Article
Engineering, Civil
Yang Liu, Samitha Samaranayake
Summary: This study presents a probabilistic proactive rebalancing method and speed-up techniques to enhance the performance of a state-of-the-art real-time high-capacity fleet management framework. It improves computational efficiency and system performance by implementing search-space pruning and I/O cost reduction methods, resulting in a significant reduction of computation time. The proactive rebalancing approach increases the service rate, reduces waiting time, and decreases total delay by routing idle vehicles to future demands based on probabilistic estimates from historical data.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Enrico Angelelli, Claudia Archetti, Carlo Filippi, Michele Vindigni
Summary: The study focuses on an online version of the orienteering problem, formulating it as a Markov Decision Process and testing various heuristic approaches. Extensive computational tests are conducted to discuss the pros and cons of the different methods.
COMPUTERS & OPERATIONS RESEARCH
(2021)
Article
Physics, Multidisciplinary
Xu Wang, Huijun Sun, Si Zhang, Ying Lv, Tongfei Li
Summary: This paper investigates the impact of bike redistribution on users' demand in an extended bike-sharing rebalancing problem. By optimizing routing plans and target bike numbers at each station, the profit of bike-sharing operators can be maximized. The model is tested using real Beijing Mobike data, revealing the significant influence of rebalancing behavior on users' demand and providing guidance for operators.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Mostafa Hajiaghaei-Keshteli, Golman Rahmanifar, Mostafa Mohammadi, Fatemeh Gholian-Jouybari, Jirf Jaromfr Klemes, Sasan Zahmatkesh, Awais Bokhari, Gaetano Fusco, Chiara Colombaroni
Summary: This paper proposes a mixed integer linear mathematical model to optimize the multi-period production routing problem using electric vehicles. The model considers the cost of utilizing electric vehicles, mileage limitations, and the impact of traffic conditions on energy consumption. It also introduces a simultaneous multi-period dynamic production routing problem using heterogeneous electric vehicles.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Engineering, Civil
Aigerim Bogyrbayeva, Sungwook Jang, Ankit Shah, Young Jae Jang, Changhyun Kwon
Summary: The study presents a reinforcement learning approach for nightly offline rebalancing operations in electric vehicle sharing systems, which trains neural networks to learn routing policies, resulting in a significant reduction in the time needed to rebalance the network.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Mathematical & Computational Biology
Xianlong Ge, Yonghong Liang, Yuanzhi Jin, Chunbing Song
Summary: This paper introduces a Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model, aiming to develop a cost-effective and responsive distribution system. By proactive prediction and order matching strategies, the model can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Pawel Sitek, Jaroslaw Wikarek, Mieczyslaw Jagodzinski
Summary: Unmanned aerial vehicles (UAVs), also known as drones, are increasingly popular due to their low prices and high mobility. This study presents a proactive approach to solve the vehicle routing problems with drones (VRPD) and proposes a formal model and methods for the extended vehicle routing problem with drones (EVRPD). Experimental results show that the proposed dedicated genetic algorithm (DGA) can obtain solutions at least 20 times faster than mathematical programming solvers such as Gurobi or LINGO.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Zhenzhong Wang, Kai Ye, Min Jiang, Junfeng Yao, Neal N. Xiong, Gary G. Yen
Summary: This study proposes a framework to reuse knee points in a new environment to address the Dynamic Vehicle Routing Problem based on Hybrid Charging Strategy. Reusing knee points helps generate a better initial population and brings convenience to decision makers.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Brenner Humberto Ojeda Rios, Eduardo C. Xavier, Flavio K. Miyazawa, Pedro Amorim, Eduardo Curcio, Maria Joao Santos
Summary: Technological advances have led to a significant growth in the number of articles related to dynamic vehicle routing problems (DVRPs) over the past seven years, with 65% focusing on dynamic and stochastic problems and 35% on dynamic and deterministic problems. In terms of applications, 40% of the articles are related to goods transportation, 17.5% to services, 17.5% to transport of people, and 25% to generic applications. Heuristics and metaheuristics are the prominent solution methods, with applications expanding into new concepts of logistical operations.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Transportation Science & Technology
Ruixue Gu, Yang Liu, Mark Poon
Summary: This paper investigates the dynamic truck-drone routing problem for an on-demand logistics system, aiming to maximize the total profits by reassigning vehicles based on customer requests. A heuristic solution approach framework is proposed to solve the problem, and numerical experiments demonstrate its effectiveness and benefits. The model improves total profits by considering on-demand requests and the drone operations increase the acceptance rate of dynamic customer requests.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Review
Green & Sustainable Science & Technology
Reza Moghdani, Khodakaram Salimifard, Emrah Demir, Abdelkader Benyettou
Summary: In recent decades, optimization packages have been increasingly utilized for efficient management of distribution systems, resulting in significant savings in global transportation costs. The emerging research field of green vehicle routing problem (GVRP) draws attention from many researchers. Findings suggest that researches on GVRPs are relatively new and there is room for significant improvements in various areas.
JOURNAL OF CLEANER PRODUCTION
(2021)
Review
Green & Sustainable Science & Technology
Reza Moghdani, Khodakaram Salimifard, Emrah Demir, Abdelkader Benyettou
Summary: In recent decades, the utilization of optimization packages in distribution systems based on Operations Research and Mathematical Programming techniques has increased significantly, leading to substantial savings in global transportation costs. The emerging research field of Green Vehicle Routing Problem (GVRP) attracts many researchers, with plenty of room for improvement in various areas.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Economics
Carlos Brunner, Ricardo Giesen, Mathias A. Klapp, Luz Florez-Calderon
Summary: In logistics operations in cities with significant road grades, we studied a VRP model considering road grade and vehicle load, achieving up to a 12.4% reduction in operating costs. Our routing plans prioritize roads with smaller grades initially and plan higher grades after unloading cargo. We also found that inserting intermediate depot visits and splitting routes into subroutes can be cheaper when traveling over mountainous areas with a lighter vehicle.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Engineering, Multidisciplinary
Do Thi Thanh Tuyen, Nguyen Van Hop
Summary: This article proposes a dynamic game approach to address the vehicle routing problem with random product returns. The proposed method is validated through numerical experiments and sensitivity analyses.
ENGINEERING OPTIMIZATION
(2023)
Article
Engineering, Electrical & Electronic
Chen Gao, Hongliang Guo, Wenda Sheng
Summary: This paper investigates the stochastic on-time arrival problem in Gaussian process regulated environments and proposes the Gaussian process proactive path planner (GP4) to maximize the traveler's on-time arrival probability. By predicting the posterior travel time distribution over the entire transportation network, GP4 allows the ego vehicle to proactively select the next traversal link for maximizing the expected stochastic on-time arrival probability. Various extensions of GP4 to different travel time distribution assumptions are introduced and demonstrated to be efficient and applicable in various transportation networks.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)