Refined layout is a basis of warehousing efficiency. Straight aisle is a typical feature of current warehouse internal layouts. The purpose of this paper is to explore the possibility of using curve aisles for warehouse layout. By Choosing typical non-traditional layouts and transforming their inclined cross-aisle trajectory into parabola, two parabolic aisle layouts, parabolic Flying-V and parabolic Fishbone, are constructed. For unit-load warehouses, based on the morphological characteristic analysis and the parabolic types selection, the picking distance model and the cross-aisle length formula are presented. Interval Numerical Simulation Method (INSM) and Genetic Algorithms (GA) are adopted to solve the model respectively in order to verify the results. This research breaks through the realistic situation of straight aisle leading warehouse layout, and enriches the relevant layout theory. The calculation results of 100 warehouses with different sizes show that the picking distance of parabolic Flying-V could be reduced by 0.22-0.62 % compared with the straight layout, and the theoretical possible improvement space has been compressed by 2.42-12.26 %. Its length of cross-aisle is shortened by -0.03-3.10 %. The picking distance of parabolic Fishbone could be only reduced by 0.02-0.04 %. The theoretical possible improvement space has been compressed by 1.27-1.83 %. But its length of cross-aisle will increase by 4.63-19.50 % significantly. We believe that the layout of non-rectangular complex special-shaped warehouses based on curve trajectory aisles would become an important research topic. In addition, after some necessary modifications to the objectives and constraints, the proposed method in this paper may also be used for the arrangement of machines and devices in a workshop in principle.
{"title":"Designing a warehouse internal layout using a parabolic aisles based method","authors":"Z. Y. Zhang, Y. Liang, Y. Hou, Q. Wang","doi":"10.14743/apem2021.2.396","DOIUrl":"https://doi.org/10.14743/apem2021.2.396","url":null,"abstract":"Refined layout is a basis of warehousing efficiency. Straight aisle is a typical feature of current warehouse internal layouts. The purpose of this paper is to explore the possibility of using curve aisles for warehouse layout. By Choosing typical non-traditional layouts and transforming their inclined cross-aisle trajectory into parabola, two parabolic aisle layouts, parabolic Flying-V and parabolic Fishbone, are constructed. For unit-load warehouses, based on the morphological characteristic analysis and the parabolic types selection, the picking distance model and the cross-aisle length formula are presented. Interval Numerical Simulation Method (INSM) and Genetic Algorithms (GA) are adopted to solve the model respectively in order to verify the results. This research breaks through the realistic situation of straight aisle leading warehouse layout, and enriches the relevant layout theory. The calculation results of 100 warehouses with different sizes show that the picking distance of parabolic Flying-V could be reduced by 0.22-0.62 % compared with the straight layout, and the theoretical possible improvement space has been compressed by 2.42-12.26 %. Its length of cross-aisle is shortened by -0.03-3.10 %. The picking distance of parabolic Fishbone could be only reduced by 0.02-0.04 %. The theoretical possible improvement space has been compressed by 1.27-1.83 %. But its length of cross-aisle will increase by 4.63-19.50 % significantly. We believe that the layout of non-rectangular complex special-shaped warehouses based on curve trajectory aisles would become an important research topic. In addition, after some necessary modifications to the objectives and constraints, the proposed method in this paper may also be used for the arrangement of machines and devices in a workshop in principle.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"4 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83053817","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}
Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Among several hundred steel grades, 70MnVS4 steel is also produced. In the paper optimization of steelmaking of 70MnVS4 steel is presented. 70MnVS4 is a high-strength microalloyed steel which is used for forging of connecting rods in the automotive industry. During 70MnVS4 ladle treatment, the sulfur addition in the melt should be conducted only once. For several reasons the sulfur is repeatedly added and therefore threatening clogging during continuous casting and as such influencing surface defects occurrence and steel cleanliness. Accordingly, the additional sulfur addition was predicted using linear regression and genetic programming. Following parameters were collected within the period from January 2018 to December 2018 (78 consequently cast batches): sulfur and carbon cored wire addition after chemical analysis after tapping, carbon, manganese and sulfur content after tapping, time between chemical analysis after tapping and starting of the casting, ferromanganese and ferrosilicon addition and additional sulfur cored wire addition. Based on modelling results it was found out that the ferromanganese is the most influential parameter. Accordingly, 12 consequently cast batches (from February 2019 to October 2019) were produced with as lower as possible addition of ferromanganese. The additional sulfur addition in all 12 cases was not needed. Also, the melt processing time, surface quality of rolled material and sulfur cored wire consumption did not change statistically significantly after reduction of ferromanganese addition. The steel cleanliness was statistically significantly better.
{"title":"Modelling and optimization of sulfur addition during 70MnVS4 steelmaking: An industrial case study","authors":"M. Kovačič, B. Leser, M. Brezocnik","doi":"10.14743/apem2021.2.398","DOIUrl":"https://doi.org/10.14743/apem2021.2.398","url":null,"abstract":"Štore Steel Ltd. is one of the major flat spring steel producers in Europe. Among several hundred steel grades, 70MnVS4 steel is also produced. In the paper optimization of steelmaking of 70MnVS4 steel is presented. 70MnVS4 is a high-strength microalloyed steel which is used for forging of connecting rods in the automotive industry. During 70MnVS4 ladle treatment, the sulfur addition in the melt should be conducted only once. For several reasons the sulfur is repeatedly added and therefore threatening clogging during continuous casting and as such influencing surface defects occurrence and steel cleanliness. Accordingly, the additional sulfur addition was predicted using linear regression and genetic programming. Following parameters were collected within the period from January 2018 to December 2018 (78 consequently cast batches): sulfur and carbon cored wire addition after chemical analysis after tapping, carbon, manganese and sulfur content after tapping, time between chemical analysis after tapping and starting of the casting, ferromanganese and ferrosilicon addition and additional sulfur cored wire addition. Based on modelling results it was found out that the ferromanganese is the most influential parameter. Accordingly, 12 consequently cast batches (from February 2019 to October 2019) were produced with as lower as possible addition of ferromanganese. The additional sulfur addition in all 12 cases was not needed. Also, the melt processing time, surface quality of rolled material and sulfur cored wire consumption did not change statistically significantly after reduction of ferromanganese addition. The steel cleanliness was statistically significantly better.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"42 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72625889","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}
Change impact evaluation of complex product plays an important role in controlling change cost and improving change efficiency of engineering change enterprises. In order to improve the accuracy of engineering change impact evaluation, this paper introduces three-parameter interval grey number to evaluate complex products according to the data characteristics. The linear combination of BWM and Gini coefficient method is used to improve the three-parameter interval grey number correlation model. It is applied to the impact evaluation of complex product engineering change. This paper firstly constructs a multi-stage complex network for complex product engineering change. Then the engineering change impact evaluation index system is determined. Finally, a case analysis was carried out with the permanent magnet synchronous centrifugal compressor in a large permanent magnet synchronous centrifugal unit to verify the effectiveness of the proposed method.
{"title":"Change impact analysis of complex product using an improved three-parameter interval grey relation model","authors":"W. Yang, C. D. Li, Y. H. Chen, Y. Yu","doi":"10.14743/apem2021.2.393","DOIUrl":"https://doi.org/10.14743/apem2021.2.393","url":null,"abstract":"Change impact evaluation of complex product plays an important role in controlling change cost and improving change efficiency of engineering change enterprises. In order to improve the accuracy of engineering change impact evaluation, this paper introduces three-parameter interval grey number to evaluate complex products according to the data characteristics. The linear combination of BWM and Gini coefficient method is used to improve the three-parameter interval grey number correlation model. It is applied to the impact evaluation of complex product engineering change. This paper firstly constructs a multi-stage complex network for complex product engineering change. Then the engineering change impact evaluation index system is determined. Finally, a case analysis was carried out with the permanent magnet synchronous centrifugal compressor in a large permanent magnet synchronous centrifugal unit to verify the effectiveness of the proposed method.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"17 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82234754","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}
Bone drilling is a major stage in immobilization of the fracture site. During bone drilling operations, the temperature may exceed the allowable limit of 47 °C, causing irrecoverable damages of thermal necrosis and seriously threatening the fracture treatment. One of the parameters affecting the temperature rise of the drilling site is the frequency of applying the drill bit and its extent of wear. The present study attempted to mitigate the effect of drill bit wear on the bone temperature rise through the internal gas cooling method via CO2 and to reduce the risk of incidence of thermal necrosis. To this end, drilling tests were conducted at three rotational speeds 1000, 2000, and 3000 r·min-1 in two states of without cooling and with internal gas cooling by CO2 through an internal coolant carbide drill bit, along with six drill bit states (new, used 10, 20, 30, 40, and 50 times) on a bovine femur bone. The results indicated that in the internal gas cooling state, as the number of drill bit applications increased from the new state to more than 50 times, the temperature of the hole site increased on average by ΔT = 2-3 °C (n = 1000 r·min-1), ΔT = 5-8 °C (n = 2000 r·min-1), and ΔT = 5-7 °C (n = 3000 r·min-1). Furthermore, the internal gas cooling method was able to significantly reduce the effect of the drill bit wear on the temperature rise of the drilling site and to resolve the risk of incidence of thermal necrosis regardless of the process parameters for drilling operations.
{"title":"Bone drilling with internal gas cooling: Experimental and statistical investigation of the effect of cooling with CO2 on reduction of temperature rise due to drill bit wear","authors":"E. Shakouri, H. H. Hassanalideh, S. Fotuhi","doi":"10.14743/apem2021.2.394","DOIUrl":"https://doi.org/10.14743/apem2021.2.394","url":null,"abstract":"Bone drilling is a major stage in immobilization of the fracture site. During bone drilling operations, the temperature may exceed the allowable limit of 47 °C, causing irrecoverable damages of thermal necrosis and seriously threatening the fracture treatment. One of the parameters affecting the temperature rise of the drilling site is the frequency of applying the drill bit and its extent of wear. The present study attempted to mitigate the effect of drill bit wear on the bone temperature rise through the internal gas cooling method via CO2 and to reduce the risk of incidence of thermal necrosis. To this end, drilling tests were conducted at three rotational speeds 1000, 2000, and 3000 r·min-1 in two states of without cooling and with internal gas cooling by CO2 through an internal coolant carbide drill bit, along with six drill bit states (new, used 10, 20, 30, 40, and 50 times) on a bovine femur bone. The results indicated that in the internal gas cooling state, as the number of drill bit applications increased from the new state to more than 50 times, the temperature of the hole site increased on average by ΔT = 2-3 °C (n = 1000 r·min-1), ΔT = 5-8 °C (n = 2000 r·min-1), and ΔT = 5-7 °C (n = 3000 r·min-1). Furthermore, the internal gas cooling method was able to significantly reduce the effect of the drill bit wear on the temperature rise of the drilling site and to resolve the risk of incidence of thermal necrosis regardless of the process parameters for drilling operations.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"42 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77238794","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}
Advanced modeling and optimization techniques are imperative today to deal with complex machining processes like electric discharge machining (EDM). In the present research, Titanium alloy has been machined by considering different electrical input parameters to evaluate one of the important surface integrity (SI) parameter that is surface roughness Ra. Firstly, the response surface methodology (RSM) has been adopted for experimental design and for generating training data set. The artificial neural network (ANN) model has been developed and optimized for Ra with the same training data set. Finally, an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for Ra. Optimization of the developed ANFIS model has been done by applying the latest optimization techniques Rao algorithm and the Jaya algorithm. Different statistical parameters such as the mean square error (MSE), the mean absolute error (MAE), the root mean square error (RMSE), the mean bias error (MBE) and the mean absolute percentage error (MAPE) elucidate that the ANFIS model is better than the ANN model. Both the optimization algorithms results in considerable improvement in the SI of the machined surface. Comparing the Rao algorithm and Jaya algorithm for optimization, it has been found that the Rao algorithm performs better than the Jaya algorithm.
{"title":"Hybrid ANFIS-Rao algorithm for surface roughness modelling and optimization in electrical discharge machining","authors":"N. Agarwal, N. Shrivastava, Mohan K. Pradhan","doi":"10.14743/apem2021.2.390","DOIUrl":"https://doi.org/10.14743/apem2021.2.390","url":null,"abstract":"Advanced modeling and optimization techniques are imperative today to deal with complex machining processes like electric discharge machining (EDM). In the present research, Titanium alloy has been machined by considering different electrical input parameters to evaluate one of the important surface integrity (SI) parameter that is surface roughness Ra. Firstly, the response surface methodology (RSM) has been adopted for experimental design and for generating training data set. The artificial neural network (ANN) model has been developed and optimized for Ra with the same training data set. Finally, an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for Ra. Optimization of the developed ANFIS model has been done by applying the latest optimization techniques Rao algorithm and the Jaya algorithm. Different statistical parameters such as the mean square error (MSE), the mean absolute error (MAE), the root mean square error (RMSE), the mean bias error (MBE) and the mean absolute percentage error (MAPE) elucidate that the ANFIS model is better than the ANN model. Both the optimization algorithms results in considerable improvement in the SI of the machined surface. Comparing the Rao algorithm and Jaya algorithm for optimization, it has been found that the Rao algorithm performs better than the Jaya algorithm.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"99 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73448963","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}
Green closed-loop supply chain management is an important topic for business operations today because of increasing resource scarcity and environmental issues. Companies not only have to meet environmental regulations, but also must ensure high quality supply chain operation as a means to secure competitive advantages and increase profits. This study proposes a multi-objective mixed integer programming model for an integrated green closed-loop supply chain network designed to maximize profit, amicable production level (environmentally friendly materials and clean technology usage), and quality level. A scenario-based robust optimization method is used to deal with uncertain parameters such as the demand of new products, the return rates of returned products and the sale prices of remanufactured products. The proposed model is applied to a real industry case example of a manufacturing company to illustrate the applicability of the proposed model. The result shows a robust optimal resource allocation solution that considers multiple scenarios. This study can be a reference for closed-loop supply chain related academic research and also can be used to guide the development of a green closed-loop supply chain model for better decision making.
{"title":"A multi-objective optimal decision model for a green closed-loop supply chain under uncertainty: A real industrial case study","authors":"I. Fang, W.T. Lin","doi":"10.14743/apem2021.2.391","DOIUrl":"https://doi.org/10.14743/apem2021.2.391","url":null,"abstract":"Green closed-loop supply chain management is an important topic for business operations today because of increasing resource scarcity and environmental issues. Companies not only have to meet environmental regulations, but also must ensure high quality supply chain operation as a means to secure competitive advantages and increase profits. This study proposes a multi-objective mixed integer programming model for an integrated green closed-loop supply chain network designed to maximize profit, amicable production level (environmentally friendly materials and clean technology usage), and quality level. A scenario-based robust optimization method is used to deal with uncertain parameters such as the demand of new products, the return rates of returned products and the sale prices of remanufactured products. The proposed model is applied to a real industry case example of a manufacturing company to illustrate the applicability of the proposed model. The result shows a robust optimal resource allocation solution that considers multiple scenarios. This study can be a reference for closed-loop supply chain related academic research and also can be used to guide the development of a green closed-loop supply chain model for better decision making.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"68 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75858081","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}
Joint distribution means multiple clients were provided distribution services together by only one third-party logistics company. It is a unified plan and implementation used in distribution centres and a distribution activity implemented by multiple consortia. Many problems in distribution can be solved through the joint use of distribution warehouse, vehicles and reasonable logistics business, so as to optimize the overall logistics node and route arrangement. This paper mainly discusses the model of joint distribution of fast moving consumer goods, proposes three types of the optimization model of joint distribution system with Chaopi as an example. We draw the conclusion that Chaopi Trading Co., Ltd. is a joint distribution system optimization business model. This paper puts forward several basic distribution models and analyzes them in combination with practical applications, which has strong practical significance. Although the development of public distribution in China is not very fast, it is an inevitable trend. Through the efforts and explorations of the governments of various countries, there will be more and more choices of public distribution models.
{"title":"Joint distribution models in fast-moving consumer goods wholesale enterprise: Comparative analysis and a case study","authors":"L. Wang, X. Chen, H. Zhang","doi":"10.14743/apem2021.2.395","DOIUrl":"https://doi.org/10.14743/apem2021.2.395","url":null,"abstract":"Joint distribution means multiple clients were provided distribution services together by only one third-party logistics company. It is a unified plan and implementation used in distribution centres and a distribution activity implemented by multiple consortia. Many problems in distribution can be solved through the joint use of distribution warehouse, vehicles and reasonable logistics business, so as to optimize the overall logistics node and route arrangement. This paper mainly discusses the model of joint distribution of fast moving consumer goods, proposes three types of the optimization model of joint distribution system with Chaopi as an example. We draw the conclusion that Chaopi Trading Co., Ltd. is a joint distribution system optimization business model. This paper puts forward several basic distribution models and analyzes them in combination with practical applications, which has strong practical significance. Although the development of public distribution in China is not very fast, it is an inevitable trend. Through the efforts and explorations of the governments of various countries, there will be more and more choices of public distribution models.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"51 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79983388","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}
Improving customer satisfaction and shortening the manufacturing cycle have become common concerns of current manufacturers. This paper presents a multi‐objective location model considering the maximization of collaborative manufacturing capabilities and service benefits. This method first uses the two dimensions of customer share and market consumption to segment cus‐ tomers, and identify the weight of various customer groups. Secondly, the space vector model (VSM) is used to calculate the matching between manu‐ facturing capabilities and manufacturing requirements. Then build a multi‐ objective location model based on the two goals of collaborative manufactur‐ ing capabilities and service benefits. Finally, the model was tested with simu‐ lation data, which proved the validity and feasibility of the model. According to the simulation results, managers can accurately select the optimal manu‐ facturing base from multiple candidate manufacturing bases with regard to less costs, shorter lead times, better manufacturing capabilities, better service benefits. In this paper, Fuzzy theory, Logit model and VSM are combined to salve the problem of manufacturing base location. Considering resources and service benefits of each manufacturing base, it is helpful to optimize the loca‐ tion of enterprises. From the academic and practical points, this study pro‐ vides a new perspective for the location problem.
{"title":"Optimization of a multi-objective location model of manufacturing base considering cooperative manufacturing capabilities and service benefits","authors":"J. Sun, Q. Zhang, Y. Yu","doi":"10.14743/APEM2021.1.388","DOIUrl":"https://doi.org/10.14743/APEM2021.1.388","url":null,"abstract":"Improving customer satisfaction and shortening the manufacturing cycle have become common concerns of current manufacturers. This paper presents a multi‐objective location model considering the maximization of collaborative manufacturing capabilities and service benefits. This method first uses the two dimensions of customer share and market consumption to segment cus‐ tomers, and identify the weight of various customer groups. Secondly, the space vector model (VSM) is used to calculate the matching between manu‐ facturing capabilities and manufacturing requirements. Then build a multi‐ objective location model based on the two goals of collaborative manufactur‐ ing capabilities and service benefits. Finally, the model was tested with simu‐ lation data, which proved the validity and feasibility of the model. According to the simulation results, managers can accurately select the optimal manu‐ facturing base from multiple candidate manufacturing bases with regard to less costs, shorter lead times, better manufacturing capabilities, better service benefits. In this paper, Fuzzy theory, Logit model and VSM are combined to salve the problem of manufacturing base location. Considering resources and service benefits of each manufacturing base, it is helpful to optimize the loca‐ tion of enterprises. From the academic and practical points, this study pro‐ vides a new perspective for the location problem.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"28 1","pages":"112-124"},"PeriodicalIF":3.6,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86256454","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}
Nowadays, it is necessary to formulate and implement a development strate‐ gy in manufacturing enterprises, in line with the assumptions of the Industry 4.0 concept. In this context, a gap in the research has been observed in effec‐ tive management methods, in order to gain a competitive advantage through the implementation and use of Additive Manufacturing (AM) technologies. The main purpose of the study is to build a new approach to management, based on the implementation of new AM technologies and good practice. This paper uses the detailed literature studies and results from the empirical re‐ search of some 250 Polish manufacturing enterprises; this material contains a sample thereof, processed into a new approach. The major contributions of the work are as follows: (1) identification of current management areas in which manufacturing companies focus their activities, in the context of Indus‐ try 4.0, (2) the establishment of the correlation between gaining a competitive advantage and implementing AM technologies in the context of Industry 4.0, (3) Defining the so‐called AM4.0CARD as a new management approach, based on AM technologies and the requirements of Industry 4.0. Managers of manu‐ facturing enterprises, thanks to the use of the proposed approach, may take a strategic decision, regarding the implementation of AM technologies, due to the possibility of forecasting the impact of such an investment on the im‐ provement of the company's competitive advantage.
{"title":"A new management approach based on Additive Manufacturing technologies and Industry 4.0 requirements","authors":"J. Patalas-Maliszewska, M. Topczak","doi":"10.14743/APEM2021.1.389","DOIUrl":"https://doi.org/10.14743/APEM2021.1.389","url":null,"abstract":"Nowadays, it is necessary to formulate and implement a development strate‐ gy in manufacturing enterprises, in line with the assumptions of the Industry 4.0 concept. In this context, a gap in the research has been observed in effec‐ tive management methods, in order to gain a competitive advantage through the implementation and use of Additive Manufacturing (AM) technologies. The main purpose of the study is to build a new approach to management, based on the implementation of new AM technologies and good practice. This paper uses the detailed literature studies and results from the empirical re‐ search of some 250 Polish manufacturing enterprises; this material contains a sample thereof, processed into a new approach. The major contributions of the work are as follows: (1) identification of current management areas in which manufacturing companies focus their activities, in the context of Indus‐ try 4.0, (2) the establishment of the correlation between gaining a competitive advantage and implementing AM technologies in the context of Industry 4.0, (3) Defining the so‐called AM4.0CARD as a new management approach, based on AM technologies and the requirements of Industry 4.0. Managers of manu‐ facturing enterprises, thanks to the use of the proposed approach, may take a strategic decision, regarding the implementation of AM technologies, due to the possibility of forecasting the impact of such an investment on the im‐ provement of the company's competitive advantage.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"51 1","pages":"125-135"},"PeriodicalIF":3.6,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78291381","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}
Ideally, the solution to job-shop scheduling problem (JSP) should effectively reduce the cost of manpower and materials, thereby enhancing the core competitiveness of the manufacturer. Deep learning (DL) neural networks have certain advantages in handling complex dynamic JSPs with a massive amount of historical data. Therefore, this paper proposes a dynamic job-shop scheduling model based on DL. Firstly, a data prediction model was established for dynamic job-shop scheduling, with long short-term memory network (LSTM) as the basis; the Dropout technology and adaptive moment estimation (ADAM) were introduced to enhance the generalization ability and prediction effect of the model. Next, the dynamic JSP was described in details, and three objective functions, namely, maximum makespan, total device load, and key device load, were chosen for optimization. Finally, the multi-objective problem of dynamic JSP scheduling was solved by the improved multi-objective genetic algorithm (MOGA). The effectiveness of the algorithm was proved experimentally.
{"title":"A dynamic job-shop scheduling model based on deep learning","authors":"W. Tian, H. Zhang","doi":"10.14743/APEM2021.1.382","DOIUrl":"https://doi.org/10.14743/APEM2021.1.382","url":null,"abstract":"Ideally, the solution to job-shop scheduling problem (JSP) should effectively reduce the cost of manpower and materials, thereby enhancing the core competitiveness of the manufacturer. Deep learning (DL) neural networks have certain advantages in handling complex dynamic JSPs with a massive amount of historical data. Therefore, this paper proposes a dynamic job-shop scheduling model based on DL. Firstly, a data prediction model was established for dynamic job-shop scheduling, with long short-term memory network (LSTM) as the basis; the Dropout technology and adaptive moment estimation (ADAM) were introduced to enhance the generalization ability and prediction effect of the model. Next, the dynamic JSP was described in details, and three objective functions, namely, maximum makespan, total device load, and key device load, were chosen for optimization. Finally, the multi-objective problem of dynamic JSP scheduling was solved by the improved multi-objective genetic algorithm (MOGA). The effectiveness of the algorithm was proved experimentally.","PeriodicalId":48763,"journal":{"name":"Advances in Production Engineering & Management","volume":"63 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90154933","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}
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