مدل‌سازی چندهدفه مسیریابی سبز با استفاده از الگوریتم ترکیبی یادگیری ماشین حداکثری و برنامه‌ریزی ژنتیک

نوع مقاله : پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه صنعتی امیرکبیر (پلی‌تکنیک)، تهران، ایران

2 کارشناسی ارشد، دانشکده فنی و مهندسی، گروه مهندسی صنایع، دانشگاه آزاد واحد علوم تحقیقات، تهران، ایران

3 دانشیار پژوهشکده فناوری اطلاعات، گروه پژوهشی مدیریت فناوری اطلاعات، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران

4 استادیار، دانشکده فنی و مهندسی، گروه مهندسی صنایع، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران

5 کارشناسی مهندسی صنایع، دانشگاه هنر و معماری پارس، تهران، ایران

چکیده

حمل‌و‌نقل بخش قابل‌توجهی از تولید ناخالص ملی و مصرف محصولات نفتی هر کشوری را به خود اختصاص می‌دهد. در کشور ما با توجه به تحریم‌های سال‌های اخیر و عدم توسعه‌ی سامانه‌های حمل‌و‌نقل ریلی، هوایی و دریایی، استفاده از حمل‌و‌نقل جاده‌ای بیشتر شده است. حمل‌و‌نقل جاده‌ای بیشترین نقش را در تولید گازهای گلخانه‌ای نظیر کربن‌دی‌اکسید دارد. بااین‌حال حمل‌و‌نقل یکی از عناصر اصلی لجستیک بوده و مسئله‌ی مسیریابی وسایل نقلیه با در‌نظر گرفتن آلودگی از جمله مهم‌ترین مسائل در این حوزه است. بنابراین در این مقاله با درنظر گرفتن عواملی چون بار وسیله نقلیه، سرعت وسیله نقلیه، پارامترهای آلایندگی وسیله نقلیه نظیر ضریب بهره‌وری سوخت و موتور، شیب مسیر، تراکم رفت‌وآمد، سرعت و جهت باد، دمای هوا و جنس آسفالت به بهینه‌سازی هزینه‌های ناشی از مصرف سوخت و دستمزد راننده پرداخته شده است. همچنین با درنظر گرفتن تقاضا به صورت احتمالی و سامانه توزیع با جمع‌آوری و تحویل کالا، یک مدل ریاضی احتمالی عددصحیح آمیخته خطی به منظور کمینه‌سازی مجموع هزینه‌های ذکر شده ارائه گردیده است. استفاده از این مدل موجب تخمین دقیق‌تر هزینه‌های سامانه شده و منجر به تحلیل و برنامه‌ریزی بهتر برای سازمان‌ها می‌شود. باتوجه به اینکه مسئله‌ی مطرح شده از نوع مسائل با درجه سختی بالا می‌باشد، مسئله در ابعاد بزرگ با ترکیب دو الگوریتم فراابتکاری یادگیری ماشین حداکثری و برنامه‌ریزی ژنتیک حل شده است. با توجه به نتایج حاصل شده از محاسبات، الگوریتم ترکیبی توسعه یافته قابلیت تخمین جواب با دقت مناسبی را دارد و از سرعت عمل بالایی نسبت به الگوریتم‌های مشابه برخوردار است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Multi-Objective Modeling of Green Vehicle Routing Problem Using a Hybrid Extreme Learning Machine (ELM) and Genetic Programming (GP)

نویسندگان [English]

  • Mohammad Mehdi Ershadi 1
  • Mahsa Momeni Sharifabad 2
  • Mohammad Javad Ershadi 3
  • Amir Azizi 4
  • Samaneh Behzadipour 5
1 Faculty of Industrial and Systems Engineering, Amirkabir University of Technology (Polytechnic)
2 Faculty of Industrial Engineering, Islamic Azad University Science and Research Branch
3 Iranian Research Institute for Information Science & Technology (IRANDOC)
4 Islamic Azad University Science and Research Branch
5 Faculty of Industrial Engineering, Pars University
چکیده [English]

Transportation plays a significant role in the gross domestic product and oil consumption of every nation. In our country, a combination of recent sanctions and underdeveloped rail, air, and sea transportation systems has led to an increased reliance on road transport. Unfortunately, road transport contributes significantly to the emission of greenhouse gases, particularly carbon dioxide. Nevertheless, transportation is a vital aspect of logistics, and addressing pollution in vehicle routing stands as a paramount concern within this realm.This paper introduces a model aimed at optimizing fuel consumption costs, considering various factors such as vehicle load, speed, pollution, as well as parameters like fuel and engine efficiency, incline, traffic density, wind speed and direction, air temperature, asphalt quality, and driver remuneration. Additionally, this mathematical linear mixed-integer model incorporates probabilistic demand and a distribution system involving both delivery and pickup processes, all geared towards cost minimization.By employing this model, organizations can achieve more precise cost estimates, enhanced analysis, and improved planning. Given the NP-hard nature of the problem, its resolution involves the amalgamation of two meta-heuristic algorithms: Extreme Learning Machine (ELM) and Genetic Programming (GP). Experimental results indicate that the developed hybrid algorithm offers highly accurate estimations in a remarkably short time span when compared with similar algorithms.

کلیدواژه‌ها [English]

  • Vehicle Routing Problem
  • Multi-Objective Model
  • Delivery and Pickup
  • Probabilistic Demand
  • Extreme Learning Machine
  • Genetic Programming

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