پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

بهینه سازی چندهدفه پلاتونینگ کامیونها با در نظر گرفتن پنجره زمانی و سرعتهای مختلف

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری، دانشکده مدیریت، دانشگاه آزاد اسلامی، واحد فیروزکوه، ایران
2 دانشیار، گروه مهندسی صنایع، دانشکده مدیریت و مهندسی صنایع، دانشگاه علوم و فنون مازندران، بابل، ایران
3 دانشیار، گروه مهندسی صنایع، دانشگاه آزاد اسلامی، واحد فیروزکوه، ایران
4 دانشیار، گروه مدیریت، دانشگاه آزاد اسلامی، واحد فیروزکوه، ایران
چکیده
پلاتونینگ به معنای حرکت کامیونها در یک دنباله در جاده است. در این روش یک ردیف گروهی از وسایل نقلیه با خیال راحت و با سرعت زیاد می‌توانند با هم بصورت یک ریسمان حرکت کنند که می تواند باعث صرفه جویی در مصرف سوخت، کاهش انتشار گازهای گلخانه ای و استفاده کارآمدتر از ظرفیت جاده شود. این به این دلیل است که کامیونی که جلوتر از کامیون(های) دیگر حرکت می‌کند، باعث کاهش نیروی کشش هوا می شود و نیروی مقاومتی کمتری به وسایل نقلیه عقب وارد می شود. از آنجایی که در یک پلاتون، فاصله بین خودروها کوتاه است و وسایل نقلیه با سرعت بالا حرکت می کنند، برای جلوگیری از برخورد و اطمینان از ایمنی، یک سیستم کروز کنترل تطبیقی و برنامه‌ریزی مناسب مورد نیاز است. استقرار و توسعه پلاتونینگ در سیستم های حمل و نقل با وسایل نقلیه سنگین سودآور و مفید است زیرا هزینه سوخت و همچنین میزان انتشار کربن توسط این وسایل نقلیه را کاهش می‌دهد. در این تحقیق به دنبال ارائه یک مدل بهینه‌سازی چند هدفه برای مسئله پلاتونینگ کامیون با در نظر گرفتن چندین هدف هستیم. همچنین در نظر داریم که از الگوریتمهای فراابتکاری مبتنی بر آرشیو پارتو برای حل مدل استفاده نمائیم. در مقاله حاضر، ابتدا یک مدل ریاضی دو هدفه برای مسئله مورد مطالعه ارائه شده و سپس از الگوریتمهای انبوه ذرات چندهدفه، NSGA-II و رویکرد محدودیت اپسیلون برای حل مدل استفاده شد. مدل برای مسائل نمونه مختلف حل گردید و نتایج نشان داد، الگوریتم انبوه ذرات پیشنهادی در همه موارد توانایی بالاتری برای تولید جوابهای باکیفیت‌تر و پراکندگی بالاتر نسبت به الگوریتم NSGA-II دارد. همچنین، الگوریتم NSGA-II جوابهایی با یکنواختی بالاتری را در زمان کمتری نسبت به الگوریتم PSO تولید می‌کند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Multi-Objective Optimization of Truck Platooning Considering Time Window and Different Speeds

نویسندگان English

Hosein Firouzi 1
Javad Rezaeian 2
Alireza Rashidi Komijan 3
Mohammad Meddi Movahedi 4
1 Ph.D., Candidate, Faculty of Management, Islamic Azad University, Firouzkouh Branch, Iran.
2 Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, University of Science and Technology of Mazandaran, Babol, Iran.
3 Associate Professor, Department of Industrial Engineering, Islamic Azad University, Firouzkouh Branch, Iran.
4 Associate Professor, Department of Management, Islamic Azad University, Firouzkouh Branch, Iran.
چکیده English

Platooning means moving trucks in a sequence on the road. In this method, a group of vehicles can safely and at high speed move together as a string, which can save fuel consumption, reduce greenhouse gas emissions, and use road capacity more efficiently. This is because a truck that is ahead of the other truck(s) has less air drag and less drag on the vehicles behind. Since in a platoon, the distance between vehicles is short and the vehicles move at high speed, an adaptive cruise control system and proper planning are needed to avoid collisions and ensure safety. The establishment and development of platooning in transportation systems with heavy vehicles is profitable and useful because it reduces the cost of fuel as well as the amount of carbon emissions by these vehicles. In this research, we seek to present a multi-objective optimization model for the truck platooning problem by considering several objectives. We also intend to use meta-heuristic algorithms based on Pareto archive to solve the model. In this article, first, a two-objective mathematical model is presented for the studied problem, and then multi-objective particle swarm algorithms, NSGA-II, and the epsilon constraint approach were used to solve the model. The model was solved for different sample problems and the results showed that the proposed particle mass algorithm has a higher ability to produce higher quality solutions and higher dispersion than the NSGA-II algorithm in all cases. Also, the NSGA-II algorithm produces solutions with higher uniformity in less time than the PSO algorithm.

کلیدواژه‌ها English

Rest of Drivers
Multi-Objective Optimization
Platooning of Trucks
Fuel Reduction
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