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A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： Optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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Optimal logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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Computers, Materials & Continua 2022年第12期73卷 5269-5282页

作者： Basim Aljabhan Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Ports and Maritime Transportation Department Faculty of Maritime StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia S 7Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia

Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective exploitation.The traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical models.The recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)models.In this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)model.The presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the environment.To attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic flow.In order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively.

关键词： Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis logistics

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Harvesting Technologies and Costs for Switchgrass Production

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Advances in Bioscience and Biotechnology 2024年第2期15卷 112-128页

作者： Hana Toth Jude Liu Department of Agricultural and Biological Engineering The Pennsylvania State University University Park USA

As today’s society searches for renewable energy sources that could be an alternative to fossil fuels, biomass and biofuels provide a promising solution. Switchgrass is one of feedstocks that can be utilized as a renewable energy source. When farming, one of the most important considerations is efficiency. This consists of several factors, including time, fuel use, economic and power efficiencies of equipment. Inefficient field operations could increase harvesting costs and in turn could cause hesitation when a farmer decides to participate in biomass production. This literature review will cover the main elements of biomass and biomass handling relating to determining harvesting efficiency and biomass quality for switchgrass round bales. Specifically, the following sections include past research activities relating to biomass harvesting, biomass bale quality during outdoor storage, logistics models, and data collection methods during biomass harvesting. The objective of this review is to examine status and needs for switchgrass round bale harvesting operations and the expenses that come with it.

关键词： Biomass Baling Energy Crop logistics

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Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

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Intelligent Automation & Soft Computing 2024年第1期39卷 15-31页

作者： Nguyen Vu Anh Duy Nguyen Le Thai Nguyen Huu Tho Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City700000Vietnam

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail stations.In particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of cranes.The rail crane scheduling problems in this study are based on the movement of containers.We consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is developed.The traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling solutions.Numerical examples are conducted to evaluate the applicability of the proposed algorithms.Verification of the proposed parallel SA is done by comparing it to existing previous works.Results of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

关键词： Multi-crane scheduling logistics containers makespan rail stations

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Underground logistics systems:Development overview and new prospects in China

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Frontiers of Engineering Management 2023年第2期10卷 354-359页

作者： Wanjie HU Jianjun DONG Rui REN Zhilong CHEN Faculty of Architecture Civil and Transportation EngineeringBeijing University of TechnologyBeijing 100124China School of Science Nanjing University of Science and TechnologyNanjing 210094China College of Defense Engineering Army Engineering University of PLANanjing 210007China

1 Introduction Underground logistics systems(ULSs)are a set of self-contained,multimodal,and intelligent physical distribution concepts that enable the automated movement of goods via tunnels and underground pipelines installed within and between cities(Visser,2018).ULSs are also recognized as the fifth type of logistics and generic supply system after seaways,airlines,roads,and railways(Qian and Guo,2007).

关键词： underground enable logistics

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Efficient logistics enabled by smart solutions in tunneling

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Underground Space 2017年第4期2卷 227-233页

作者： Zakaria Dakhli Zoubeir Lafhaj Ecole Centrale de Lille Civil EngineeringCite ScientifiqueCS 2004859651 LilleFrance

While logistics comprises an important part of tunneling costs,it is generally not considered a lever of performance but rather a con-straint to a project’s progress.This study presents some insights on how smart technology can impact the tunneling industry.The impact is even greater because of the complexity of the tunneling supply chain,and smart technology could help support this process.Finally,we discuss how the nature of the tunneling industry invites stakeholders to develop a common understanding of the project prior to con-struction to successfully deploy smart technology during the use or maintenance phase.

关键词： Smart technology logistics Underground space Supply chain Construction Lean construction

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CREATING BALANCED AND CONNECTED CLUSTERS TO IMPROVE SERVICE DELIVERY ROUTES IN logistics PLANNING

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Journal of Systems Science and Systems Engineering 2010年第4期19卷 453-480页

作者： Buyang CAO Fred GLOVER Esri Inc. 380 New York Street CA 92373 USA OptTek Systems Inc. 2241 17th Street Boulder CO 80302 USA

A challenging problem in real world logistics applications consists in planning service territories for customer deliveries, in contexts where customers must be clustered into groups that satisfy various conditions such as balance and connectivity. In this paper we propose new algorithms for producing such clusters based upon special procedures for exploiting Thiessen polygons. Our methods are able to handle multiple criteria for balancing the clusters, such as the number of customers in each cluster, the service revenue in each cluster, or the delivery/pickup quantity in each cluster. Computational results demonstrate the efficacy of our new procedures, which are able to assist users to plan service personal service territories and vehicle routes more efficiently.

关键词： Clustering K-means logistics routing Thiessen polygon

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OPTIMAL logistics FOR MULTIPLE JEEPS

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Acta Mathematica Scientia 2010年第5期30卷 1429-1439页

作者：陈文磊丁义明范文涛 Economics and Management School Wuhan University Wuhan Institute of Physics and Mathematics The Chinese Academy of Sciences

We consider variations of the classical jeep problems： the optimal logistics for a caravan of jeeps which travel together in the desert. The main purpose is to arrange the travels for the one-way trip and the round trip of a caravan of jeeps so that the chief jeep visits the farthest destination. Based on the dynamic program principle, the maximum distances for the caravan when only part of the jeeps should return and when all drivers should return are obtained. Some related results such as the efficiency of the abandoned jeeps, and the advantages of more jeeps in the caravan are also presented.

关键词： jeep problem logistics dynamic program principle

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Research Needs and Applications of Machine Learning｡ｪPredicting logistics Stress by Machine Learning

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计算机科学与技术汇刊（中英文版） 2022年第1期9卷 35-42页

作者： Bin Yan School of Control and Computer Engineering North China Electric Power University102206China

Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that they can make decisions similar to those made by humans when faced with problems.With the development of various industries,the amount of data has increased and the efficiency of data processing and analysis has become more demanding,a series of machine learning algorithms have emerged.Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems.Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements.This paper presents the interim state of a dynamic disruption management software solution for logistics,using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters,to prevent physical and mental stress on workers in the logistics industry,to maintain their health,to make them more optimistic and better able to adapt to their work,and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.

关键词： Machine Learning Pressure logistics Rest Regulation Sensor Technology Keywords:Machine Learning Pressure logistics Rest Regulation Sensor Technology Machine Learning Pressure logistics Rest Regulation Sensor Technology

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The logistics of Production and Supply of Ag Pellets for Industrial Applications in Canada

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Journal of Sustainable Bioenergy Systems 2023年第1期13卷 40-55页

作者： Shahabaddine Sokhansanj Mahmood Ebadian Hamid Rezaei Fahimeh Yazdanpanah University of British Columbia Vancouver Canada University of Saskatchewan Saskatoon Canada Wood Pellet Association of Canada Vancouver Canada

In this work we analyze the supply of biomass from field to an in-land or port destination. The biomass is pelletized to increase its bulk density to extend its storage period and for ease of its transport. The pellet may be used for conversion to chemicals and animal bedding or for straight combustion. We analyzed supply chain in Saskatchewan where there are plenty of crop residues but widely dispersed and harvest seasons are short. We envisioned that the farmer collects bales from field and transports the bales to farmstead during the harvest season. The bales are then processed into pellets using small scale pellet equipment. A custom operator with expertise in pelletization may engage in handling and densifying the biomass. The business case for the mobile mill will be similar to the well established custom grain and forage harvesting operations. The pellets are stored in hopper bottom grain bins at the farmstead. From this point, the handling of pellets would be similar to the handling and marketing of grain. The farmer trucks a specified volume of pellets from farmstead to the nearest elevator where the pellets are transferred to larger bins or silos. Pellets are extracted from silos and loaded onto the rail cars. The Canadian freight rail companies (mainly CN) currently transport over 3 million dry tonne (dt) of wood pellets in rail cars. The pellets are hauled to marine ports on the West Coast or East Coast for export. The cost of delivering ag pellets to biorefinery or to the shipping port is $86.09/dt. This cost does not include the equivalent value of removing biomass from the farm (e.g. fertilizer replacement) and return on investment. The GHG emissions to produce and transport ag pellets add up to 185.9 kg of CO2 per dt of biomass. The cost of producing pellets without drying feedstock is $35.05/dt and the corresponding GHG for palletization amounts $146.30/dt.

关键词： Canada Pellets Ag Pellets Supply Chain logistics Cost GHG Emissions Infrastructure

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