Aiming at Distributed Permutation Flow-shop Scheduling Problems (DPFSPs), this study took the minimization of the maximum completion time of the workpieces to be processed in all production tasks as the goal, and took the multi-agent Reinforcement Learning (RL) method as the main frame of the solution model, then, combining with the NASH equilibrium theory and the RL method, it proposed a NASH Q-Learning algorithm for Distributed Flow-shop Scheduling Problem (DFSP) based on Mean Field (MF). In the RL part, this study designed a two-layer online learning mode in which the sample collection and the training improvement proceed alternately, the outer layer collects samples, when the collected samples meet the requirement of batch size, it enters to the inner layer loop, which uses the Q-learning model-free batch processing mode to proceed, and adopts neural network to approximate the value function to adapt to large-scale problems. By comparing the Average Relative Percentage Deviation (ARPD) index of the benchmark test questions, the calculation results of the proposed algorithm outperformed other similar algorithms, which proved the feasibility and efficiency of the proposed algorithm.
{"title":"A new solution to distributed permutation flow shop scheduling problem based on NASH Q-Learning","authors":"J. Ren, C. Ye, Y. Li","doi":"10.14743/apem2021.3.399","DOIUrl":"https://doi.org/10.14743/apem2021.3.399","url":null,"abstract":"Aiming at Distributed Permutation Flow-shop Scheduling Problems (DPFSPs), this study took the minimization of the maximum completion time of the workpieces to be processed in all production tasks as the goal, and took the multi-agent Reinforcement Learning (RL) method as the main frame of the solution model, then, combining with the NASH equilibrium theory and the RL method, it proposed a NASH Q-Learning algorithm for Distributed Flow-shop Scheduling Problem (DFSP) based on Mean Field (MF). In the RL part, this study designed a two-layer online learning mode in which the sample collection and the training improvement proceed alternately, the outer layer collects samples, when the collected samples meet the requirement of batch size, it enters to the inner layer loop, which uses the Q-learning model-free batch processing mode to proceed, and adopts neural network to approximate the value function to adapt to large-scale problems. By comparing the Average Relative Percentage Deviation (ARPD) index of the benchmark test questions, the calculation results of the proposed algorithm outperformed other similar algorithms, which proved the feasibility and efficiency of the proposed algorithm.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88572099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ironmaking-steelmaking interface of the steel manufacturing process involves the hot metal ladle circulation and the energy dissipation which are coupled processes with an interrelated but independent relation. Therefore, the synergistic operation of the material flow and the energy flow at the interface is momentous to the effective production of the ironmaking-steelmaking section. However, there is a lack of solutions to realize the synergy. Here, we presented a coupling simulation model for the material flow and energy flow of the ironmaking-steelmaking interface, based on the mathematical description of their operation behaviors, the operation and technical model of the production equipment and the temperature-decreasing model of the ladle. Further, the coupling simulation model was applied to a concrete ironmaking-steelmaking interface using the One-Ladle Technique. The coupling simulation model proved its performance in providing comprehensive decision-making supports and optimized production management strategies by achieving a solution that results in a decline of 10 °C in the average temperature drop of the hot metal and a reduction in the cost per tonne of steel by CNY 1.02.
{"title":"Simulation-based optimization of coupled material–energy flow at ironmaking-steelmaking interface using One-Ladle Technique","authors":"Z. Hu, Z. Zheng, L. He, J. Fan, F. Li","doi":"10.14743/apem2021.3.405","DOIUrl":"https://doi.org/10.14743/apem2021.3.405","url":null,"abstract":"The ironmaking-steelmaking interface of the steel manufacturing process involves the hot metal ladle circulation and the energy dissipation which are coupled processes with an interrelated but independent relation. Therefore, the synergistic operation of the material flow and the energy flow at the interface is momentous to the effective production of the ironmaking-steelmaking section. However, there is a lack of solutions to realize the synergy. Here, we presented a coupling simulation model for the material flow and energy flow of the ironmaking-steelmaking interface, based on the mathematical description of their operation behaviors, the operation and technical model of the production equipment and the temperature-decreasing model of the ladle. Further, the coupling simulation model was applied to a concrete ironmaking-steelmaking interface using the One-Ladle Technique. The coupling simulation model proved its performance in providing comprehensive decision-making supports and optimized production management strategies by achieving a solution that results in a decline of 10 °C in the average temperature drop of the hot metal and a reduction in the cost per tonne of steel by CNY 1.02.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88632275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article presents the application of the original methodology to support tactical capacity planning in a medium-sized manufacturing company. Its essence is to support medium-term decisions regarding the development of the production system through economic assessment of potential change scenarios. It has been assumed that the developed methodology should be adapted to small and medium-sized enterprises (SMEs). Due to their flexibility, they usually have limited time for decision-making, and due to limited financial resources, they rely on internal competencies. The proposed approach that does not require mastery of mathematical modelling but allows streamlining capacity planning decisions. It uses the reasoning of throughput accounting (TA) supported by data obtained based on discrete event simulation (DES). Using these related tools in the design and analysis of change scenarios, make it possible for SME managers to make a rational decision regarding the development of the production system. Case studies conducted in a roof window manufacturing company showed the methodology. The application example presented in the article includes seven change scenarios analyzed based on computer simulations by the software Tecnomatix Plant Simulation. The implementation of the approach under real conditions has shown that a rational decision-making process is possible over time scale and with the resources available to SMEs for this type of decision.
{"title":"Tactical manufacturing capacity planning based on discrete event simulation and throughput accounting: A case study of medium sized production enterprise","authors":"M. Jurczyk-Bunkowska","doi":"10.14743/apem2021.3.404","DOIUrl":"https://doi.org/10.14743/apem2021.3.404","url":null,"abstract":"The article presents the application of the original methodology to support tactical capacity planning in a medium-sized manufacturing company. Its essence is to support medium-term decisions regarding the development of the production system through economic assessment of potential change scenarios. It has been assumed that the developed methodology should be adapted to small and medium-sized enterprises (SMEs). Due to their flexibility, they usually have limited time for decision-making, and due to limited financial resources, they rely on internal competencies. The proposed approach that does not require mastery of mathematical modelling but allows streamlining capacity planning decisions. It uses the reasoning of throughput accounting (TA) supported by data obtained based on discrete event simulation (DES). Using these related tools in the design and analysis of change scenarios, make it possible for SME managers to make a rational decision regarding the development of the production system. Case studies conducted in a roof window manufacturing company showed the methodology. The application example presented in the article includes seven change scenarios analyzed based on computer simulations by the software Tecnomatix Plant Simulation. The implementation of the approach under real conditions has shown that a rational decision-making process is possible over time scale and with the resources available to SMEs for this type of decision.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75190536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Purba, A. Nindiani, A. Trimarjoko, C. Jaqin, S. Hasibuan, S. Tampubolon
Industrial sustainability is an important attribute and becomes a parameter of the business success. Quality improvement with an indicator of increasing process capability will affect productivity improvements and lead to industrial competitiveness and maintain industrial sustainability. The purpose of this paper is to obtain a relationship between the consistency of the DMAIC phase to increase the sigma level in productivity improvement and industrial sustainability. This paper applied for a systematic literature review from various sources of trusted articles from 2006 to 2019 using the keywords “Six Sigma, Productivity, and Industrial Sustainability.” A matrix was developed to provide synthesis and summary of the literature. Six Sigma approach has been successful in reducing product variation, defects, cycle time, production costs, as well as increasing customer satisfaction, cost savings, profits, and competitiveness to maintain industrial sustainability. Extraction and synthesis in this study managed to obtain seven objectives value that found a consistent relationship between the DMAIC phase of increasing sigma levels, productivity, and industrial sustainability. The broad scope of Six Sigma literature is very beneficial for organizations to understand the critical variables and key success factors in Six Sigma implementation, which leads to substantial long-term continuous improvement, the value of money, and business.
{"title":"Increasing Sigma levels in productivity improvement and industrial sustainability with Six Sigma methods in manufacturing industry: A systematic literature review","authors":"H. Purba, A. Nindiani, A. Trimarjoko, C. Jaqin, S. Hasibuan, S. Tampubolon","doi":"10.14743/apem2021.3.402","DOIUrl":"https://doi.org/10.14743/apem2021.3.402","url":null,"abstract":"Industrial sustainability is an important attribute and becomes a parameter of the business success. Quality improvement with an indicator of increasing process capability will affect productivity improvements and lead to industrial competitiveness and maintain industrial sustainability. The purpose of this paper is to obtain a relationship between the consistency of the DMAIC phase to increase the sigma level in productivity improvement and industrial sustainability. This paper applied for a systematic literature review from various sources of trusted articles from 2006 to 2019 using the keywords “Six Sigma, Productivity, and Industrial Sustainability.” A matrix was developed to provide synthesis and summary of the literature. Six Sigma approach has been successful in reducing product variation, defects, cycle time, production costs, as well as increasing customer satisfaction, cost savings, profits, and competitiveness to maintain industrial sustainability. Extraction and synthesis in this study managed to obtain seven objectives value that found a consistent relationship between the DMAIC phase of increasing sigma levels, productivity, and industrial sustainability. The broad scope of Six Sigma literature is very beneficial for organizations to understand the critical variables and key success factors in Six Sigma implementation, which leads to substantial long-term continuous improvement, the value of money, and business.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85357611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Xu, M. S. Yang, Y. Li, X. Q. Gao, Z.Y. Wang, L. Ren
Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.
{"title":"A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms","authors":"E. Xu, M. S. Yang, Y. Li, X. Q. Gao, Z.Y. Wang, L. Ren","doi":"10.14743/apem2021.3.407","DOIUrl":"https://doi.org/10.14743/apem2021.3.407","url":null,"abstract":"Aiming at the problem that the downtime is simply assumed to be constant and the limited resources are not considered in the current selective maintenance of the series-parallel system, a three-objective selective maintenance model for the series-parallel system is established to minimize the maintenance cost, maximize the probability of completing the next task and minimize the downtime. The maintenance decision-making model and personnel allocation model are combined to make decisions on the optimal length of each equipment’s rest period, the equipment to be maintained during the rest period and the maintenance level. For the multi-objective model established, the NSGA-III algorithm is designed to solve the model. Comparing with the NSGA-II algorithm that only considers the first two objectives, it is verified that the designed multi-objective model can effectively reduce the downtime of the system.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81943826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electric vehicle battery recharging on the swapping mode has grown up as an important option other than the plug-in recharging mode in China, given that several auto giants have been dedicated in constructing their battery swapping systems. However, the lack of effective operational methods on battery recharging and transportation scheduling has aroused a big challenge on the practical application of the swapping mode, which enables the necessity of our work. This study proposes a joint optimization model of recharging and scheduling of electric vehicle batteries with a dynamic electricity price system which is able to identify the optimal charging arrangement (the recharging time and the quantity of recharging batteries) as well as the optimal transportation arrangement (the transportation time and the quantity of transporting batteries). For the validation purpose, a numerical study is implemented based on dynamic electricity prices in Beijing. A sensitivity analysis of parameters is carried out to increase the robustness and provide more managerial insights of the model.
{"title":"Recharging and transportation scheduling for electric vehicle battery under the swapping mode","authors":"A. Huang, Y. Zhang, Z. He, G. Hua, X. Shi","doi":"10.14743/apem2021.3.406","DOIUrl":"https://doi.org/10.14743/apem2021.3.406","url":null,"abstract":"Electric vehicle battery recharging on the swapping mode has grown up as an important option other than the plug-in recharging mode in China, given that several auto giants have been dedicated in constructing their battery swapping systems. However, the lack of effective operational methods on battery recharging and transportation scheduling has aroused a big challenge on the practical application of the swapping mode, which enables the necessity of our work. This study proposes a joint optimization model of recharging and scheduling of electric vehicle batteries with a dynamic electricity price system which is able to identify the optimal charging arrangement (the recharging time and the quantity of recharging batteries) as well as the optimal transportation arrangement (the transportation time and the quantity of transporting batteries). For the validation purpose, a numerical study is implemented based on dynamic electricity prices in Beijing. A sensitivity analysis of parameters is carried out to increase the robustness and provide more managerial insights of the model.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76112063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services.
{"title":"Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature","authors":"Y.D. Zhang, L. Liao, Q. Yu, W. Ma, K. Li","doi":"10.14743/apem2021.3.400","DOIUrl":"https://doi.org/10.14743/apem2021.3.400","url":null,"abstract":"Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77662216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Printing companies are commonly SMEs with high flow of materials, which management could be significantly improved through the digitalization. In this study we propose a smart Warehouse 4.0 solution by using QR code, open-source software tools for machine vision and conventional surveillance equipment. Although there have been concerns regarding the usage of QR in logistics, it has shown to be suitable for the particular use-case as pallets are static in the interwarehouse. The reliability of reading of QR codes was achieved by using multiple IP cameras, so that sub-optimal view angle or light reflection is compensated with alternative views. Since surveillance technology and machine vision are constantly evolving and becoming more affordable, we report that more attention needs to be invested into their adaptation to fit the needs and budgets of SMEs, which are the industrial cornerstone in the most developed countries. The demo of proposed solution is available on the public repository https://github.com/ArsoVukicevic/PalletManagement/.
{"title":"A smart Warehouse 4.0 approach for the pallet management using machine vision and Internet of Things (IoT): A real industrial case study","authors":"A. Vukičević, M. Mladineo, N. Banduka, I. Macuzic","doi":"10.14743/apem2021.3.401","DOIUrl":"https://doi.org/10.14743/apem2021.3.401","url":null,"abstract":"Printing companies are commonly SMEs with high flow of materials, which management could be significantly improved through the digitalization. In this study we propose a smart Warehouse 4.0 solution by using QR code, open-source software tools for machine vision and conventional surveillance equipment. Although there have been concerns regarding the usage of QR in logistics, it has shown to be suitable for the particular use-case as pallets are static in the interwarehouse. The reliability of reading of QR codes was achieved by using multiple IP cameras, so that sub-optimal view angle or light reflection is compensated with alternative views. Since surveillance technology and machine vision are constantly evolving and becoming more affordable, we report that more attention needs to be invested into their adaptation to fit the needs and budgets of SMEs, which are the industrial cornerstone in the most developed countries. The demo of proposed solution is available on the public repository https://github.com/ArsoVukicevic/PalletManagement/.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84075397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Y. J. Wang, Nan Wang, S. Cheng, X. C. Zhang, H. Y. Liu, J. L. Shi, Q. Y. Ma, M. J. Zhou
Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer components.
{"title":"Optimization of disassembly line balancing using an improved multi-objective Genetic Algorithm","authors":"Y. J. Wang, Nan Wang, S. Cheng, X. C. Zhang, H. Y. Liu, J. L. Shi, Q. Y. Ma, M. J. Zhou","doi":"10.14743/apem2021.2.397","DOIUrl":"https://doi.org/10.14743/apem2021.2.397","url":null,"abstract":"Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer components.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85579617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.
{"title":"Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP)","authors":"Y. Wang, X. Lu, J. Shen","doi":"10.14743/apem2021.2.392","DOIUrl":"https://doi.org/10.14743/apem2021.2.392","url":null,"abstract":"The multiple traveling salesman problem (mTSP) is an extension of the traveling salesman problem (TSP), which has wider applications in real life than the traveling salesman problem such as transportation and delivery, task allocation, etc. In this paper, an improved genetic algorithm (VNS-GA) that uses polar coordinate classification to generate the initial solutions is proposed. It integrates the variable neighbourhood algorithm to solve the multiple objective optimization of the mTSP with workload balance. Aiming to workload balance, the first design of this paper is about generating initial solutions based on the polar coordinate classification. Then a distance comparison insertion operator is designed as a neighbourhood action for allocating paths in a targeted manner. Finally, the neighbourhood descent process in the variable neighbourhood algorithm is fused into the genetic algorithm for the expansion of search space. The improved algorithm is tested on the TSPLIB standard data set and compared with other genetic algorithms. The results show that the improved genetic algorithm can increase computational efficiency and obtain a better solution for workload balance and this algorithm has wild applications in real life such as multiple robots task allocation, school bus routing problem and other optimization problems.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90300852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：