In the context of the new round of manufacturing innovation, the sharing economy drives the transformation of manufacturing industry to accelerate the integration and development. However, there are some problems in the process of manufacturing capacity sharing, such as information privacy and security, and difficulty in tracing the sharing process, etc. The application of blockchain technology can effectively solve these problems. To explore the capacity sharing behaviour of manufacturing enterprises from the perspective of blockchain, the article combines evolutionary game theory and constructs a tripartite game model of manufacturing capacity sharing. The replication dynamics and evolutionary stability of the model are analysed using evolutionary game theory, and numerical simulations are carried out using MATLAB software to analyse the impact of parameter changes on the evolutionary outcome. The research results show that the incentive and penalty coefficients under blockchain technology have a facilitating effect on enterprises to carry out sharing, and the enhancement of reputation gain coefficient and loss can promote positive services on the platform.
{"title":"Blockchain-based tripartite evolutionary game study of manufacturing capacity sharing","authors":"T.Y. Wang, H. Zhang","doi":"10.14743/apem2023.2.469","DOIUrl":"https://doi.org/10.14743/apem2023.2.469","url":null,"abstract":"In the context of the new round of manufacturing innovation, the sharing economy drives the transformation of manufacturing industry to accelerate the integration and development. However, there are some problems in the process of manufacturing capacity sharing, such as information privacy and security, and difficulty in tracing the sharing process, etc. The application of blockchain technology can effectively solve these problems. To explore the capacity sharing behaviour of manufacturing enterprises from the perspective of blockchain, the article combines evolutionary game theory and constructs a tripartite game model of manufacturing capacity sharing. The replication dynamics and evolutionary stability of the model are analysed using evolutionary game theory, and numerical simulations are carried out using MATLAB software to analyse the impact of parameter changes on the evolutionary outcome. The research results show that the incentive and penalty coefficients under blockchain technology have a facilitating effect on enterprises to carry out sharing, and the enhancement of reputation gain coefficient and loss can promote positive services on the platform.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates a flow shop scheduling problem with queue time limits and skipping jobs, which are common scheduling requirements for semiconductor and printed circuit board manufacturing systems. These manufacturing systems involve the most complex processes, which are strictly controlled and constrained to manufacture high-quality products and satisfy dynamic customer orders. Further, queue times between consecutive stages are limited. Given that the queue times are limited, jobs must begin the next step within the maximum queue time after the jobs in the previous step are completed. In the considered flow shop, several jobs can skip the first step, referred to as skipping jobs. Skipping jobs exist because of multiple types of products processed in the same flow shop. For the considered flow shop, this paper proposes a mathematical programming formulation and a genetic algorithm to minimize the makespan. The GA demonstrated its strengths through comprehensive computational experiments, demonstrating its effectiveness and efficiency. As the problem size increased, the GA's performance improved noticeably, while maintaining acceptable computation times for real-world fab facilities. We also validated its performance in various scenarios involving queue time limits and skipping jobs, to further emphasize its capabilities.
本研究探讨了一个具有排队时间限制和跳过作业的流水车间调度问题，这是半导体和印刷电路板制造系统的常见调度要求。这些制造系统涉及最复杂的流程，这些流程受到严格控制和约束，以制造高质量的产品并满足动态的客户订单。此外，连续阶段之间的排队时间是有限的。鉴于队列时间有限，作业必须在上一步作业完成后的最长队列时间内开始下一步作业。在所考虑的流水车间中，有几项作业可以跳过第一步，称为跳过作业。跳过作业之所以存在，是因为在同一流程车间中要处理多种类型的产品。针对所考虑的流程车间，本文提出了一种数学编程公式和一种遗传算法，以最小化作业时间。通过全面的计算实验，遗传算法展示了它的优势，证明了它的有效性和效率。随着问题规模的增大，遗传算法的性能得到了明显改善，同时保持了现实世界工厂可接受的计算时间。我们还在涉及队列时间限制和跳过作业的各种情况下验证了 GA 的性能，以进一步强调其能力。
{"title":"Genetic algorithm-based approach for makespan minimization in a flow shop with queue time limits and skip-ping jobs","authors":"J.H. Han, Lee J.Y.","doi":"10.14743/apem2023.2.463","DOIUrl":"https://doi.org/10.14743/apem2023.2.463","url":null,"abstract":"This study investigates a flow shop scheduling problem with queue time limits and skipping jobs, which are common scheduling requirements for semiconductor and printed circuit board manufacturing systems. These manufacturing systems involve the most complex processes, which are strictly controlled and constrained to manufacture high-quality products and satisfy dynamic customer orders. Further, queue times between consecutive stages are limited. Given that the queue times are limited, jobs must begin the next step within the maximum queue time after the jobs in the previous step are completed. In the considered flow shop, several jobs can skip the first step, referred to as skipping jobs. Skipping jobs exist because of multiple types of products processed in the same flow shop. For the considered flow shop, this paper proposes a mathematical programming formulation and a genetic algorithm to minimize the makespan. The GA demonstrated its strengths through comprehensive computational experiments, demonstrating its effectiveness and efficiency. As the problem size increased, the GA's performance improved noticeably, while maintaining acceptable computation times for real-world fab facilities. We also validated its performance in various scenarios involving queue time limits and skipping jobs, to further emphasize its capabilities.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Muñoz-Ibáñez, I. Chairez, M. Jimenez-Martinez, A. Molina, M. Alfaro-Ponce
The techniques employed to manage an industrial project are based on tools that aim to achieve the objectives set by an organization. Most of these techniques consider the development of operative and predictive models. The difficulty in developing project planning models relies on estimating large sets of parameters and the need to include model sections of poorly identifiable, that increase costs and time. This work develops a hybrid forecasting model for all the phases that make up die-casting projects through a series of parameters and sub-models that contemplate the particularities of each case, thereby achieving greater precision in the forecast. The model identifies the cost and time factors that affect project planning, specifically in the die-casting industry, and intends to predict their future behaviour when certain initially given conditions are modified. To estimate the parameters of the hybrid model, several factors in the processes were considered that interact in this industry, such as primary matter costs and activities associated to the process. The considered processes that have a substantial economic impact on the implementation of the project were selected. The criteria for this selection considered identifying the relevant parts of the design and manufacturing in the die-casting industry. Process factors such as the Cost of aluminium and its related activities, whose processes will be grouped into cost and time entities to build a set of metrics that allow better control over them. Finally, the proposed model is based on analytical, parametric, and analog methods that achieve accuracy greater than 85 % in predicting the time and Cost of the process.
{"title":"Hybrid forecasting modelling of cost and time entities for planning and optimizing projects in the die-cast aluminium industry","authors":"C. Muñoz-Ibáñez, I. Chairez, M. Jimenez-Martinez, A. Molina, M. Alfaro-Ponce","doi":"10.14743/apem2023.2.464","DOIUrl":"https://doi.org/10.14743/apem2023.2.464","url":null,"abstract":"The techniques employed to manage an industrial project are based on tools that aim to achieve the objectives set by an organization. Most of these techniques consider the development of operative and predictive models. The difficulty in developing project planning models relies on estimating large sets of parameters and the need to include model sections of poorly identifiable, that increase costs and time. This work develops a hybrid forecasting model for all the phases that make up die-casting projects through a series of parameters and sub-models that contemplate the particularities of each case, thereby achieving greater precision in the forecast. The model identifies the cost and time factors that affect project planning, specifically in the die-casting industry, and intends to predict their future behaviour when certain initially given conditions are modified. To estimate the parameters of the hybrid model, several factors in the processes were considered that interact in this industry, such as primary matter costs and activities associated to the process. The considered processes that have a substantial economic impact on the implementation of the project were selected. The criteria for this selection considered identifying the relevant parts of the design and manufacturing in the die-casting industry. Process factors such as the Cost of aluminium and its related activities, whose processes will be grouped into cost and time entities to build a set of metrics that allow better control over them. Finally, the proposed model is based on analytical, parametric, and analog methods that achieve accuracy greater than 85 % in predicting the time and Cost of the process.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Lean concept was devised in large business systems and is tailored to this way of conducting business. It is a set of principles, techniques and procedures used to identify and eliminate losses within processes. The results of applying this concept are impressive. Western businesses are delighted with the success of large enterprises that have implemented or have begun to implement the Lean concept. Considering the structures of business systems in transitional and EU countries, a question has arisen as to whether it is possible to apply the Lean concept to small and medium-sized enterprises, as these account for more than 99 % of all business systems. The research which was conducted with the goal of designing a suitable model for the implementation of the Lean concept in small to medium-sized enterprises was based on an analysis of the essential elements of this concept. This article presents part of the conducted research that refers to analysis of losses and identification of the dominant losses according to the opinions of real sector experts and scientists from the academic community. The results of this research were used to define procedures for the elimination of major losses and design a final model for the implementation of the Lean concept in small and medium-sized enterprises.
{"title":"Ranking dominant losses in small and medium-sized enterprises (SMEs) in the context of the lean concept application","authors":"V. Kondic, L. Maglic, L. Runje, D. Maric","doi":"10.14743/apem2023.2.467","DOIUrl":"https://doi.org/10.14743/apem2023.2.467","url":null,"abstract":"The Lean concept was devised in large business systems and is tailored to this way of conducting business. It is a set of principles, techniques and procedures used to identify and eliminate losses within processes. The results of applying this concept are impressive. Western businesses are delighted with the success of large enterprises that have implemented or have begun to implement the Lean concept. Considering the structures of business systems in transitional and EU countries, a question has arisen as to whether it is possible to apply the Lean concept to small and medium-sized enterprises, as these account for more than 99 % of all business systems. The research which was conducted with the goal of designing a suitable model for the implementation of the Lean concept in small to medium-sized enterprises was based on an analysis of the essential elements of this concept. This article presents part of the conducted research that refers to analysis of losses and identification of the dominant losses according to the opinions of real sector experts and scientists from the academic community. The results of this research were used to define procedures for the elimination of major losses and design a final model for the implementation of the Lean concept in small and medium-sized enterprises.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.
{"title":"An improved discrete particle swarm optimization approach for a multi-objective optimization model of an urban logistics distribution network considering traffic congestion","authors":"K. Li, D. Li, H.Q. Ma","doi":"10.14743/apem2023.2.468","DOIUrl":"https://doi.org/10.14743/apem2023.2.468","url":null,"abstract":"To optimize urban logistics networks, this paper proposes a multi-objective optimization model for urban logistics distribution networks (ULDN). The model optimizes vehicle usage costs, transportation costs, penalty costs for failing to meet time windows, and carbon emission costs, while also considering the impact of urban road traffic congestion on total costs. To solve the model, a DPSO (Discrete Particle Swarm Optimization) algorithm based on the basic principle of PSO (Particle Swarm Optimization) is proposed. The DPSO introduces multiple populations to handle multiple targets and uses a variable neighbourhood search strategy to improve the search ability of particles, which helps to improve the local search ability of the algorithm. Simulation results demonstrate the effectiveness of the proposed model in avoiding traffic congestion, reducing carbon emissions costs, and time penalty costs. The optimization comparison results between DPSO and PSO also verify the superiority of the DPSO algorithm. The proposed model can be applied to real-world urban logistics networks to improve their efficiency, reduce costs, and minimize environmental impact.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"116 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Bojic, M. Maslaric, D. Mircetic, S. Nikolicic, V. Todorovic
To stay competitive on the constantly changing and demanding market, production systems need to optimize their performance daily. This is particularly challenging in labour-intensive industries, which is characterized by highly volatile customer demand and significant daily variability of available workers. The Uncertainty related to the key production parameters in the industry is causing disruptions in long-term production planning and optimization, which leads to the long lead production times, operational risks and accumulation of inventory. To address these challenges, production systems need to ensure adequate operational production planning and optimization of all variables that are influencing the productivity of their systems on a daily basis. To tackle the problem, this study elaborates the application of discrete event simulations and genetic algorithm, using the Tecnomatix Plant Simulation software, to support decision-making and operational production planning and optimization in the industry. The simulation model developed for this purpose considers: customers demand changes, variable production times, operationally available resources and production batch size, to provide an optimal production sequence with the highest number of produced pieces and the lowest total work in process (WIP) inventory per day. To demonstrate the efficiency of the methodology and prove the benefits of the selected optimization approach, a case study is conducted in the textile factory.
{"title":"Simulation and Genetic Algorithm-based approach for multi-objective optimization of production planning: A case study in industry","authors":"S. Bojic, M. Maslaric, D. Mircetic, S. Nikolicic, V. Todorovic","doi":"10.14743/apem2023.2.471","DOIUrl":"https://doi.org/10.14743/apem2023.2.471","url":null,"abstract":"To stay competitive on the constantly changing and demanding market, production systems need to optimize their performance daily. This is particularly challenging in labour-intensive industries, which is characterized by highly volatile customer demand and significant daily variability of available workers. The Uncertainty related to the key production parameters in the industry is causing disruptions in long-term production planning and optimization, which leads to the long lead production times, operational risks and accumulation of inventory. To address these challenges, production systems need to ensure adequate operational production planning and optimization of all variables that are influencing the productivity of their systems on a daily basis. To tackle the problem, this study elaborates the application of discrete event simulations and genetic algorithm, using the Tecnomatix Plant Simulation software, to support decision-making and operational production planning and optimization in the industry. The simulation model developed for this purpose considers: customers demand changes, variable production times, operationally available resources and production batch size, to provide an optimal production sequence with the highest number of produced pieces and the lowest total work in process (WIP) inventory per day. To demonstrate the efficiency of the methodology and prove the benefits of the selected optimization approach, a case study is conducted in the textile factory.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Q. Bi, M. Lai, K. Chen, J.M. Liu, H. Tang, X.B. Teng, Y.Y. Guo
In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.
{"title":"Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted","authors":"Q. Bi, M. Lai, K. Chen, J.M. Liu, H. Tang, X.B. Teng, Y.Y. Guo","doi":"10.14743/apem2023.1.456","DOIUrl":"https://doi.org/10.14743/apem2023.1.456","url":null,"abstract":"In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121104096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In response to the wide range of customer demands, the concept of reconfigurable manufacturing systems (RMS) was introduced in the industrial sector. RMS enables producers to meet varying volumes of demand over varying time periods by swiftly adjusting its production capacity and functionality within a part family in response to abrupt market changes. In these circumstances, RMS are made to swiftly reconfigure their Reconfigurable Machine Tools (RMTs). RMTs are designed to have a variety of configurations that may be conditionally chosen and reconfigured in accordance with specific performance goals. However, the reconfiguration process is not an easy process, which entails optimization of several objectives and many of which are inherently conflictual. As a result, it necessitates real-time monitoring of the RMS's condition, which may be achieved by digital twinning, or the real-time capture of system data. The concept of using a digital replica of a physical system to provide real-time optimization is known as digital twin. This work considered a case study of discrete parts manufacturing on a reconfigurable single manufacturing transfer line (SMTL). Six manufacturing operations are required to be performed on the parts at six production stages. This work uses the Digital Twin (DT) based approach to assist a discrete multi-objective optimization problem for a reconfigurable manufacturing transfer line. This multi-objective optimization problem consists of four objective functions which is illustrated by using DT-based Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The innovative aspect of the current study is the use of a DT-based framework for RMS reconfiguration to produce the best optimum solutions. The produced real-time solutions will be of great assistance to the decision maker in selecting the appropriate real-time optimal solutions for reconfigurable manufacturing transfer lines.
为了响应广泛的客户需求，可重构制造系统(RMS)的概念被引入工业领域。RMS使生产商能够满足不同时期的不同数量的需求，通过快速调整其生产能力和功能，以响应突然的市场变化。在这种情况下，RMS被要求快速重新配置其可重构机床(rmt)。rmt被设计成具有各种配置，这些配置可以根据特定的性能目标有条件地选择和重新配置。然而，重新配置过程并不是一个简单的过程，它需要优化几个目标，其中许多目标本身就是相互冲突的。因此，需要实时监测RMS的状态，这可以通过数字孪生或实时捕获系统数据来实现。使用物理系统的数字副本来提供实时优化的概念被称为数字孪生。本工作考虑了在可重构单制造传输线(SMTL)上离散零件制造的案例研究。需要在六个生产阶段对零件进行六个制造操作。这项工作使用基于数字孪生(DT)的方法来辅助可重构制造传输线的离散多目标优化问题。该多目标优化问题由四个目标函数组成，并利用基于dt的非支配排序遗传算法- ii (NSGA-II)进行了说明。当前研究的创新之处在于使用基于dt的RMS重构框架来产生最佳的最佳解决方案。生成的实时解决方案将极大地帮助决策者在可重构制造生产线中选择合适的实时最优方案。
{"title":"A NSGA-II based approach for multi-objective optimization of a reconfigurable manufacturing transfer line supported by Digital Twin: A case study","authors":"M. Ali, A. AlArjani, M. A. Mumtaz","doi":"10.14743/apem2023.1.461","DOIUrl":"https://doi.org/10.14743/apem2023.1.461","url":null,"abstract":"In response to the wide range of customer demands, the concept of reconfigurable manufacturing systems (RMS) was introduced in the industrial sector. RMS enables producers to meet varying volumes of demand over varying time periods by swiftly adjusting its production capacity and functionality within a part family in response to abrupt market changes. In these circumstances, RMS are made to swiftly reconfigure their Reconfigurable Machine Tools (RMTs). RMTs are designed to have a variety of configurations that may be conditionally chosen and reconfigured in accordance with specific performance goals. However, the reconfiguration process is not an easy process, which entails optimization of several objectives and many of which are inherently conflictual. As a result, it necessitates real-time monitoring of the RMS's condition, which may be achieved by digital twinning, or the real-time capture of system data. The concept of using a digital replica of a physical system to provide real-time optimization is known as digital twin. This work considered a case study of discrete parts manufacturing on a reconfigurable single manufacturing transfer line (SMTL). Six manufacturing operations are required to be performed on the parts at six production stages. This work uses the Digital Twin (DT) based approach to assist a discrete multi-objective optimization problem for a reconfigurable manufacturing transfer line. This multi-objective optimization problem consists of four objective functions which is illustrated by using DT-based Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The innovative aspect of the current study is the use of a DT-based framework for RMS reconfiguration to produce the best optimum solutions. The produced real-time solutions will be of great assistance to the decision maker in selecting the appropriate real-time optimal solutions for reconfigurable manufacturing transfer lines.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a global simulation optimization approach is developed to imitate and optimize the performance of the Pharmaceutical Supply Chain (PSC). Firstly, a hierarchical hybrid simulation model is developed in which aggregate and detailed data levels are addressed simultaneously. The model consists of two types of interdependent paradigms: the system dynamics paradigm, which depicts the echelons of pharmacies and wholesalers in the PSC, and the discrete event paradigm, which simulates the manufacturers with their detailed production operations, as well as the echelons of suppliers. Secondly, the "As is" scenario analysis and a screening process are performed to extract significant input parameters as well as sensitive outputs of the model. The final step optimizes the performance of PSC. The proposed approach validity is appraised by being applied to the PSC of a leading pharmaceutical company in Jordan. As a result, the opportunity loss cost has considerably decreased for both the manufacturer and wholesalers’ echelons and the service level has improved throughout the PSC.
{"title":"Hierarchical hybrid simulation optimization of the pharmaceutical supply chain","authors":"S. Altarazi, M. Shqair","doi":"10.14743/apem2023.1.457","DOIUrl":"https://doi.org/10.14743/apem2023.1.457","url":null,"abstract":"In this paper, a global simulation optimization approach is developed to imitate and optimize the performance of the Pharmaceutical Supply Chain (PSC). Firstly, a hierarchical hybrid simulation model is developed in which aggregate and detailed data levels are addressed simultaneously. The model consists of two types of interdependent paradigms: the system dynamics paradigm, which depicts the echelons of pharmacies and wholesalers in the PSC, and the discrete event paradigm, which simulates the manufacturers with their detailed production operations, as well as the echelons of suppliers. Secondly, the \"As is\" scenario analysis and a screening process are performed to extract significant input parameters as well as sensitive outputs of the model. The final step optimizes the performance of PSC. The proposed approach validity is appraised by being applied to the PSC of a leading pharmaceutical company in Jordan. As a result, the opportunity loss cost has considerably decreased for both the manufacturer and wholesalers’ echelons and the service level has improved throughout the PSC.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132296997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Improving quality, enhancing productivity, redesigning machining tools, eliminating waste in production, and shortening lead time are all objectives aimed at improving customer satisfaction and increasing profitability for manufacturing companies. This study combines lean manufacturing and six sigma techniques to form a technique called Lean Six Sigma (LSS) by using the DMAIC (Define-Measure-Analysis-Improve-Control) model. This study proposes to use statistical test models to analyze real data collected directly from the operator. The study proposes to use the Taguchi optimization technique to determine the optimal conditions for oil dipping tanks of molybdenum materials. In addition, the study also proposes a computer vision technique to recognize objects using color recognition techniques running on the LABVIEW software platform. This study builds a digital numerical control (DNC) model operating on digital signal processing techniques, linking the data of each process together. The results reduced the rate of defective parts in the whole processing stage from 6.5 % to zero defects, the whole processing line production capacity increased by 7.9 %, and the profit of the whole production line was USD 35762 per year. As a valuable external outcome, the conclusion of the LSS project fostered a spirit of continuous improvement. The utilization of research results from the research environment in the actual production setting is significantly enhanced for the operator. The LSS model is deployed with specific tasks and targets for each member of the LSS project team, and the processing conditions for each specific stage are optimized, such as the oil dipping process and hole grinding process. Industry 4.0 techniques, including computer vision, digital numerical control, and commercial software such as LabVIEW and MINITAB, are optimized for use, simplifying machining operations. Some proposed directions for future research are also presented in detail. For example, studying the improvement of the quality of the 220 V power supply through harmonic mitigation in processing factories is an intriguing area of investigation. Additionally, exploring data security for big data in the context of Industry 4.0 would be a valuable study to enhance customer satisfaction with big data technology in the future.
{"title":"Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0","authors":"M. Ly Duc, L. Hlavaty, P. Bilik, R. Martinek","doi":"10.14743/apem2023.1.455","DOIUrl":"https://doi.org/10.14743/apem2023.1.455","url":null,"abstract":"Improving quality, enhancing productivity, redesigning machining tools, eliminating waste in production, and shortening lead time are all objectives aimed at improving customer satisfaction and increasing profitability for manufacturing companies. This study combines lean manufacturing and six sigma techniques to form a technique called Lean Six Sigma (LSS) by using the DMAIC (Define-Measure-Analysis-Improve-Control) model. This study proposes to use statistical test models to analyze real data collected directly from the operator. The study proposes to use the Taguchi optimization technique to determine the optimal conditions for oil dipping tanks of molybdenum materials. In addition, the study also proposes a computer vision technique to recognize objects using color recognition techniques running on the LABVIEW software platform. This study builds a digital numerical control (DNC) model operating on digital signal processing techniques, linking the data of each process together. The results reduced the rate of defective parts in the whole processing stage from 6.5 % to zero defects, the whole processing line production capacity increased by 7.9 %, and the profit of the whole production line was USD 35762 per year. As a valuable external outcome, the conclusion of the LSS project fostered a spirit of continuous improvement. The utilization of research results from the research environment in the actual production setting is significantly enhanced for the operator. The LSS model is deployed with specific tasks and targets for each member of the LSS project team, and the processing conditions for each specific stage are optimized, such as the oil dipping process and hole grinding process. Industry 4.0 techniques, including computer vision, digital numerical control, and commercial software such as LabVIEW and MINITAB, are optimized for use, simplifying machining operations. Some proposed directions for future research are also presented in detail. For example, studying the improvement of the quality of the 220 V power supply through harmonic mitigation in processing factories is an intriguing area of investigation. Additionally, exploring data security for big data in the context of Industry 4.0 would be a valuable study to enhance customer satisfaction with big data technology in the future.","PeriodicalId":445710,"journal":{"name":"Advances in Production Engineering & Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124307605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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