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Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

IEEE ACCESS 2023年 11卷 83069-83080页

作者： Zhang, Juanmei Wang, Guoyong Sheng, Qian Jia, Xinyu Xie, Ping Luoyang Inst Sci & Technol Luoyang 471023 Peoples R China Googol Technol Co Ltd Shenzhen 471023 Peoples R China Henan Univ Sci & Technol Luoyang 471023 Peoples R China

This paper considers a multi-depot heterogeneous vehicle routing problem (MDHVRP) with time windows, which is very crucial for hazardous materials transportation. For this reason, we formalize this problem as a multi-objective MDHVRP optimization model, where the actual load dependent risk of hazardous materials transportation is considered. To solve the optimization problem, we propose a hybrid multi-objective evolutionary algorithm (HMOEA) and a two-stage algorithm (TSA). In addition, we verify the performance of the proposed algorithms by experiments on the modified Solomon's VRPTW examples. In the experiment, it can be seen from the distribution of Pareto solution sets and the convergence distribution of IGD values that HMOEA is significantly superior to the other three algorithms in searching for Pareto solutions, as well as in the convergence and diversity of the algorithm. HMOEA and TSA were compared, and the minimum cost obtained by TSA was 13.38% lower than HMOEA, while the minimum risk was 81.69% higher than HMOEA. The advantages of each algorithm in finding solutions in reality were analyzed. A comparison was made between multi-depots heterogeneous VRP and multi-depots homogeneous VRP in the C101 instance, and the results showed that scheduling heterogeneous vehicles would reduce risk and cost.

关键词： Hazardous materials transportation actual load robust multi-depot heterogeneous vehicle routing problem hybrid multi-objective evolutionary optimization algorithm two-stage algorithm

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Improving the robustness of motion vector temporal descriptor

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IET IMAGE PROCESSING 2018年第1期12卷 98-104页

作者： Rahmani, Farzaneh Zargari, Farzad Ghanbari, Mohammad Iran Telecomm Res Ctr IT Fac Res Inst ICT Tehran Iran Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England Univ Tehran Sch Elect & Comp Engn Tehran Iran

Motion vectors (MVs) are the most common temporal descriptors in video analysis, indexing and retrieval applications. However, video indexing and analysis based on MVs do not perform well for videos at different dimension ratios (DRs) or even various resolutions. As a result, video indexing and analysis which are based on identifying similar video face with many difficulties at different DRs or resolutions by MVs. In this study, a two-stage algorithm is introduced to make MV descriptors robust against variations first in DR and then at resolution. In the experiments performed on motion vector histograms, the proposed method improves the performance on identifying similar videos at various spatial specifications by up to 73%. Moreover, in the video retrieval experiments, the proposed modified MV outperforms original MV feature vector. This is an indication of improvement in differentiation of similar and dissimilar videos by the proposed temporal feature vector.

关键词： video retrieval image motion analysis motion vector temporal descriptor video analysis video indexing dimension ratios MV descriptors motion vector histograms video retrieval two-stage algorithm

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Modeling and Solution of Joint Storage Space Allocation and Handling Operation for Outbound Containers in Rail-Water Intermodal Container Terminals

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IEEE ACCESS 2019年 7卷 55142-55158页

作者： Chang, Yimei Zhu, Xiaoning Haghani, Ali Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

Storage space allocation and handling operation problems are two main crucial problems in container terminals. Early research efforts, however, are seldom devoted to studying them together. Therefore, this paper considers these two problems simultaneously for outbound containers in rail-water intermodal container terminals (RWICTs), where rail-mounted gantry cranes, inner trucks, and quay cranes are involved. A two-stage problem is proposed: stage 1 is to determine locations of the containers and reduce the overlapping amount, considering container weight, departure time, destination ports, and containers left from earlier planning periods in railway container yards, according to the locations of containers from stage 1;and stage 2 aims at obtaining optimal job sequences of different types of equipment and minimizing makespans of handling operations, considering some operational constraints, particularly rehandling time and inner truck congestion. To solve the problem, a two-stage heuristic algorithm is proposed, where the rolling planning horizon and a new update strategy are introduced. A heuristic algorithm is introduced in stage 1 and a novel two-layer genetic algorithm is proposed in stage 2, which introduces proximity principles and the reselection operation. Afterward, the results from stage 2 are used to resolve the first stage problem, while the problem in stage 2 is also resolved using the new results from stage 1. This iterative process continues until there are no more improvements in stage 1. Finally, the results of the computational experiments indicate that the proposed model and solution approaches are effective and efficient in solving the two-stage problem for outbound containers in RWICTs.

关键词： Rail-water intermodal transportation storage space allocation handling operation two-stage algorithm container terminal

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Eco-friendly lane reservation-based autonomous truck transportation network design

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第23期62卷 8239-8259页

作者： Xu, Ling Wu, Peng Chu, Chengbin D'Ariano, Andrea Fuzhou Univ Sch Econ & Management Fuzhou Peoples R China Univ Gustave Eiffel ESIEE Paris COSYS GRETTIA Marne La Vallee France Roma Tre Univ Dept Engn Rome Italy Fuzhou Univ Sch Econ & Management Fuzhou 350108 Peoples R China Univ Gustave Eiffel SIEE Paris COSYS GRETTIA F-77454 Marne La Vallee France

As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an epsilon-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the epsilon-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis.

关键词： Autonomous truck lane reservation carbon emissions multi-objective optimisation two-stage algorithm

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Accelerated Identification algorithms for Exponential Nonlinear Models: two-stage Method and Particle Swarm Optimization Method

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第5期41卷 2636-2652页

作者： Pu, Yan Rong, Yingjiao Chen, Jing Mao, Yawen Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214028 Jiangsu Peoples R China

The traditional least squares (LS) and gradient descent (GD) algorithms can estimate the parameters of the regression models. They can be inefficient when the models have complex structures: (1) the unknown parameters in the information vector make the algorithm be impossible to update the parameters;(2) the zigzagging nature of the gradient descent algorithm and the complex structures lead to slow convergence rates;and (3) the step-size and derivative function calculations may be unsolvable for complex nonlinear models. This paper proposes two kinds of algorithms for exponential nonlinear models. The first is the two-stage algorithm, which decomposes the complex model into a linear part and a nonlinear part, where the linear part is estimated using the LS algorithm and the nonlinear part is identified based on the GD algorithm. The second is the particle swarm optimization algorithm which can simultaneously obtain all the parameters. To increase the convergence rates, the Aitken method is also introduced. The simulation results demonstrate the effectiveness of the proposed algorithms.

关键词： Parameter estimation Exponential nonlinear model two-stage algorithm Particle swarm optimization Aitken method

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DiffRank: Enhancing efficiency in discontinuous frame rate analysis for urban surveillance systems

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ALEXANDRIA ENGINEERING JOURNAL 2024年 103卷 148-157页

作者： Cheng, Ziying Li, Zhe Zhang, Tianfan Zhao, Xiaochao Jing, Xiao Hubei Engn Univ Sch Comp & Informat Sci Xiaogan 432100 Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan 432100 Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian 710000 Peoples R China

Urban public safety management relies heavily on video surveillance systems, which provide crucial visual data for resolving a wide range of incidents and controlling unlawful activities. Traditional methods for target detection predominantly employ a two-stage approach, focusing on precision in identifying objects such as pedestrians and vehicles. These objects, typically sparse in large-scale, lower-quality surveillance footage, induce considerable redundant computation during the initial processing stage. This redundancy constrains real-time detection capabilities and escalates processing costs. Furthermore, transmitting raw images and videos laden with superfluous information to centralized back-end systems significantly burdens network communications and fails to capitalize on the computational resources available at diverse surveillance nodes. This study introduces DiffRank, a novel preprocessing method for fixed-angle video imagery in urban surveillance. The method strategically generates candidate regions during preprocessing, thereby reducing redundant object detection and improving the efficiency of the detection algorithm. Drawing upon change detection principles, a background feature learning approach utilizing shallow features has been developed. This approach prioritizes learning the characteristics of fixed-area backgrounds over direct background identification. As a result, alterations in ROI are efficiently discerned using computationally efficient shallow features, markedly accelerating the generation of proposed Regions of Interest (ROIs) and diminishing the computational demands for subsequent object detection and classification. Comparative analysis on various public and private datasets illustrates that DiffRank, while maintaining high accuracy, substantially outperforms existing baselines in terms of speed, particularly with larger image sizes (e.g., an improvement exceeding 300 % at 1920 x1080 resolution). Moreover, the method demonstrates en

关键词： Video image processing Portrait detection Public security management two-stage algorithm Change detection

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Variable neighborhood search for consistent vehicle routing problem

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 113卷 66-76页

作者： Xu, Zefeng Cai, Yanguang Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China

This article presents a variable neighborhood search (VNS) algorithm for the consistent vehicle routing problem (ConVRP). ConVRP is a variant of the vehicle routing problem (VRP). In ConVRP, vehicle routes must be designed for multiple days, and each customer must be visited by the same driver at approximately an identical time on each day. VNS is an efficient algorithmic framework and is widely used. The proposed algorithm consists of two stages. In the first stage, VNS is applied to obtain approximately optimized solutions. The solutions obtained might be infeasible. If a solution is of acceptable quality, the second stage is applied to make it feasible and optimize it further. Several techniques are employed to reduce computation time of the local search stage. A special shaking method is introduced and proofed to be more effective than ordinary methods by experiments. A new method for computing time difference excess is proposed to solve the problem that change of time difference excess caused by operations on an individual day is not obvious. The proposed algorithm is tested on the benchmark ConVRP data set and compared with extant ConVRP approaches from the literature. The results demonstrate that VNS outperforms all the extant ConVRP approaches in terms of quality of solutions obtained. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： two-stage algorithm Template Diverge Local search

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Optimal decision-making of post-disaster emergency material scheduling based on helicopter-truck-drone collaboration

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2024年 127卷

作者： Shi, Yong Yang, Junhao Han, Qian Song, Hao Guo, Haixiang China Univ Geosci Sch Econ & Management Lumo Rd Wuhan 430074 Peoples R China China Univ Geosci Lab Nat Disaster Risk Prevent & Emergency Manageme Lumo Rd Wuhan 430074 Peoples R China China Univ Geosci Lumo Rd 388 Wuhan Peoples R China

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people's lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the twostage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.

关键词： Emergency materials dispatching Tabu search Vehicle routing problem with drones two-stage algorithm

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Main-Lobe Beamwidth Constrained Target Localization Using Bistatic Range Measurements

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 289-304页

作者： Mao, Zhi Su, Hongtao Shen, Lu Jing, Xinchen Jia, Congyue Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

This paper addresses the localization of a target in 3D scenarios using bistatic range measurements with a single passive receiver and multiple transmitters. The main-lobe beamwidth of the receiving antenna contains valuable a-priori information about the target position but is commonly overlooked. This study introduces an angular constraint to incorporate the beamwidth information into the elliptic localization, resulting in a Quadratically Constrained Quadratic Programming (QCQP) problem. A novel two-stage Main-lobe beamwidth Constrained Estimation (TSMCE) algorithm is proposed to solve this challenging localization problem. Theoretical analysis demonstrates that the algorithm can converge to the candidate solutions that satisfy the Karush-Kuhn-Tucker (KKT) optimality conditions. The corresponding covariance can achieve the Cramer-Rao Lower Bound (CRLB) under a small noise assumption. Numerical simulations confirm that the proposed algorithm achieves superior localization accuracy compared to other methods, particularly in the low Signal to Noise Ratio (SNR) regime. Additionally, the algorithm demonstrates fast convergence and robustness to variations in localization geometry.

关键词： Location awareness Signal processing algorithms Antenna measurements Receiving antennas Mathematical models Three-dimensional displays Signal to noise ratio Passive localization bistatic range main-lobe beamwidth angular constraint two-stage algorithm

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two incentive policies for green shore power system considering multiple objectives

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Zhong, Ziyi Jin, Huan Sun, Yuyao Zhou, Yanjie Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315100 Peoples R China Univ Nottingham Ningbo China Business Sch China Ningbo 315100 Peoples R China Zhengzhou Univ Sch Management Zhengzhou 450001 Peoples R China

Shipping significantly contributes to air pollution and greenhouse gas emissions, accounting for more than 80% of global trade. Consequently, governments have been focused on air pollution reduction for many years. The adoption of shore power systems has increased, recognized for their effectiveness in reducing emissions. This study introduces multi-objective mixed-integer programming models to investigate the impacts of governmentsubsidy-based and berthing-priority-based incentive policies on various types of expense benefits from the perspectives of different stakeholders. This paper aims to optimize shore power equipment deployment, berth allocation, and ship scheduling while balancing environmental benefits, costs associated with shore power, and operational efficiency. We design an improved NSGA-II with a two-stage solution algorithm to solve the studied problem, which is more suitable for large-scale case calculations. Following the case study, we offer insights for stakeholders to navigate the complexities of sustainable port development and promote the adoption of shore power systems.

关键词： Shore power equipment Berth allocation Multi-objective model two-stage algorithm

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