Records with Subject: Planning & Scheduling
Showing records 1 to 25 of 1331. [First] Page: 1 2 3 4 5 Last
Effect of Palmitic Acid on Tertiary Structure of Glycated Human Serum Albumin
Agnieszka Szkudlarek
February 10, 2024 (v1)
Keywords: AGEs, Ellman’s reagent, glucose–fructose syrup, glycation, palmitic acid, spectroscopic analysis, tertiary structure of HSA
Non-enzymatic glycation is a process, which can be best described as a significant posttranslational modification of various proteins. It emerges in hyperglycemic conditions and may have an impact on albumin stability as well as its activity and physical and chemical properties, essentially affecting all its physiological functions. The goal of this research was to answer the following questions: (i) how does the glycation of defatted human serum albumin by glucose−fructose syrup (GFS) alter its tertiary structure; (ii) does palmitic acid (PA), a component of palm oil, affect the in vitro glycation process and cause conformational changes of glycated albumin; and (iii) does PA inhibit the formation of Advanced Glycation End-Products (AGEs)? Therefore, in order to point out differences in the tertiary structure of macromolecules, the absorption and emission of fluorescence spectra and their second derivatives, excitation fluorescence and synchronous spectra, Red-Edge Excitation Shift (R... [more]
A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution
Zhengchao Liu, Liuyang Xu, Chunrong Pan, Xiangdong Gao, Wenqing Xiong, Hongtao Tang, Deming Lei
February 10, 2024 (v1)
Keywords: crane transportation, flexible manufacturing, hybrid differential evolution, low-carbon scheduling, multi-state collaborative configuration
With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution... [more]
A State Transition Diagram and an Artificial Physarum polycephalum Colony Algorithm for the Flexible Job Shop Scheduling Problem with Transportation Constraints
Zhengying Cai, Yihang Feng, Shanshan Yang, Jia Yang
January 12, 2024 (v1)
Keywords: artificial Physarum polycephalum colony, flexible job shop scheduling, state transition diagram, swarm intelligence, transportation scheduling
In many flexible job shop scheduling problems, transportation scheduling problems are involved, increasing the difficulty in problem-solving. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us address this problem. First, the flexible job shop scheduling problem with transportation constraints is modeled as a state transition diagram and a multi-objective function, where there are ten states in total for state transition, and the multi-objective function considers the makespan, average processing waiting time, and average transportation waiting time. Second, a novel artificial Physarum polycephalum colony algorithm is designed herein with two main operations: expansion and contraction. In the expansion operation, each mycelium can cross with any other mycelia and generate more offspring mycelia, of which each includes multiple pieces of parental information, so the population expands to more than twice its original size. In the contraction operation,... [more]
Equipment Disassembly and Maintenance in an Uncertain Environment Based on a Peafowl Optimization Algorithm
Jiang Liu, Changshu Zhan, Zhiyong Liu, Shuangqing Zheng, Haiyang Wang, Zhou Meng, Ruya Xu
January 5, 2024 (v1)
Keywords: disassembly sequence planning, equipment maintenance, peafowl optimization algorithm, uncertain environment
Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls’ courtship behavior, the adaptive behavior of female peafowls in pro... [more]
Research on Optimization Algorithm of AGV Scheduling for Intelligent Manufacturing Company: Taking the Machining Shop as an Example
Chao Wu, Yongmao Xiao, Xiaoyong Zhu
November 30, 2023 (v1)
Keywords: automatic guided vehicle, Genetic Algorithm, intelligent manufacturing shop, machining shop, scheduling optimization algorithm
Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution... [more]
An Improved Discrete Jaya Algorithm for Shortest Path Problems in Transportation-Related Processes
Ren Wang, Mengchu Zhou, Jinglin Wang, Kaizhou Gao
September 21, 2023 (v1)
Keywords: Jaya algorithm, route planning, shortest path problem (SPP)
Shortest path problems are encountered in many engineering applications, e.g., intelligent transportation, robot path planning, and smart logistics. The environmental changes as sensed and transmitted via the Internet of Things make the shortest path change frequently, thus posing ever-increasing difficulty for traditional methods to meet the real-time requirements of many applications. Therefore, developing more efficient solutions has become particularly important. This paper presents an improved discrete Jaya algorithm (IDJaya) to solve the shortest path problem. A local search operation is applied to expand the scope of solution exploration and improve solution quality. The time complexity of IDJaya is analyzed. Experiments are carried out on seven real road networks and dense graphs in transportation-related processes. IDJaya is compared with the Dijkstra and ant colony optimization (ACO) algorithms. The results verify the superiority of the IDJaya over its peers. It can thus be w... [more]
HOMER-Based Multi-Scenario Collaborative Planning for Grid-Connected PV-Storage Microgrids with Electric Vehicles
Yifan Zhang, Shiye Yan, Wenqian Yin, Chao Wu, Jilei Ye, Yuping Wu, Lili Liu
September 21, 2023 (v1)
Keywords: collaborative planning, electric vehicles, grid-connected PV-storage microgrid, HOMER simulation, sensitivity analysis
One of the crucial methods for adapting distributed PV generation is the microgrid. However, solar resources, load characteristics, and the essential microgrid system components are all directly tied to the optimal planning scheme for microgrids. This article conducts a collaborative planning study of grid-connected PV-storage microgrids under electric vehicle integration in various scenarios using HOMER 1.8.9 software. To be more specific, in multiple scenarios, we built capacity optimization models for PV modules, energy storage, and converters in microgrids, with several scenarios each accounting for the cleanliness, economic performance, and overall performance of microgrids. For multiple scenarios, this paper used the net present value cost and levelized cost of electricity as indicators of microgrid economics, and carbon dioxide emissions and the fraction of renewable energy were used as indicators of microgrid cleanliness. The optimal capacity allocation for economy, cleanliness... [more]
Sustainable Supply Chains in Industrial Engineering and Management
Conghu Liu, Nan Wang, Xiaoqian Song, Zhi Liu, Fangfang Wei
September 20, 2023 (v1)
The integration of information technologies with the industry has marked the beginning of the Fourth Industrial Revolution and has promoted the development of industrial engineering [...]
Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach
Zufa Wu, Hongbo Fan, Yimeng Sun, Manyu Peng
August 3, 2023 (v1)
Keywords: deep reinforcement learning, delay time sum, dual layer deep Q-network, dynamic flexible job shop scheduling, global optimum, makespan, multi-objective optimization
Previous research focuses on approaches of deep reinforcement learning (DRL) to optimize diverse types of the single-objective dynamic flexible job shop scheduling problem (DFJSP), e.g., energy consumption, earliness and tardiness penalty and machine utilization rate, which gain many improvements in terms of objective metrics in comparison with metaheuristic algorithms such as GA (genetic algorithm) and dispatching rules such as MRT (most remaining time first). However, single-objective optimization in the job shop floor cannot satisfy the requirements of modern smart manufacturing systems, and the multiple-objective DFJSP has become mainstream and the core of intelligent workshops. A complex production environment in a real-world factory causes scheduling entities to have sophisticated characteristics, e.g., a job’s non-uniform processing time, uncertainty of the operation number and restraint of the due time, avoidance of the single machine’s prolonged slack time as well as overweigh... [more]
Green Supply Chain Circular Economy Evaluation System Based on Industrial Internet of Things and Blockchain Technology under ESG Concept
Cheng Qian, Yuying Gao, Lifeng Chen
August 3, 2023 (v1)
Keywords: economy evaluation, ESG performance, green blockchain, industrial internet of things, Supply Chain
A green supply chain economy considering environmental, social, and governance (ESG) factors improves the chances of functional growth through minimal risk factors. The implication of sophisticated technologies such as the Industrial Internet of Things (IIoT) and the blockchain improves the optimization and evaluation of ESG performance. An IIoT-Blockchain-based Supply Chain Economy Evaluation (IB-SCEE) model is introduced to identify and reduce functional growth risk factors. The proposed model uses green blockchain technology to identify distinct transactions’ economic demands and supply distribution. The flaws and demands in the circular economy process are validated using the IIoT forecast systems relying on ESG convenience. The minimal and maximum risks are identified based on economic and distribution outcomes. The present investigation highlights the significance of ongoing ESG-conceptualized research into blockchain-based supply chain economics. Companies who recognize the bloc... [more]
Development of an MCTS Model for Hydrogen Production Optimisation
Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis, Kaspars Ozols, Arturs Nikulins, Kaspars Sudars
August 3, 2023 (v1)
Keywords: control modes, cost optimisation, Hydrogen, Monte Carlo tree search, operation scheduling
Hydrogen has the potential to revolutionize the energy industry due to its clean-burning and versatile properties. It is the most abundant element in the universe and can be produced through a variety of methods, including electrolysis. The widespread adoption of hydrogen faces various challenges, including the high cost of production; thus, it is important to optimise the production processes. This research focuses on development of models for hydrogen production optimisation based on various external factors and parameters. Models based on electricity prices are developed and compared between different market situations. To run hydrogen production more effectively, it is required to use renewable energy sources for the production process. Adding the solar power component to the economic evaluation model outcome is more positive. The Monte Carlo tree search (MCTS) algorithm is adapted to effectively control the electrolysis process. MCTS schedule optimization was performed for a 24 h... [more]
A Cost/Benefit and Flexibility Evaluation Framework for Additive Technologies in Strategic Factory Planning
Angela Luft, Sebastian Bremen, Nils Luft
August 3, 2023 (v1)
Keywords: additive manufacturing, factory planning, manufacturing flexibility, mix flexibility, production planning, volume flexibility
There is a growing demand for more flexibility in manufacturing to counter the volatility and unpredictability of the markets and provide more individualization for customers. However, the design and implementation of flexibility within manufacturing systems are costly and only economically viable if applicable to actual demand fluctuations. To this end, companies are considering additive manufacturing (AM) to make production more flexible. This paper develops a conceptual model for the impact quantification of AM on volume and mix flexibility within production systems in the early stages of the factory-planning process. Together with the model, an application guideline is presented to help planners with the flexibility quantification and the factory design process. Following the development of the model and guideline, a case study is presented to indicate the potential impact additive technologies can have on manufacturing flexibility Within the case study, various scenarios with diff... [more]
Low-Carbon and Energy-Saving Path Optimization Scheduling of Material Distribution in Machining Shop Based on Business Compass Model
Yongmao Xiao, Hao Zhang, Ruping Wang
August 3, 2023 (v1)
Keywords: business compass, dung beetle optimizer, low carbon, material distribution, path planning
In order to reduce carbon emission and energy consumption in the process of raw material distribution, the workshop material distribution management model was established based on the business compass model; it can help guide enterprises to manage workshop production. Based on the raw material distribution equipment, a path calculation model considering the carbon emission and energy consumption in the process of raw material distribution was established. The dung beetle optimizer was selected for the optimization calculation. The dung beetle optimizer has the characteristics of fast convergence and high solution accuracy. The material distribution of an engine assembly workshop was taken as an example; the results showed that the optimized scheduling model could effectively optimize the material distribution route and reduce energy consumption and carbon emission in the distribution process on the basis of meeting the distribution demand.
Food Production Scheduling: A Thorough Comparative Study between Optimization and Rule-Based Approaches
Maria E. Samouilidou, Georgios P. Georgiadis, Michael C. Georgiadis
August 3, 2023 (v1)
Keywords: food process industry, heuristics, MILP, Optimization, production scheduling
This work addresses the lot-sizing and production scheduling problem of multi-stage multi-product food industrial facilities. More specifically, the production scheduling problem of the semi-continuous yogurt production process, for two large-scale Greek dairy industries, is considered. Production scheduling decisions are made using two approaches: (i) an optimization approach and (ii) a rule-based approach, which are followed by a comparative study. An MILP model is applied for the optimization of short-term production scheduling of the two industries. Then, the same problems are solved using the commercial scheduling tool ScheduleProTM, which derives scheduling decisions using simulation-based techniques and empirical rules. It is concluded that both methods, despite having their advantages and disadvantages, are suitable for addressing complex food industrial scheduling problems. The optimization-based approach leads to better results in terms of operating cost reduction. On the oth... [more]
Scheduling Jobs with a Limited Waiting Time Constraint on a Hybrid Flowshop
Sang-Oh Shim, BongJoo Jeong, June-Yong Bang, JeongMin Park
July 13, 2023 (v1)
Keywords: diffusion workstation, hybrid flowshop, limited waiting time, Scheduling, semiconductor fabrication
In this paper, we address a two-stage hybrid flowshop scheduling problem with identical parallel machines in each stage. The problem assumes that the queue (Q)-time for each job, which represents the waiting time to be processed in the current stage, must be limited to a predetermined threshold due to quality concerns for the final product. This problem is motivated by one that occurs in the real field, especially in the diffusion workstation of a semiconductor fabrication. Our objective is to minimize the makespan of the jobs while considering product quality. To achieve this goal, we formulated mathematical programming, developed two dominance properties for this problem, and proposed three heuristics with the suggested dominance properties to solve the considered problem. We conducted simulation experiments to evaluate the performance of the proposed approaches using randomly generated problem instances that are created to closely resemble real production scenarios, and the results... [more]
Research on the Access Planning of SOP and ESS in Distribution Network Based on SOCP-SSGA
Yuxin Jia, Qiong Li, Xu Liao, Linjun Liu, Jian Wu
July 13, 2023 (v1)
Keywords: economic costs, Energy Storage, second-order cone planning, soft open point, steady-state genetic algorithm
This paper proposes a two-stage planning model for soft open point (SOP) and energy storage system (ESS) that considers the cost of faults in response to the current issue of SOP and ESS systems not considering the impact of SOP access on load transfer in the event of a fault in the distribution network. Firstly, considering the uncertainty of “PV-load”, typical scenarios of PV and load are constructed based on the clustering algorithm. Secondly, aiming at the economic performance of the distribution network and the capacity of PV access, a two-stage optimization model is established for the joint integration of SOP and ESS into the distribution network (normal and fault operation) under typical scenarios. The model is solved by using the second-order cone programming algorithm and steady-state genetic algorithm (SOCP-SSGA). Stage one involves planning for the integration capacity and location of SOP and ESS into the distribution network under each scenario within a period based on SOC... [more]
Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR
Yong Zhang, Feng Gao, Fengkui Zhao
July 13, 2023 (v1)
Keywords: autonomous vehicle, linear quadratic regulator, Particle Swarm Optimization, path planning, RRT*, tracking control
Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of itera... [more]
An Efficient and Improved Coronavirus Herd Immunity Algorithm Using Knowledge-Driven Variable Neighborhood Search for Flexible Job-Shop Scheduling Problems
Xunde Ma, Li Bi, Xiaogang Jiao, Junjie Wang
July 13, 2023 (v1)
Keywords: adaptive mutation, coronavirus herd immunity algorithm, flexible job-shop scheduling, multi-population, variable neighborhood search
By addressing the flexible job shop scheduling problem (FJSP), this paper proposes a new type of algorithm for the FJSP. We named it the hybrid coronavirus population immunity optimization algorithm. Based on the characteristics of the problem, firstly, this paper redefined the discretized two-stage individual encoding and decoding scheme. Secondly, in order to realize the multi-scale search of the solution space, a multi-population update mechanism is designed, and a collaborative learning method is proposed to ensure the diversity of the population. Then, an adaptive mutation operation is introduced to enrich the diversity of the population, relying on the adaptive adjustment of the mutation operator to balance global search and local search capabilities. In order to realize a directional and efficient neighborhood search, this algorithm proposed a knowledge-driven variable neighborhood search strategy. Finally, the algorithm’s performance comparison experiment is carried out. The mi... [more]
Experimental Study on the Working Efficiency and Exergy Efficiency of the Vehicle-Mounted Thermoelectric Generator for Cold Chain Logistics Transportation Vehicle
Yunchi Fu, Yanzhe Li
July 7, 2023 (v1)
Keywords: cold chain logistics transport vehicle, copper foam, logistics and supply chain, thermoelectric generator, working efficiency and exergy efficiency
This paper investigates a vehicle-mounted thermoelectric generator system working efficiency and exergy efficiency in a cold chain logistics transport vehicle (CLVTEG). The study examines the impact of factors such as load resistance, temperature difference, and copper foam on the performance of CLVTEG. Results demonstrate that adding copper foam significantly improves the output power of CLVTEG, with 40 PPI copper foam showing a 1.8 times increase compared to no copper foam. Additionally, copper foam enhances working and exergy efficiency, with 10 PPI copper foam achieving the best overall efficiency. The study also explores the effect of temperature difference on CLVTEGs efficiency, observing an initial increase followed by a decrease. Overall, this research underscores the importance of considering work and exergy efficiency when evaluating thermoelectric generators. Adding copper foam in the CLVTEG central area enhances heat transfer, resulting in improved efficiency. These finding... [more]
Optimal Scheduling for Hybrid Battery Swapping System of Electric Vehicles
Ziqi Wang, Sizu Hou
July 4, 2023 (v1)
Keywords: electric vehicle, mobile battery swapping, range anxiety, Sigmoid function
Range anxiety seriously restricts the development of electric vehicles (EVs). To address the above issue, a hybrid battery swapping system (HBSS) is developed in this paper. In the system, EVs can swap their battery at battery swapping stations or by the roadside via battery swapping vans. The proposed scheduling strategy aims to achieve the best service quality for the HBSS by controlling the mobile swapping service fee. In the model, the uncertainty of EV selection is managed by leveraging the Sigmoid function. Based on proving the uniqueness of the solution, the particle swarm optimization algorithm is used to solve the problem. Simulations validate the effectiveness of the proposed strategy in alleviating range anxiety. Moreover, the impacts of maximum service capacity and the operating rule have been analyzed.
Low-Carbon Optimal Scheduling Model for Peak Shaving Resources in Multi-Energy Power Systems Considering Large-Scale Access for Electric Vehicles
Kang Dai, Kun Zhang, Jicheng Li, Liang Liu, Zhe Chen, Peng Sun
June 9, 2023 (v1)
Keywords: carbon reduction, electric vehicle, multi-energy storage, peak regulating resources
Aiming at the synergy between a system’s carbon emission reduction demand and the economy of peak shaving operation in the process of optimizing the flexible resource peaking unit portfolio of a multi-energy power system containing large-scale electric vehicles, this paper proposes a low-carbon optimal scheduling model for peak shaving resources in multi-energy power systems considering large-scale access for electric vehicles. Firstly, the charging and discharging characteristics of electric vehicles were studied, and a comprehensive cost model for electric vehicles, heat storage, and hydrogen storage was established. At the same time, the carbon emission characteristics of multi-energy power systems and their emission cost models under specific carbon trading mechanisms were established. Secondly, the change characteristics of the system’s carbon emissions were studied, and a carbon emission cost model of multi-energy power was established considering the carbon emission reduction de... [more]
Research on Low-Carbon Strategies of Supply Chains, Considering Livestreaming Marketing Modes and Power Structures
Yonghua Gong, Guangqiang He
June 7, 2023 (v1)
Keywords: consumer low-carbon preference, level of low-carbon promotion effort, livestreaming marketing mode, low-carbon strategy, power structures
A livestreaming supply chain composed of a single manufacturer and a single streamer in the low-carbon market is examined. Motivated by the actual production and operation, both the manufacturer and the streamer have a chance to dominate the supply chain. Low-carbon strategies and livestreaming marketing modes of the supply chain are studied. The impacts of the consumer’s price sensitivity coefficient, low-carbon preference, and streamer’s promotion sensitivity coefficient on the equilibrium results are further studied. The results show that: the streamer achieves the optimal level of promotion effort in the resale mode under both power structures. The manufacturer achieves the optimal low-carbon level in the commission mode when the promotion sensitivity coefficient is smaller under both of two power structures. The streamer’s profit is optimal in the resale mode, while the manufacturer’s profit is optimal in the commission mode when under the streamer-led structure. Two parties’ prof... [more]
Research on a Three-Dimensional Fuzzy Active Disturbance Rejection Controller for the Mechanical Arm of an Iron Roughneck
Kaige Zhang, Yanjun Liu, Hua Jia, Feng Yan, Gang Xue
June 7, 2023 (v1)
Keywords: mechanical arm of an iron roughneck (MAIR), position control, proportional-valve-controlled single-extension (PVCSE) hydraulic cylinder, three-dimensional fuzzy active disturbance rejection controller (TF-ADRC), trajectory planning
In the position control of the mechanical arm of an iron roughneck (MAIR), a controller with high responsiveness, high accuracy, and high anti-interference capability is necessary. An MAIR consists of two proportional-valve-controlled single-extension (PVCSE) hydraulic cylinders, and a traditional proportional−integral−derivative (PID) controller cannot easily achieve the accuracy and robustness requirements of the hydraulic cylinders. In this paper, a three-dimensional fuzzy active disturbance rejection controller (TF-ADRC) is proposed for an MAIR, which adds a three-dimensional fuzzy module to a classical active disturbance rejection controller (ADRC) to adjust the controller output according to the tracking of differential deviation, deviation change rate, and deviation change acceleration rate. Firstly, the trajectory planning of the MAIR was carried out using the quintic polynomial interpolation method to improve the smoothness of the target trajectory. Then, the reliability of th... [more]
Applications of Terrestrial Laser Scanner in Detecting Pavement Surface Defects
Abdelhalim Azam, Abdulaziz H. Alshehri, Mohammad Alharthai, Mona M. El-Banna, Ahmed M. Yosri, Ashraf A. A. Beshr
June 7, 2023 (v1)
Keywords: flexible pavement, laser scanner, pavement distress, PCI, plane equation
An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of paveme... [more]
Emergency Material Scheduling Optimization Method Using Multi-Disaster Point Distribution Approach
Mengying Chang, Huizhi Xu, Dongsheng Hao, Jinhuan Zhou, Chen Liu, Chujie Zhong
June 7, 2023 (v1)
Keywords: emergency dispatch, epidemic prevention and control, logistics engineering, multi-distribution center problem, TOPSIS decision
The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decrease... [more]
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