مقاله انگلیسی رایگان در مورد استراتژی کنترل افقی پس رونده فازی – IEEE 2019
مشخصات مقاله | |
ترجمه عنوان مقاله | یک استراتژی کنترل افقی پس رونده فازی برای مشکل مسیریابی پویا وسایل نقلیه |
عنوان انگلیسی مقاله | A Fuzzy Receding Horizon Control Strategy for Dynamic Vehicle Routing Problem |
انتشار | مقاله سال ۲۰۱۹ |
تعداد صفحات مقاله انگلیسی | ۱۳ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس میباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
۴٫۶۴۱ در سال ۲۰۱۸ |
شاخص H_index | ۵۶ در سال ۲۰۱۹ |
شاخص SJR | ۰٫۶۰۹ در سال ۲۰۱۸ |
شناسه ISSN | ۲۱۶۹-۳۵۳۶ |
شاخص Quartile (چارک) | Q2 در سال ۲۰۱۸ |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر، مهندسی فناوری ازلاعات |
گرایش های مرتبط | مهندسی الگوریتم و محاسبات، سامانه های شبکه ای |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | School of Computer and Information, Anqing Normal University, Anqing 246133, China |
کلمات کلیدی | مشکل مسیریابی پویا وسایل نقلیه، کنترل فازی، عملکرد عضویت، کنترل افقی پس رونده |
کلمات کلیدی انگلیسی | Dynamic vehicle routing problem, fuzzy control, membership function, receding horizon control |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2948154 |
کد محصول | E13880 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Probkem Description and Mathematical Model III. Methodology IV. Experimental Studies V. Conclusion Authors Figures References |
بخشی از متن مقاله: |
Abstract
The receding horizon control (RHC) combining with the various intelligent algorithms is a common method for the dynamic vehicle routing problem (DVRP). However, the traditional RHC only considers the objects within each time window while making route plan, and can’t make adjustment according to the situations of the objects near the window. In order to deal with this problem, a fuzzy receding horizon control strategy (FRHC) is proposed. By combining the RHC and the membership function theory, the relationship between objects and time window is redefined. And the travel routes are planned by the genetic algorithm (GA) for each fuzzy time window. Finally, ten instances are selected from the DVRP standard test library to verify the proposed strategy. The experimental results show that when comparing with the RHC strategy, the FRHC can reduce the distance, the waiting time of all customers and the number of waiting customers dramatically. The FRHC combines with the GA (FRHC-GA) method is also reasonable and effective. Introduction The Vehicle Routing Problem (VRP) is a classical NP-hard problem in the field of operations research, is always a hot topic [1]–[۶]. It arms to design an optimal route for a number of vehicles in serving a set of customers. The vehicles serve each customer in an orderly manner to get the plan with the shortest distance or the shortest waiting time under some constraints. The VRP is mainly divided into two categories according to its characteristics: the Static VRP (SVRP) and the Dynamic VRP (DVRP). The main feature of the SVRP is that all the information of the environment such as the customer demands and travel costs is known and unchanged. However, this assumption is rarely true in real life, where the environment is often changing over time, e.g. a new customer request arrives while the vehicles are on their routes. In such a dynamic environment, the theories and the solution methods of the SVRP are no longer applicable. The DVRP is first proposed by Psaraftis [7], [8]. The main difference between the DVRP and the SVRP is that the information of customers (e.g. demand, address, service time, etc.) may change with time. To solve DVRP, many scholars have proposed various optimization algorithms [9]–[۲۵]. These approaches can be roughly divided into three categories. (1) The original travel route is generated at the beginning of the system. The system will modify the original travel route when the dynamic information generates [10]. |