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Processes
Special Issues
Modeling, Operation and Planning in Engineering System Problems
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Modeling, Operation and Planning in Engineering System Problems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 6252

Special Issue Editors

Prof. Dr. Oveis Abedinia

E-Mail Website
Guest Editor
School of Engineering, Nazarbayev University, Nursultan, Kazakhstan
Interests: load forecast; power system planning; peak-load; system losses
Special Issues, Collections and Topics in MDPI journals
Dr. Saeid Gholami Farkoush

E-Mail Website
Guest Editor
Drexel University Philadelphia, Philadelphia, PA, USA
Interests: power system protection; smart grid; power system; FACT devices; electric vehicle; grounding grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our great pleasure to introduce a Special Issue entitled " Modeling, operation and planning in engineering system problems " in Processes. We are currently inviting interested authors to submit original contributions on topics relevant to this Issue.

Engineering problems present different challenges for researchers. This Special Issue considers the application of the internet of things (IoT) in the modeling, operations and planning processes in engineering to solve electrical, mechanical, computer and industrial problems.  We welcome papers detailing IoT applications for smart grids (SMs), microgrids (MGs), energy issues and buildings that deal with different industrial engineering problems. Accordingly, revolutionary approaches to engineering problems, such as Internet of Things, can be considered through artificial intelligent methods. Additionally, IoT devices can be easily embedded in building service systems; these IoT-enabled building management systems (BMSs) closely monitor inside and outside conditions, occupant presence and comfort, equipment operation and energy use in real time. This Special Issue aims to provide a platform through which professionals may discuss major challenges and new possibilities in the adoption of IoT in modeling, operation and planning processes for engineering problems. Original research articles as well as critical reviews are welcome.

Prof. Dr. Oveis Abedinia
Dr. Saeid Gholami Farkoush
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • peak-load forecasting and planning
  • artificial neural networks
  • optimization and evolutionary algorithms
  • operation and control of multi energy systems
  • smart buildings and smart homes
  • internet of things
  • IoT-enabled building management systems
  • machine learning and smart buildings
  • comfort and productivity in smart buildings
  • emerging technologies in energy and power systems
  • developments in internet of things technology in engineering systems
  • application of big data in energy and engineering systems
  • data management and grid analytics in electrical systems

Published Papers (4 papers)

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Research

14 pages, 3717 KiB  
Article
Application of Improved Artificial Immune System Algorithm Based on Applied Mathematics for Optimization of Manpower Allocation in Construction Engineering
by Qingbo Huang and Yong Bai
Processes 2023, 11(7), 1870; https://doi.org/10.3390/pr11071870 - 21 Jun 2023
Viewed by 874
Abstract
The outbreak of the COVID-19 pandemic has led construction companies to prioritize the intelligent and optimal scheduling of human resources in construction projects to reduce costs. This study addresses the problem of heterogeneity in human resource scheduling in construction projects, presents a mathematical [...] Read more.
The outbreak of the COVID-19 pandemic has led construction companies to prioritize the intelligent and optimal scheduling of human resources in construction projects to reduce costs. This study addresses the problem of heterogeneity in human resource scheduling in construction projects, presents a mathematical model with generic human resources as an example, proposes an improved artificial immune system (NAIS) algorithm to solve the problem, and verifies its effectiveness. Experimental results show that the NAIS algorithm achieves the optimal duration of 9 days in just 2 s using the Matrix Laboratory (MATLAB), which is significantly faster than mathematical optimization technique software (CPLEX), thus confirming the feasibility of the NAIS algorithm. Additionally, the average PD values for the NAIS algorithm, calculated for different worker counts, skills, and the number of tasks, were lower compared to the comparison algorithm. Overall, the NAIS algorithm effectively addresses the heterogeneous problem of human resource scheduling in construction projects with multiple modes, thereby optimizing construction engineering labor allocation. Full article
(This article belongs to the Special Issue Modeling, Operation and Planning in Engineering System Problems)
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18 pages, 1343 KiB  
Article
An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization
by Qing Liu, Houman Kosarirad, Sajad Meisami, Khalid A. Alnowibet and Azadeh Noori Hoshyar
Processes 2023, 11(4), 1162; https://doi.org/10.3390/pr11041162 - 10 Apr 2023
Cited by 16 | Viewed by 2138
Abstract
Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud [...] Read more.
Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks. Full article
(This article belongs to the Special Issue Modeling, Operation and Planning in Engineering System Problems)
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14 pages, 1888 KiB  
Article
Application of Multidimensional Structural Equations in the Emergency Management of Coal Mine Accidents
by Tianyue Zhang, Jiayu Liu and Liang Hong
Processes 2023, 11(3), 882; https://doi.org/10.3390/pr11030882 - 15 Mar 2023
Viewed by 1049
Abstract
The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new [...] Read more.
The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new industrialization and sustainable development. Therefore, it is crucial to predict a safety accident in the coal mine in advance. In order to facilitate the early warning of coal mine safety accidents, this study seeks to present a prediction model based on emergency management of safety accidents, which is a fusion model of principal component analysis (PCA) and long short-term memory neural network. According to the results, the correlation coefficients of risk identification and monitoring (a11), safety inspection and warning (a12), emergency planning and training (a13), material and technical support (a15), and macroenvironmental management (a21) were 0.718, 0.653, 0.628, 0.444, and 0.553, respectively, after the PCA dimensionality reduction process, demonstrating that the previous principal component analysis had a better effect. The absolute relative errors of each evaluation index of safety accident emergency management did not exceed the limit of 5%, including a15 and a21, whose values were 4.5% and −3.8%, while the relative errors of the remaining indicators were kept at a relatively low level. In conclusion, it is clear that the algorithm model suggested in this research improved the warning capabilities of safety accident emergency risk. Full article
(This article belongs to the Special Issue Modeling, Operation and Planning in Engineering System Problems)
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14 pages, 2825 KiB  
Article
Regional Geological Disasters Emergency Management System Monitored by Big Data Platform
by Xiaoping Qian
Processes 2022, 10(12), 2741; https://doi.org/10.3390/pr10122741 - 19 Dec 2022
Cited by 1 | Viewed by 1338
Abstract
In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based [...] Read more.
In order to deal with the hazards caused by geological disasters in time, an emergency management system is proposed based on association rule data mining. With the support of a big data platform, a regional geological disaster emergency management system is built based on monitoring data. In the result analysis, the association rule algorithm demonstrates high computing power in the test, which can filter the data with strong association rules. In addition, the big data platform can allow data visualization, which has good data storage capacity and disaster early warning capacity. In the simulation test of the emergency management system, it was found that the system is feasible in theory. When it is applied to the actual disaster emergency management, it wasfound that, in the face of geological disasters, the processing speed of relevant departments increased by 59.4%, and the allocation of personnel and materials wasmore reasonable. The above results show that the big data platform monitoring data can improve the regional geological disasters emergency management capacity and ensure the safety of people’s lives and property. Full article
(This article belongs to the Special Issue Modeling, Operation and Planning in Engineering System Problems)
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