Algorithm (ACA) is also proposed. The objective function is to minimize the total completion time (make-span). Eventually, in order to show the model and algorithm’s efficiency, the computational results of 13 test problems and sensitivity analysis are exhibited. The obtained results show that ACA is an efficient meta-heuristic for this problem, especially for the large-sized problems. In addition, the optimal number of both AGVs and rail-ways in the production environment is determined by economic analysis.