Machine Learning-Based Minimization of Empty Truck Repositioning

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

This study applies a Deep Reinforcement Learning (DRL) algorithm for a multi-agent system integrated within a Markov decision process framework to tackle the empty repositioning problem of the cargo truck fleet. The goal is to learn a relocation strategy that makes supply-demand aware actions under stochastic customer demands to reduce repositioning costs and the associated CO2 emissions and to provide a decision support tool for short-term operational planning. Further, the performance of the obtained policy is evaluated and compared with a designed rule-based policy with tunable parameters against significant key performance indicators. This is accomplished by implementing a simulated environment utilizing a real Geographic Information System (GIS) map of the UAE to replicate the dynamics of the problem. According to the experimental results, the reinforcement learning policy achieved a 5% reduction in cost and a 9% decrease in the waiting time of orders for a small problem setting compared to the rule-based policy performance. It also improved the customer demand fulfillment rate, demonstrating a potential for applying the developed DRL model to address larger-scale problems.

روابط وملفات

التعريف والنوع

رقم الوثيقة

01eed59f-572c-47c2-b4e7-53b6121ba690

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2282717_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":"Adriana Gabor ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Andreas Henschel","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Andrei Sleptchenko ","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Maythah Alkathiri","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Machine Learning-Based Minimization of Empty Truck Repositioning

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Empty Truck Repositioning Cargo Transportation Deep Reinforcement Learning Multi-Agent Systems

إشراف وإعداد

الإشراف

Adriana Gabor , Andreas Henschel, Andrei Sleptchenko

الإعداد

Maythah Alkathiri

الاقتباسات الببليوغرافية