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Optimization Algorithms Applied to Sustainable Production Processes
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Optimization Algorithms Applied to Sustainable Production Processes

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 57441

Special Issue Editors

Prof. Dr. Chia-Nan Wang

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Interests: operations management; decision-making; management of technology; electronic commerce; systematic innovation
Special Issues, Collections and Topics in MDPI journals
Dr. Nguyen Van Thanh

E-Mail
Guest Editor
Department of Logistics and Supply Chain Management, Faculty of Commerce, Van Lang University, Ho Chi Minh City 72320, Vietnam
Interests: optimization algorithm; MCDM model; operation research; sustainable energy; energy and sustainable development; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The theme of ICLIE 2022 is: “Operational Excellence and Sustainability in a Post-Pandemic World”. As the world moves toward the end of the pandemic, supply chain managers must reconsider the structure of their supply chains. As such, operational excellence and sustainability strategies in a post-pandemic world, where both internal and external norms have become obsolete, must be reviewed and adapted to ensure the survivability of businesses.

The International Conference on Logistics and Industrial Engineering (ICLIE) 2022 (https://iclie-vlu.com/) will be held at Van Lang University, Ho Chi Minh City, Vietnam, 27–28 August 2022.

The ICLIE 2022 event aims to collect high-quality research studies from leading researchers, educators, software developers, and practitioners to discuss current issues, and share ideas and experiences in the latest development in the field of technology and innovation, technology management, industrial system engineering, logistics and supply chain management, and management sciences.

This Special Issue is being published in cooperation with ICLIE 2022. The authors of outstanding papers related to industrial engineering/supply chain management are invited to submit their manuscript to this Special Issue for publication.

This Special Issue on “Optimization Algorithms Applied to Sustainable Production Processes” aims to collect high-quality research studies addressing this problem. Topics include but are not limited to the following:

  • Processes to minimize GHG emissions;
  • Renewable energy system design;
  • Applying optimization algorithms to sustainable production processes (chemical engineering/biological engineering);
  • Multicriteria decision-making models in production processes (ANP/AHP, TOPSIS, DEA model, etc.);
  • Sustainable supply chain management;
  • Operation research as applied to production and services management;
  • Sustainability in logistics and supply chains;
  • Optimization of logistics and SCM systems;
  • Inventory management;
  • Decision support systems for logistics and SCM.

Prof. Dr. Chia-Nan Wang
Dr. Nguyen Van Thanh
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

  • optimization algorithms
  • sustainable production
  • renewable energy
  • MCDM model
  • operations research
  • industry production process
  • zero-emission supply chains

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Published Papers (16 papers)

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Research

14 pages, 555 KiB  
Article
Inventory Turnover and Firm Profitability: A Saudi Arabian Investigation
by Musaab Alnaim and Amel Kouaib
Processes 2023, 11(3), 716; https://doi.org/10.3390/pr11030716 - 28 Feb 2023
Cited by 5 | Viewed by 5056
Abstract
The purpose of this paper is to explore the impact of inventory turnover on the profitability level of Saudi manufacturers. The data comprises 78 manufacturers listed on the Saudi Stock Exchange and was used to test the research hypothesis. The related data over [...] Read more.
The purpose of this paper is to explore the impact of inventory turnover on the profitability level of Saudi manufacturers. The data comprises 78 manufacturers listed on the Saudi Stock Exchange and was used to test the research hypothesis. The related data over the 2017–2021 period were collected from annual reports and the Datastream database. After running a multiple regression analysis with a fixed effects model, findings showed that the higher the inventory turnover ratio, the higher the cost which could be suppressed, and the greater the profitability of a company. The outcomes of this study have significant implications for managerial accounting issues in the setting of Saudi Arabia. Further, they provide policy recommendations to decision makers and assist managers in enhancing sustainability in the manufacturing sector. This research is the first to investigate this relationship including the impact of COVID-19 among Saudi companies in several industries, thus filling a gap in comparable research. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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22 pages, 2788 KiB  
Article
Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm
by António Agrela Freitas, Tânia Miranda Lima and Pedro Dinis Gaspar
Processes 2022, 10(7), 1364; https://doi.org/10.3390/pr10071364 - 13 Jul 2022
Cited by 5 | Viewed by 1996
Abstract
In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, [...] Read more.
In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are simultaneously minimized by the rotation and alternation of operative teams between those tasks with higher and lower values of metabolic energy required. We also verified that tasks with lower ergonomic-load requirements influence the reorganization of the task sequence by lowering the overall value of metabolic energy, which is reflected in the reduction of the ergonomic load. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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17 pages, 1123 KiB  
Article
Research on Profit Distribution of Logistics Alliance Considering Communication Structure and Task Completion Quality
by Panqian Dai, Jing Xu and Wen Li
Processes 2022, 10(6), 1139; https://doi.org/10.3390/pr10061139 - 6 Jun 2022
Cited by 3 | Viewed by 1935
Abstract
A reasonable profit distribution mechanism is the foundation for the formation and stability of the logistics alliance. On the basis of traditional influencing factors such as marginal contribution, resource input efficiency and risk-taking, the influence of communication structure restrictions and task completion quality [...] Read more.
A reasonable profit distribution mechanism is the foundation for the formation and stability of the logistics alliance. On the basis of traditional influencing factors such as marginal contribution, resource input efficiency and risk-taking, the influence of communication structure restrictions and task completion quality on the profit distribution of logistics alliance was further considered in this paper. Then, a two-stage profit distribution model for logistics alliance with two types of communication structures was constructed. According to this model, the initial distribution uses the vertical projection method weighting each influencing factor to form the distribution scheme of modified Shapley value and Average Tree solution; the secondary distribution further modifies the initial distribution scheme based on the fuzzy comprehensive evaluation of the logistics task completion quality, and forms the final distribution scheme. The example analysis shows that the profit distribution model established in this paper is more fair and feasible, which can effectively avoid the “hitchhiking” phenomenon, and can motivate members to work hard to complete the logistics task. In addition, the model can provide a method for the distribution of benefits for alliances with limited communication structures. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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12 pages, 1282 KiB  
Article
Logistics Service Provider Evaluation and Selection: Hybrid SERVQUAL–FAHP–TOPSIS Model
by Le Anh Luyen and Nguyen Van Thanh
Processes 2022, 10(5), 1024; https://doi.org/10.3390/pr10051024 - 20 May 2022
Cited by 11 | Viewed by 3677
Abstract
Production and business enterprises are aiming to improve their logistics activities in order to increase competitiveness. Therefore, the criteria and decision support models for selecting logistics service providers are significant to businesses. Fuzzy theory has been applied to almost all industrial engineering fields, [...] Read more.
Production and business enterprises are aiming to improve their logistics activities in order to increase competitiveness. Therefore, the criteria and decision support models for selecting logistics service providers are significant to businesses. Fuzzy theory has been applied to almost all industrial engineering fields, such as decision making, operations research, quality control, project scheduling and many more. In this research, the authors combined fuzzy theory and a Multicriteria Decision Making (MCDM) model for the evaluation and selection of potential third-party logistics (3PL) providers. The goal is to take the advantages of these approaches and allow for more accurate and balanced (symmetric) decision making through their integration. The main contribution of this study is that it develops a complete approach to assessing the quality of the logistics service industry. The combined method of the SERVQUAL and FAHP–TOPSIS models not only provides reasonable results, but it also allows decision makers to visualize the impact of different criteria on the final outcome. Furthermore, this integrated model can provide valuable insights and methods for other areas to define service quality. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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14 pages, 1506 KiB  
Article
Evaluating the Energy Efficiency and Environmental Impact of COVID-19 Vaccines Coolers through New Optimization Indexes: Comparison between Refrigeration Systems Using HFC or Natural Refrigerants
by Alexandre F. Santos, Pedro D. Gaspar and Heraldo J. L. de Souza
Processes 2022, 10(4), 790; https://doi.org/10.3390/pr10040790 - 17 Apr 2022
Cited by 2 | Viewed by 3066
Abstract
COVID-19 vaccines are used worldwide to promote immunity and, in that sense, vaccination is a step forward toward ending the pandemic. Nevertheless, current vaccines must be ultra-cold or cold-stored. Vaccine coolers’ energy demand and greenhouse gas emissions lead to a significant environmental impact. [...] Read more.
COVID-19 vaccines are used worldwide to promote immunity and, in that sense, vaccination is a step forward toward ending the pandemic. Nevertheless, current vaccines must be ultra-cold or cold-stored. Vaccine coolers’ energy demand and greenhouse gas emissions lead to a significant environmental impact. This article predicts the environmental and energy impacts of some COVID-19 vaccines: Moderna, Janssen, CoronaVac, Pfizer, AstraZeneca–Oxford–Covishield, and Sputnik V, in terms of carbon dioxide emissions using a new approach for the TEWI (Total Equivalent Warming Impact) methodology, with several options of refrigerants from halogenated to natural fluids such as propane, which is natural gas with low GWP (global warming potential). Through the application of new optimization indexes, it is concluded that the evaporation temperature of the refrigerant gas has a great influence on the sizing of the coolers. For example, for the same number of vaccines, the thermal load of Pfizer is more than double that of AstraZeneca–Covishield, CoronaVac, or Janssen, while the direct environmental impact is seven times greater. Another relevant factor is the choice of refrigerant. For example, the greenhouse effect varies greatly for the same brand of vaccine. The Moderna vaccine’s global warming potential (GWP) is 776 times higher using R-449A gas than using R-290 (propane gas). In Brazil, the refrigerators used to store the Pfizer vaccine have a total TEWI almost two times higher than the total TEWI of refrigerators using propane to store the Janssen vaccine. At this time of the pandemic, these optimization indexes can be used to support important decisions regarding the future selection of vaccine brands considering the energy consumption and environmental impact required for their storage. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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24 pages, 920 KiB  
Article
Queuing Models for Analyzing the Steady-State Distribution of Stochastic Inventory Systems with Random Lead Time and Impatient Customers
by Khalid A. Alnowibet, Adel F. Alrasheedi and Firdous S. Alqahtani
Processes 2022, 10(4), 624; https://doi.org/10.3390/pr10040624 - 23 Mar 2022
Cited by 6 | Viewed by 3948
Abstract
In material management, the inventory systems may have good management aspects in terms of materials; however, this negatively affects the relationship between the facility and customers. In classical inventory models, arriving demands are satisfied immediately if there is enough on-hand inventory. Traditional inventory [...] Read more.
In material management, the inventory systems may have good management aspects in terms of materials; however, this negatively affects the relationship between the facility and customers. In classical inventory models, arriving demands are satisfied immediately if there is enough on-hand inventory. Traditional inventory models consider optimization problems and find the optimal policy of decision variables without computing the stationary distribution of the inventory states for random demand. Hence, a detailed analysis of inventory management systems requires a joint distribution of system stock levels and the number of requests to be investigated thoroughly. This research provides a new stochastic mathematical model for inventory systems with lead times and impatient customers under deterministic and uniform order sizes. The proposed model identifies the performance measures in a stochastic environment, analyzing the properties of the inventory system with stochastic and probabilistic parameters, and finally, validating the model’s accuracy. To analyze the system, balance equations were derived from a mathematical characterization of the underlying queuing model dependent on the Markov chain formalism. The precise performance was achieved by examining the graphical representation of the service process in a steady-state as a function of both arrival distribution and the customer patience coefficient, while it was challenging to derive an optimal curve fit in a three-dimensional space that features two input variables and a single output variable. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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25 pages, 4002 KiB  
Article
A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling
by Ricardo Mesquita and Pedro D. Gaspar
Processes 2022, 10(1), 62; https://doi.org/10.3390/pr10010062 - 28 Dec 2021
Cited by 18 | Viewed by 4187
Abstract
Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned [...] Read more.
Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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19 pages, 20013 KiB  
Article
Risk Propagation of Concentralized Distribution Logistics Plan Change in Cruise Construction
by Yahong Zheng, Jiangcen Ke and Haiyan Wang
Processes 2021, 9(8), 1398; https://doi.org/10.3390/pr9081398 - 12 Aug 2021
Cited by 6 | Viewed by 2317
Abstract
Compared with the ordinary merchant ship building, the concentralized distribution in cruise building is more complex. Plan change is a common phenomenon in cruise building, and it is easy to lead to mismatch between production and logistics, resulting in risks such as production [...] Read more.
Compared with the ordinary merchant ship building, the concentralized distribution in cruise building is more complex. Plan change is a common phenomenon in cruise building, and it is easy to lead to mismatch between production and logistics, resulting in risks such as production schedule delay and inventory backlog. In order to reduce the adverse effects of plan change on the shipyard, it is necessary to conduct an in-depth study on the risks of a centralized distribution logistics plan. Based on the analysis of the composition of the centralized distribution logistics planning system, risk factors in different plan links are identified in this paper. A system dynamic model is constructed to simulate the propagation of five basic types of planning risk, including procurement plan, warehousing plan, pallet concentralization plan, distribution plan and production plan. In the case study of HVAC (heating, ventilation and air conditioning) materials, the values of risk factors are estimated though consulting experts with questionnaire. The weight of each risk factor in each subsystem is calculated by a method combined with analytic hierarchy process and coefficient of variation method. Through the simulation experiments carried out in Vensim, it is found that both inventory backlog risk and cruise construction schedule delay risk increase with the increasement of estimated values of risk factors, which is an effective proof of the rationality of the model, and that the most sensitive risk factor for both the two kinds of risk is production planning risk. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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19 pages, 1320 KiB  
Article
Scheduling Two Identical Parallel Machines Subjected to Release Times, Delivery Times and Unavailability Constraints
by Adel M. Al-Shayea, Mustafa Saleh, Moath Alatefi and Mageed Ghaleb
Processes 2020, 8(9), 1025; https://doi.org/10.3390/pr8091025 - 21 Aug 2020
Cited by 5 | Viewed by 3287
Abstract
This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than [...] Read more.
This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than making them always available. The objective is to minimize the makespan (Cmax). A lower bound (LB) of the makespan for the considered problem was proposed. The GA performance was evaluated in terms of the relative percentage deviation (RPD) (the relative distance to the LB) and central processing unit (CPU) time. Response surface methodology (RSM) was used to optimize the GA parameters, namely, population size, crossover probability, mutation probability, mutation ratio, and pressure selection, which simultaneously minimize the RPD and CPU time. The optimized settings of the GA parameters were used to further analyze the scheduling problem. Factorial design of the scheduling problem input variables, namely, processing times, release times, delivery times, availability and unavailability periods, and number of jobs, was used to evaluate their effects on the RPD and CPU time. The results showed that increasing the release time intervals, decreasing the availability periods, and increasing the number of jobs increase the RPD and CPU time and make the problem very difficult to reach the LB. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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15 pages, 2520 KiB  
Article
Simulation-Based Optimization of a Two-Echelon Continuous Review Inventory Model with Lot Size-Dependent Lead Time
by Ibrahim Alharkan, Mustafa Saleh, Mageed Ghaleb, Abdulsalam Farhan and Ahmed Badwelan
Processes 2020, 8(9), 1014; https://doi.org/10.3390/pr8091014 - 19 Aug 2020
Cited by 5 | Viewed by 4196
Abstract
This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size [...] Read more.
This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size inspection times. A simulation-based model is proposed for optimizing order quantity (Q) and reorder point (r) that minimize the total inventory costs (holding, backlogging, and ordering costs) in a two-echelon supply chain, which consists of two identical retailers, a distributor, and a supplier. The simulation model is created with Arena software and validated using an analytical model. The model is interfaced with the OptQuest optimization tool, which is embedded in the Arena software, to search for the least cost lot sizes and reorder points. The proposed model is designed for general demand distributions that are too complex to be solved analytically. Hence, for the first time, the present study considers the stochastic inventory continuous review policy (Q,r) in a two-echelon supply chain system with lot size-dependent lead time L(Q). An experimental study is conducted, and results are provided to assess the developed model. Results show that the optimized Q and r for different distributions of daily demand are not the same even if the associated total inventory costs are close to each other. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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17 pages, 2841 KiB  
Article
Kinetic Parameter Determination for Depolymerization of Biomass by Inverse Modeling and Metaheuristics
by Dalyndha Aztatzi-Pluma, Susana Figueroa-Gerstenmaier, Luis Carlos Padierna, Edgar Vázquez-Núñez and Carlos E. Molina-Guerrero
Processes 2020, 8(7), 836; https://doi.org/10.3390/pr8070836 - 14 Jul 2020
Cited by 1 | Viewed by 2602
Abstract
A computational methodology based on inverse modeling and metaheuristics is presented for determining the best parameters of kinetic models aimed to predict the behavior of biomass depolymerization processes during size scaling up. The Univariate Marginal Distribution algorithm, particle swarm optimization, and Interior-Point algorithm [...] Read more.
A computational methodology based on inverse modeling and metaheuristics is presented for determining the best parameters of kinetic models aimed to predict the behavior of biomass depolymerization processes during size scaling up. The Univariate Marginal Distribution algorithm, particle swarm optimization, and Interior-Point algorithm were applied to obtain the values of the kinetic parameters (KM and Vmax) of four mathematical models based on the Michaelis–Menten equation: (i) Traditional Michaelis–Menten, (ii) non-competitive inhibition, (iii) competitive inhibition, and (iv) substrate inhibition. The kinetic data were obtained from our own experimentation in micro-scale. The parameters obtained from an optimized micro-scale experiment were compared with a bench scale experiment (0.5 L). Regarding the metaheuristic optimizers, it is concluded that the Interior-Point algorithm is effective in solving inverse modeling problems and has the best prediction power. According to the results, the Traditional model adequately describes the micro-scale experiments. It was found that the Traditional model with optimized parameters was able to predict the behavior of the depolymerization process during size scaling up. The methodology followed in this study can be adopted as a starting point for the solution of future inverse modeling problems. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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17 pages, 9162 KiB  
Article
Development of an Optimal Path Algorithm for Construction Equipment
by Hak June Lee and So Young Lim
Processes 2020, 8(6), 674; https://doi.org/10.3390/pr8060674 - 8 Jun 2020
Cited by 4 | Viewed by 3611
Abstract
The fourth industrial revolution based on information and communication technology (ICT and IoT) is converging into the overall realm of technology, economy and society, creating innovative changes. In line with these changes, research is being actively carried out to integrate information and communication [...] Read more.
The fourth industrial revolution based on information and communication technology (ICT and IoT) is converging into the overall realm of technology, economy and society, creating innovative changes. In line with these changes, research is being actively carried out to integrate information and communication with automation at construction sites. This study was started to analyze problems arising from inefficient operation of construction equipment through analysis of risks arising at construction sites and to provide solutions related to these problems. In order to provide the optimal route of movement of construction equipment, an expert survey was conducted and an algorithm was developed to establish the optimal route of movement by analyzing the weights for each item of the survey. The adequacy of the algorithm was determined by comparing the developed algorithm with the actual data of the construction site in operation, and a safe and productive route as well as problems related to invisible safety are provided through the developed algorithm. Applying the optimal-route-generation algorithm conducted in this study to the construction site will not only increase productivity within the workplace, but also ultimately save time in operating equipment and increase safety. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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24 pages, 5078 KiB  
Article
Minimize the Route Length Using Heuristic Method Aided with Simulated Annealing to Reinforce Lean Management Sustainability
by Ahmed M. Abed and Samia Elattar
Processes 2020, 8(4), 495; https://doi.org/10.3390/pr8040495 - 23 Apr 2020
Cited by 4 | Viewed by 3517
Abstract
Cost reduction is a cornerstone of the Lean administration’s sustainability through modify its algorithms scheme to become multi-useful. This paper focuses on control “movement” waste, to minimize pipeline, cabling and sewerage network deployments time, to avoid demurrages (i.e., constructor sectors) and quickens planning [...] Read more.
Cost reduction is a cornerstone of the Lean administration’s sustainability through modify its algorithms scheme to become multi-useful. This paper focuses on control “movement” waste, to minimize pipeline, cabling and sewerage network deployments time, to avoid demurrages (i.e., constructor sectors) and quickens planning through two stages. The first belongs to the build constrained hybridization of published heuristic routing methods (e.g., S-Shape, Mid-point, Largest-Gap, Return, Ascending, FLA-5, FLA-6 [Flow Line Analysis], and Composite) to select the shortest path that serves many locations (i.e., Plan-A), while allowing for the modification of these locations during service (i.e., Plan-B). The new locations are grouped into two clusters, the first of which lay on the shortest preferred path, while the second cluster contains locations that do not lay on the preferred path and are therefore moved on the backlogs-list, then use Simulated Annealing when to serve them. Finally, the impact of the selected performance is investigated after studying its correlation with another published effective one under cost considerations. The computational results of proposed Minimize-Route-Length aided with simulated annealing (MRL-SA) significantly outperform others in terms of the performance of the routing heuristics and total costs and develop the Last Planner System, which has a good reputation in construction projects and approve the proposed algorithm to maintain its competitiveness sustainability. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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23 pages, 6057 KiB  
Article
A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems
by Ai-Qing Tian, Shu-Chuan Chu, Jeng-Shyang Pan and Yongquan Liang
Processes 2020, 8(3), 356; https://doi.org/10.3390/pr8030356 - 20 Mar 2020
Cited by 30 | Viewed by 4500
Abstract
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a [...] Read more.
The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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13 pages, 836 KiB  
Article
Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks
by Rui-Na Fan, Fan-Qi Ma and Quan-Lin Li
Processes 2020, 8(3), 345; https://doi.org/10.3390/pr8030345 - 18 Mar 2020
Cited by 7 | Viewed by 2922
Abstract
The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs [...] Read more.
The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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24 pages, 1126 KiB  
Article
Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power
by Jeng-Shyang Pan, Pei Hu and Shu-Chuan Chu
Processes 2019, 7(11), 845; https://doi.org/10.3390/pr7110845 - 11 Nov 2019
Cited by 100 | Viewed by 4292
Abstract
Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. [...] Read more.
Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. Parallel meta-heuristic saves computation time and improves solution quality. Four communication strategies, which include ranking, combination, dynamic change and hybrid, are introduced to balance exploration and exploitation. The dynamic change strategy is to dynamically increase or decrease the members of subgroup to keep the diversity of the population. The benchmark functions show that the algorithms have excellent performance in exploration and exploitation. In the end, they are applied to successfully realize the prediction for wind power by training the parameters of the neural network. Full article
(This article belongs to the Special Issue Optimization Algorithms Applied to Sustainable Production Processes)
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