Journal articles on the topic 'Enhanced production planning and scheduling method'
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Yadav, Deepika, R. Naresh, V. K. Sarda, Veena Sharma, and Paras Mani Goel. "Short Term Hydro Thermal Scheduling using Lambda Gamma Iteration Method." Hydro Nepal: Journal of Water, Energy and Environment 9 (November 20, 2012): 20–26. http://dx.doi.org/10.3126/hn.v9i0.7068.
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Abstract: Short term hydrothermal scheduling (STHTS) is a daily planning proposition in power system operation. The main purpose of hydro thermal generation scheduling is to minimize the overall operation cost and to satisfy the given constraints by optimally scheduling the power outputs of all hydro and thermal units under study periods, given electrical load and limited water resource. This paper presents the lambda-gamma iteration method for fixed head hydro-thermal problems. Also, hydro power input-output model with constant head has been developed for Sewa Hydro Electric Power Plant by using the curve fitting techniques by least square method in MATLAB 7.9 version software. From scheduling results, operational decisions can be made for the best performance under changing conditions of load, head, unit availability and other important constraints. Furthermore, these decisions may be used as an effective input to the on-line decision support system for real-time operation of reservoir systems in availability based tariff (ABT) context that results in increased power production and enhanced revenue earnings in the process of planning and management of a water resources project.DOI: http://dx.doi.org/10.3126/hn.v9i0.7068 Hydro Nepal Vol.9 July 2011 20-26
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Chen, Chen, Thomas Phang, and Lee Kong Tiong. "Planning semi-automated precast production using GA." International Journal of Industrialized Construction 1, no. 1 (July 27, 2020): 48–63. http://dx.doi.org/10.29173/ijic215.
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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.
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A, Sadhu Adwitya, M. Agung Wibowo, and Syafrudin Syafrudin. "ANALISA PERBEDAAN LPS (LAST PLANNER SYSTEM) DENGAN SISTEM KONVENSIONAL SERTA PENGARUH CPM DAN BAR CHART PADA LPS." Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil 25, no. 1 (June 1, 2020): 66. http://dx.doi.org/10.32497/wahanats.v25i1.1919.
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Two generally method / tools which used in project construction management system in Indonesia and the name are CPM (critical part method) or network planning and Bar Chart. The system used general method called conventional management system. Nowadays, was appeared one of a new system which seldom to applied in Indonesia, it is called LPS (last planner system). This research, researcher doing analyzed the differences between LPS and conventional management system. The result which obtained in this research are LPS dominated to accentuates team collaborative in the scheduling planning, resource allocation, control and enhance of work productivity, issue solved, regularly scheduling planning. Conventional management system more rely on the way CPM and Bar Chart working in that system with project manager decision. Planning of the schedule in this system doing by scheduling team. rescheduling in this system is not routine, it’s depend by construction progress. CPM and Bar Chart methods is influence on LPS. The both of methods was appeared in master scheduling, look a head scheduling, weekly work planning, shielding production, workable back log, percent plan completed and reliable promise process
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Liu, Lilan, Kai Guo, Zenggui Gao, Jiaying Li, and Jiachen Sun. "Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops." Sustainability 14, no. 9 (April 28, 2022): 5340. http://dx.doi.org/10.3390/su14095340.
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The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing.
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Huang, Rong-yau, and Kuo-Shun Sun. "An optimization model for workgroup-based repetitive scheduling." Canadian Journal of Civil Engineering 33, no. 9 (September 1, 2006): 1172–94. http://dx.doi.org/10.1139/l06-057.
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Most construction repetitive scheduling methods developed so far have been based on the premise that a repetitive project is comprised of many identical production units. Recently, Huang and Sun (2005) developed a workgroup-based repetitive scheduling method that takes the view that a repetitive construction project consists of repetitive activities of workgroups. Instead of repetitive production units, workgroups with repetitive or similar activities in a repetitive project are identified and employed in the planning and scheduling. The workgroup-based approach adds more flexibility to the planning and scheduling of repetitive construction projects and enhances the effectiveness of repetitive scheduling. This work builds on previous research and develops an optimization model for workgroup-based repetitive scheduling. A genetic algorithm (GA) is employed in model formation for finding the optimal or near-optimal solution. A chromosome representation, as well as specification of other parameters for GA analysis, is described in the paper. Two sample case studies, one simple and one sewer system project, are used for model validation and demonstration. Results and findings are reported.Key words: construction scheduling, repetitive project, workgroup, optimization, genetic algorithm.
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Kolomytseva, O. V., Т. І. Burceva, and І. V. Hanzhala. "Optimization of enterprise industrial activity project analysis." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 20, no. 86 (February 26, 2018): 88–91. http://dx.doi.org/10.15421/nvlvet8617.
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The article emphasizes the importance of the enterprise activity production management, the importance of new approaches to the production problems, the need to select such forms and management methods that enhance the efficient functioning of the enterprise. The complex system of production management, its input, output and internal parameters and conditions, the principles, methods and functions of management are described. It was emphasized on the importance of using project analysis in production management to identify, compare and justify alternative management decisions. The main components of management are considered, those are the basis of production activity project analysis optimization. It is indicated that such components are: resources, time and quality. So, the main goal of the project analysis of the enterprise's production activity is the management of resource efficiency, time management and quality. It has been shown that resource efficiency management is the implementation of certain types of work, in particular analysis as a method of production activity research, organization of decisions implementation, enterprises planning and control. The importance of works scheduling, controlling their execution schedules, and adjusting possibilities are emphasized. Planning of quality, quality assurance, quality control take an important place in the project analysis of company production activities. That is, ensuring the best possible use of resources, scheduling and controlling work scheduling, planning and quality control is the key to effective management of production activities. The importance of the information technologies introduction in the process of enterprise management, development and implementation of modern software for solving a basic managerial tasks complex of the industrial enterprise system are emphasized.
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Dai, Lili, He Lu, Dezheng Hua, Xinhua Liu, Hongming Chen, Adam Glowacz, Grzegorz Królczyk, and Zhixiong Li. "A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm." Sustainability 14, no. 18 (September 19, 2022): 11747. http://dx.doi.org/10.3390/su141811747.
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Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.
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do Amaral Burghi, Ana Carolina, Tobias Hirsch, and Robert Pitz-Paal. "Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems." Energies 13, no. 6 (March 23, 2020): 1517. http://dx.doi.org/10.3390/en13061517.
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Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.
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Jang, YeEun, Jae-Man Lee, and JeongWook Son. "Development and Application of an Integrated Management System for Off-Site Construction Projects." Buildings 12, no. 7 (July 21, 2022): 1063. http://dx.doi.org/10.3390/buildings12071063.
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The off-site construction (OSC) method has attracted the interest of experts to resolve productivity stagnation and lack of skilled workforce and to reduce greenhouse gas emissions in the construction industry. Due to the unique characteristics of OSC projects, wherein building elements are produced in a factory, transported, and installed in the field, a management approach that differs from the management techniques of previous construction projects is required. Accordingly, with this study, we examined the characteristics of OSC projects and derived key management items through literature review, case analysis, and expert meetings to develop an integrated management system for OSC projects (OSC-IMS). The proposed system, OSC-IMS, integrates the entire supply chain of the OSC project. It includes the following functions: drawing management, scheduling and planning, site installation planning, production planning, production monitoring, shipping and transportation, delivery and inspection, site installation monitoring, and progress payment management. To verify the applicability and effectiveness of OSC-IMS, it was implemented in four projects. The application of the system to the case studies demonstrated the improvements in work efficiency and accuracy and decreased waste time in every work step. The findings indicate that the system can enhance project performance. This study contributes to the identification of the features and key elements of OSC management such that these factors can be linked with managing system development. This work describes the overall effect of the proposed system on real projects.
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Suanpang, Pannee, Pitchaya Jamjuntr, Kittisak Jermsittiparsert, and Phuripoj Kaewyong. "Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm." Sustainability 14, no. 23 (December 6, 2022): 16293. http://dx.doi.org/10.3390/su142316293.
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The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.
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Liu, Hang, Yongcheng Wang, Shilin Nie, Yi Wang, and Yu Chen. "Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation." Sustainability 14, no. 15 (July 22, 2022): 9013. http://dx.doi.org/10.3390/su14159013.
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Micro-energy grids integrating multiple energy sources can realize the efficient use of renewable energy and accelerate the process of energy transition. However, due to the uncertainty of renewable energy, the stability and security of system operations should be taken into account with respect to multi-energy coupling economic operations. Thus, it is essential to make flexible capacity allocations in advance of the actual scheduling of production in the micro-energy grid. With this motivation, this paper constructs a three-stage scheduling model corresponding to the running stage of the spot market. Specifically, the capacity of flexible, active devices is configured in the day-ahead stage; then, the intraday economic operation dispatching scheme is provided according to the capacity configuration. Based on the day-ahead and intraday optimization results, the system power balance is realized through the dispatching process using the reserve capacity of flexible active devices for deviations generated in the real-time stage of renewable energy. For the uncertainty of renewable energy output, the clustering method is applied to realize the clustering analysis of renewable energy output scenarios. In addition, the conditional value at risk (CVaR) theory is introduced to modify the three-stage stochastic optimization model, and the risk values caused by uncertainty are quantitatively evaluated. Finally, we simulate a practical case to verify the effectiveness of the proposed model. The results show that day-ahead flexible capacity allocation enhances the autonomy of the micro-energy grid system, ensures a certain degree of system operational security, and reduces balancing costs in the real-time stage. The higher the risk aversion factor, the more operational costs the system operator pays to avoid the risk. In addition, if the carbon penalty coefficient is higher, the overall carbon emission level of the micro-energy grid will decrease, but it will gradually converge to a minimal level. This paper guides the development of micro-energy grids and has important constructional significance for the construction of multi-energy collaborative mechanisms.
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Yan, Haofang, Song Huang, Chuan Zhang, Miriam Coenders Gerrits, Guoqing Wang, Jianyun Zhang, Baoshan Zhao, Samuel Joe Acquah, Haimei Wu, and Hanwen Fu. "Parameterization and Application of Stanghellini Model for Estimating Greenhouse Cucumber Transpiration." Water 12, no. 2 (February 13, 2020): 517. http://dx.doi.org/10.3390/w12020517.
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Accurate estimation of transpiration (Tr) is important in the development of precise irrigation scheduling and to enhance water-use efficiency in agricultural production. In this study, the air temperature (Ta) and relative humidity (RH) were measured at three different heights (0.5, 1.0, and 1.8 m above the ground near the plant canopy) parameterize aerodynamic resistance (ra) based on the heat transfer coefficient method and to estimate Tr using the Stanghellini model (SM) during two growing seasons of cucumber in a greenhouse. The canopy resistance (rc) was parameterized by an exponential relationship of stomata resistance and solar radiation, and the estimated Tr was compared to the values measured with lysimeters. After parameterization of ra and rc, the efficiency (EF) and the Root Mean Square Error (RMSE) of the estimated Tr by the SM based on micrometeorological data at a height of 0.5 m were 95% and 18 W m−2, respectively, while the corresponding values were 86% and 29 W m−2 at a height of 1.8 m for the autumn planting season. For the spring planting season, the EF and RMSE were 92% and 34 W m−2 at a height of 0.5 m, while the corresponding values were 81% and 56 W m−2 at a height of 1.8 m, respectively. This work demonstrated that when micrometeorological data within the canopy was applied alongside the data measured above the canopy, the SM led to better agreement with the lysimeter measurements.
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Bhosale, Kailash Changdeorao, and Padmakar Jagannath Pawar. "Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities." Journal of Computational Design and Engineering 7, no. 6 (July 14, 2020): 761–74. http://dx.doi.org/10.1093/jcde/qwaa055.
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Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.
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Sunday, A. Afolalu, M. Ikumapayi Omolayo, E. Elewa Remilekun, Ademola Abdulkareem, E. Emetere Moses, O. Ongbali Samuel, and U. Iheanetu Olamma. "Impact of Problems Associated with Scheduling and Capacity Planning of a Production Process – An Overview." E3S Web of Conferences 309 (2021): 01003. http://dx.doi.org/10.1051/e3sconf/202130901003.
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Production is one of the most important activities which guarantee the continued existence of man; however, it comes with its challenges which make it very difficult to meet up the consumer’s demand. In this regard, the system is required by production and manufacturing companies, human resources, and materials to be enhanced by scheduling and planning of production. In addressing this problem of scheduling over a mid-term possibility, material flow and production objectives should be forecast by solving the problems of planning. Only when the production planning problems have been solved then scheduling problems could be addressed. In this work, we relate scheduling with capacity planning in relation to the production of goods and services. Also reviewed the common problems associated with the industry and how they are overcome.
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LIANG, HELAN, and SUJIAN LI. "CC-DHCR PLANNING AND SCHEDULING METHOD BASED ON SLAB CLUSTER." Journal of Advanced Manufacturing Systems 07, no. 02 (December 2008): 249–52. http://dx.doi.org/10.1142/s0219686708001437.
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Focusing on the limitations of the traditional Continuous Casting-Direct Hot Charge Rolling (CC-DHCR) planning and scheduling methods that rarely consider dynamic scheduling problems, a new method is put forward. The key idea is to make out clusters and integrated plans in the planning layer, and then to adjust the rolling sequences according to the slab cluster-based strategy in the dynamic scheduling layer. Results of the test with data from practical production process show that the method can effectively solve the CC-DHCR planning and scheduling problem and increase the DHCR ratio.
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Wang, Zhong, Gang Zhang, and Guo Wei Zhao. "The Advanced Production Planning of Automated Scheduling Problems in Irradiation Industry." Advanced Materials Research 139-141 (October 2010): 1688–91. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1688.
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Because of the continuity and discreteness during production, Irradiation industry becomes one of an especial industry. Various product type, uncertain lots and complex processes cycle increase the difficulty of production planning and scheduling. The implementation of CIMS/ERP systems during product processes in the enterprise can not fulfill the special requirements especially in product automation monitoring and quality information monitoring. Research and application of scheduling method and optimization technology effect crucially for enterprise to improve its production efficiency and reduce its production cost. So, more and more scholars pay their attention to this research field. For this reason, the paper presents a mathematical production scheduling model in irradiation workshop based on the research of irradiation industry development and shop scheduling in China and abroad. The author also design an application of the irradiation shop scheduling Optimization with this model in detailed, based on described the concept, principle of genetic algorithm and its method.
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De Antón, J., J. Senovilla, J. M. González, and F. Acebes. "Production planning in 3D printing factories." International Journal of Production Management and Engineering 8, no. 2 (July 18, 2020): 75. http://dx.doi.org/10.4995/ijpme.2020.12944.
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<p>Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this technology. In this work, we address the problem for the suppliers that have to schedule their daily production. This problem is part of the LONJA3D model, a managed 3D printing market where the parts ordered by the customers are reorganized into new batches so that suppliers can optimize their production capacity. In this paper, we propose a method derived from the design of combinatorial auctions to solve the nesting problem in 3D printing. First, we propose the use of a heuristic to create potential manufacturing batches. Then, we compute the expected return for each batch. The selected batch should generate the highest income. Several experiments have been tested to validate the process. This method is a first approach to the planning problem in 3D printing and further research is proposed to improve the procedure.</p>
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Chen, Jian Qing. "Research on Production Scheduling Method and the Application in Switch Machine Production Enterprise." Applied Mechanics and Materials 190-191 (July 2012): 156–59. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.156.
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This paper is under the research background of a switch machine production enterprise informatization projects, and the production schedule is mainly based on customer orders and sales forecasts. This paper mainly studies the combination of similar order processing sheets according to the similarity of types and specifications of products in an order processing sheet, and the experience of master production scheduling personnel, to generate the master production scheduling methods and techniques. Finally, studies the material requirements planning methods based on nested components, focusing on the configuration of parts and components of such products in the product configuration.
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Abikoye, B. O., and P. E. Amiolemhen. "Development of a Production Planning Method for the NIFOR Digester Screw Press." Advanced Materials Research 824 (September 2013): 499–504. http://dx.doi.org/10.4028/www.scientific.net/amr.824.499.
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In most production environments, the problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to design a work plan that makes effective use of the available resources because job scheduling in manufacturing is at the crux of production planning because of its impact on revenues. This work examined the production planning and scheduling of jobs at the Nigerian Institute for Oil Palm Research (NIFOR) with the aim of developing an effective production planning method for the production of one of its products the Digester Screw Press (DSP). The material flow pattern and current manufacturing practice were observed and production data was collected on machine set up and run times. The data collected was used to estimate the actual average run times for all the machines and the total processing times for each of the three components that make up the Digester Screw Press. The Microsoft Project 2003 was used as a tool in producing an improved job schedule based on the shortest processing time rule. The technique has reduced the general processing time for the production of DSP from 157hours to 99hours. It provides low machine waiting time, high machine utilization and reduced job tardiness across the shop floor.
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Zheng, Song, Jiaxin Gao, and Jian Xu. "Research on production planning and scheduling based on improved collaborative optimization." Concurrent Engineering 27, no. 2 (April 22, 2019): 99–111. http://dx.doi.org/10.1177/1063293x19842253.
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The production planning is aimed at the formulation and distribution of the overall production plan, while the production scheduling focuses on the implementation of the specific production plan. It is very important to coordinate each other in order to promote the production efficiency of enterprises, but the integrated optimization of production planning and scheduling has great challenges. This article proposes the novel integrated optimization method of planning and scheduling based on improved collaborative optimization. An integrated model of planning and scheduling with collaborative optimization structure is established, and the detailed solution strategy of the novel integrated optimization algorithm is presented. At last, the simulation results show that the proposed integration algorithm of planning and scheduling is competitive in global optimization and practicality.
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Kalinowski, Krzysztof, Cezary Grabowik, Grzegorz Ćwikła, and Witold Janik. "The Graph of Operations Planning Sequence of a Production Order for Scheduling with Mixed Planning Strategies and Alternatives." Applied Mechanics and Materials 809-810 (November 2015): 1420–25. http://dx.doi.org/10.4028/www.scientific.net/amm.809-810.1420.
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The paper presents the most important issues related to the scheduling of production orders in real manufacturing systems. In the elaborated method an and/or type graph of operations planning sequence of a production order is proposed for modelling the production system load. In a single structure the graph takes into account alternative routes of a production order realisation and the precedence constraints in presence of complex, hierarchical structures of processes. Two modelling ways of that process using the "operation on the edge" or "operation on the node" notation are also presented. In the developed method scheduling strategies, which have a major impact on the order of placing operations in the schedule and handling of production lots are also considered. By a state space graph representation of scheduling problem, using graph theory, it can be possible to analyze the structure and complexity of both the modelling problem and the graph search techniques.
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Zhu, Bao Lin, and Shou Feng Ji. "Steelmaking-Hot Rolling Scheduling Model and Method for Integrated Management in Iron and Steel Enterprises." Advanced Materials Research 860-863 (December 2013): 3094–99. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.3094.
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Iron and steel production scheduling problems are different from general production scheduling in machine industry. They have to meet special demands of steel production process. The CCR production manner dramatically promotes the revolution in technology and management, especially to planning and scheduling. In this paper, a scheduling model is presented to integrate the three working procedures and the lagrangian relaxation technology is proposed to get the optimal solution of the scheduling model. Finally, numerical examples are given to demonstrate the effectiveness of the integrated model and method.
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Zein Vitadiar, Tanhella, Farikhin Farikhin, and Bayu Surarso. "Production Planning and Planting Pattern Scheduling Information System for Horticulture." E3S Web of Conferences 31 (2018): 10004. http://dx.doi.org/10.1051/e3sconf/20183110004.
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This paper present the production of planning and planting pattern scheduling faced by horticulture farmer using two methods. Fuzzy time series method use to predict demand on based on sales amount, while linear programming is used to assist horticulture farmers in making production planning decisions and determining the schedule of cropping patterns in accordance with demand predictions of the fuzzy time series method, variable use in this paper is size of areas, production advantage, amount of seeds and age of the plants. This research result production planning and planting patterns scheduling information system with the output is recommendations planting schedule, harvest schedule and the number of seeds will be plant.
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Charoenlarpkul, Nattapoom, Eiji Morinaga, Hidefumi Wakamatsu, and Eiji Arai. "A Method of Computer-Aided Process Planning for Machining Considering Production Scheduling." Proceedings of Manufacturing Systems Division Conference 2016 (2016): 507. http://dx.doi.org/10.1299/jsmemsd.2016.507.
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HALEVI, GIDEON. "PRODUCTION MANAGEMENT — SIMPLE OR COMPLEX." International Journal of Innovation and Technology Management 01, no. 04 (December 2004): 359–71. http://dx.doi.org/10.1142/s0219877004000313.
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Theoretical production planning and scheduling is actually very simple task: The plant gets orders which defines the product, the quantity and delivery dates. The resources of the plants are known, the product bill of material is known and the task of production scheduling is to make sure that the orders will be ready on time, that's all. It seems strange that in order to meet this simple task, over 100 complex production planning methods were proposed. Some of the outstanding ones are: PICS; MRP; ERP; GT; TOC; FMS; IMS; CIM; CE; JIT; Kanaban; TQM; Agent…, AGILE etc. Yet the search for "THE" method is carried on. In this paper an attempt to analyze why production planning is regarded as a complex task, and why the search for "THE" production planning method is still an open topic for researchers. Furthermore, to demonstrate how introduction of flexibility will restore the simplicity of production planning.
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Jie, Yanliang, Lei Hao, Shujun Yan, and Xueli Zhang. "Optimization Method of High-Speed Train Composite Material Workshop Planning and Scheduling." Advances in Materials Science and Engineering 2022 (July 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/7868974.
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With the continuous speed increase of high-speed trains, higher performance requirements are put forward for the braking technology and braking device. In order to improve productivity and overall competitiveness, manufacturing companies are actively researching new manufacturing technologies and production methods, and shop floor scheduling is one of the core components of this problem. This paper mainly studies the high-speed train composite workshop planning and scheduling optimization method. This experiment adopts B/S-based architecture mode. The prototype system is developed with Microsoft’s integrated development platform Visual Studio 2013, and Microsoft’s SQL Server 2008 is used as the background database management system. The experiment mainly uses the white box test method; the test content mainly includes module interface test, module local data structure test, and module boundary test. The interface parameters of each module are checked, and the boundary values of some functions are also analyzed and tested. According to the results, the planning management personnel revise the priority order again until all the molds meet the requirements of delivery and constraints. If the scheduled results do not meet the requirements, the methods such as those compressing the lead time, those urging the casting to be in place in advance, single process outsourcing, and overtime shall be considered. In this paper, a layered coding strategy is adopted. The first layer of coding represents the batch processing sequence. The second layer of coding determines to which process the corresponding batch belongs to. Each layer of coding is divided into different machine segments to represent the batch processing sequence on different machines. When the production process needs to switch orders, it can know which equipment parameters need to be adjusted in advance, which can effectively avoid the wrong operation caused by temporary adjustment of production parameters, reduce the order switching time, and improve the utilization rate of the production line. The data show that, compared with the artificial experience method and the priority rule method, the order production cycle after genetic optimization is reduced by 7.34% and 8.98%, respectively. The results show that the workshop scheduling optimization can help enterprises save stamping scheduling time, reduce production costs, and improve the rationality of scheduling.
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Liu, Yueyue, Xiaoya Liao, and Rui Zhang. "An Enhanced MOPSO Algorithm for Energy-Efficient Single-Machine Production Scheduling." Sustainability 11, no. 19 (September 28, 2019): 5381. http://dx.doi.org/10.3390/su11195381.
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In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.
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Zhengmin, Zhang, Guan Zailin, Yue Lei, Wang Chuangjian, and Wang Hao. "A Production Planning and Scheduling Method Based on Heuristic Rules for Forming-sintering Production System." IOP Conference Series: Materials Science and Engineering 565 (June 26, 2019): 012001. http://dx.doi.org/10.1088/1757-899x/565/1/012001.
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Kong, Tengqiao, Dong Ren Nhg, and Van Tinh Shi. "Method for Optimizing Energy Consumption in Machining Manufacturing Process." Mathematical Problems in Engineering 2022 (May 23, 2022): 1–8. http://dx.doi.org/10.1155/2022/8300666.
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With the rapid development of the economy and the continuous maturity of various industries and departments, the total amount of energy required by enterprises in the process of production and operation is also increasing year by year. To achieve sustainable development, it is necessary to adjust the current production and operation mode, reasonably evaluate the current state of resources, use advanced technology to optimize each link of production, and regard low-energy consumption and low carbon as the key to production improvement. In this paper, the integrated realization method of process planning and production scheduling based on intelligent algorithm is adopted, and the integrated model is solved by using the chromosome hierarchical coding genetic algorithm. When the alternative process schemes generated for each part through nonlinear process planning are given, the integrated model can weigh the optimization objectives and decide the suitable process route, machine tool selection scheme, and corresponding production scheduling scheme for each part. In this paper, a case study on the energy-saving effect of energy consumption optimization methods in machining and manufacturing processes is carried out. Taking a manufacturing enterprise as the background, the effectiveness of the proposed energy-saving method is verified by comparing the energy consumption of a batch of mechanical product parts in the process planning and production scheduling integrated mode and the traditional serial working mode.
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Zheng, Jianguo, Xiaomei Mi, and Huchang Liao. "Advanced Planning and Scheduling Based on the Constraint Theory and Improved Tabu Search Algorithm." Recent Patents on Engineering 14, no. 2 (October 29, 2020): 221–28. http://dx.doi.org/10.2174/1872212113666191119103505.
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Background: Planning and scheduling of manufacturing enterprises are required to be rapid and accurate, which make is easy for the traditional enterprise resource planning system to meet the needs of enterprises. Objective: This paper introduces the theory of constraints and explains the control methods for production planning and scheduling. Methods: The main characteristics of the advanced planning and scheduling system are analyzed. Then, the modeling method based on the theory of constraints is used to study the scheduling path. Results: An example is given to validate the scheduling model and realize the optimization of production tasks on the bottleneck resources. Conclusion: The feasibility of the advanced planning and scheduling methods based on the theory of constraints is proved.
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Permana, Sidik, Meri Andriani, and Dewiyana Dewiyana. "Production Capacity Requirements Planning Using The Capacity Method Requirement Planning." International Journal of Engineering, Science and Information Technology 1, no. 4 (November 5, 2021): 36–40. http://dx.doi.org/10.52088/ijesty.v1i4.165.
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PT. ABC is a company engaged in the manufacture of Dolomite Fertilizer. The number of requests is greater than the amount of production. Due to fluctuations in the number of requests that tend to increase, this occurs due to a lack of capacity at the workstation. Therefore, it is necessary to calculate the capacity requirements planning analysis for each work station to know the company's capacity needs. The research aims to identify the shortage/excess production capacity and provide proposals for the balance of production capacity at PT. A B C. Production capacity research was conducted using the Capacity Requirement Planning (CRP) method. Results and Discussion, comparison of available capacity and required capacity (load) are as follows, work station jaw crusher available capacity 36.74 hours/week while required capacity (load) 36 hours/week, work station bucket elevator available capacity 36, 74 hours/week while the required capacity (load) is 14.4 hours/week, work station ball mill available capacity is 36.74 hours/week while the required capacity (load) is 45.6 hours/week, and work station silo flour the available capacity is 36.74 hours/week while the required capacity (load) is 51.59 hours/week. In conclusion, two stations experience excess capacity, namely the jaw crusher work station with an excess capacity of 0.74 hours/week and the bucket elevator work station with an excess capacity of 22.34 hours/week. The other two work stations experienced a lack of capacity, namely the ball mill work station with a capacity shortage of 22.34 hours/week and the silo flour work station with a capacity shortage of 14.85 hours/week. Efforts to balance capacity by scheduling overtime and adding equipment (machinery) to work centers that lack capacity, so that the company's production targets are achieved.
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Hassani, Zineb Ibn Majdoub, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi, and Abdel Moumen Darcherif. "New Approach to Integrate Planning and Scheduling of Production System: Heuristic Resolution." International Journal of Engineering Research in Africa 39 (November 2018): 156–69. http://dx.doi.org/10.4028/www.scientific.net/jera.39.156.
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In general, planning and scheduling of production are treated separately under the hierarchical strategy. Then, over the time, the iterative strategy appeared which partially considers the scheduling constraints during planning, except that the latter remains unsatisfactory because there is no guarantee that these constraints are taken into account. For this, is born the integrated strategy which integrates planning and scheduling and aims to solve the problem and define a feasible production plan. Since capacity constraints don’t reflect reality in terms of resource availability, and they are not always considered, capacity becomes aggregated. To remedy this problem, it is necessary to integrate more precise constraints of scheduling at the planning level. Based on this observation, we propose in this article a new model that integrates planning and scheduling and considers the constraint of resource availability. In our model, the objective function optimizes the total cost of production for a mono-level job-shop problem. To solve this N-P difficult problem we use a stochastic approached method as genetic algorithm (GA).
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Liu, Ying, Jun Zhao, and Wei Wang. "An optimal integrated production planning and scheduling method for bell-type anneal shop." IFAC Proceedings Volumes 42, no. 4 (2009): 804–9. http://dx.doi.org/10.3182/20090603-3-ru-2001.0083.
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Zhang, Hao, Fansheng Meng, Jia Xu, Zhandong Liu, and Jun Meng. "Evaluation of Machine Learning Models for Daily Reference Evapotranspiration Modeling Using Limited Meteorological Data in Eastern Inner Mongolia, North China." Water 14, no. 18 (September 16, 2022): 2890. http://dx.doi.org/10.3390/w14182890.
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Background: Water shortages limit agricultural production in arid and semiarid regions around the world. The accurate estimation of reference evapotranspiration (ET0) is of the utmost importance for computing crop water requirements, agricultural water management, and irrigation scheduling design. However, due to the combination of insufficient meteorological data and uncertain inputs, the accuracy and stability of the ET0 prediction model were different to varying degrees. Methods: Six machine learning models were proposed in the current study for daily ET0 estimation. Information on the weather, such as the maximum and minimum air temperatures, solar radiation, relative humidity, and wind speed, during the period 1960~2019 was obtained from eighteen stations in the northeast of Inner Mongolia, China. Three input combinations were utilized to train and test the proposed models and compared with the corresponding empirical equations, including two temperature-based, three radiation-based, and two humidity-based empirical equations. To evaluate the ET0 estimation models, two strategies were used: (1) in each weather station, we trained and tested the proposed machine learning model, and then compared it with the empirical equations, and (2) using the K-means algorithm, all weather stations were sorted into three groups based on their average climatic features. Then, each station tested the machine learning model trained using the other stations within the group. Three statistical indicators, namely, determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE), were used to evaluate the performance of the models. Results: The results showed the following: (1) The temperature-based temporal convolutional neural network (TCN) model outperformed the empirical equations in the first strategy, as shown by the TCN’s R2 values being 0.091, 0.050, and 0.061 higher than those of empirical equations; the RMSE of the TCN being significantly lower than that of empirical equations by 0.224, 0.135, and 0.159 mm/d; and the MAE of the TCN being significantly lower than that of empirical equations by 0.208, 0.151, and 0.097 mm/d, and compared with the temperature-based empirical equations, the TCN model markedly reduced RMSE and MAE while increasing R2 in the second strategy. (2) In comparison to the radiation-based empirical equations, all machine learning models reduced RMSE and MAE, while significantly increasing R2 in both strategies, particularly the TCN model. (3) In addition, in both strategies, all machine learning models, particularly the TCN model, enhanced R2 and reduced RMSE and MAE significantly when compared to humidity-based empirical equations. Conclusions: When the radiation or humidity characteristics were added to the given temperature characteristics, all the proposed machine learning models could estimate ET0, and its accuracy was higher than the calibrated empirical equations external to the training study area, which makes it possible to develop an ET0 estimation model for cross-station data with similar meteorological characteristics to obtain a satisfactory ET0 estimation for the target station.
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Russkikh, P. A., O. V. Drozd, and D. V. Kapulin. "The Method of Synchronous Scheduling of Multi-Product Make-to-Order Production." Informacionnye Tehnologii 29, no. 1 (January 19, 2023): 39–46. http://dx.doi.org/10.17587/it.29.39-46.
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Make-to-order production needs flexibility, quality and synchronicity of planning. Numerous types of products and complex control parameters lead to high requirements for safety, stability and continuity of the production process, as well as strict requirements for instant production management. The need for a high reaction rate to changes in the production system, adaptability and traceability, must be ensured by methods of accurate and reliable control and management of production. The most difficulty is the stage of making a schedule for make-to-order multi-nomenclature productions. Existing systems of scheduling are often not connected in real time with the current production process, all this leads to a high level of unfinished orders. It is necessary to take a new look at the existing planning systems and search for an approach with the possibility of analyzing and optimizing the operational and production plan, taking into account the actual implementation of the provided production process. The research is aimed at developing a synchronous scheduling method suitable for a multi-nomenclature make-to-order production with a variable number of products, and designed to reduce order fulfillment time, reduce inventory and improve work efficiency by adapting to fluctuations in production and product life cycle and implementing an optimal production plan.
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Tallgren, Mikael Viklund, Mattias Roupé, and Mikael Johansson. "4D modelling using virtual collaborative planning and scheduling." Journal of Information Technology in Construction 26 (November 4, 2021): 763–82. http://dx.doi.org/10.36680/j.itcon.2021.042.
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During the last decades, the introduction of Building Information Models (BIM) has opened new possibilities to ensure better communication and a shared understanding between stakeholders in construction projects. Similar benefits have been found in 4D simulations of the schedule. While BIM and 4D use has seen a steady increase, the potential benefits have not fully been realised, primarily due to tools not supporting and enabling the full potential of collaborative stakeholder involvement in scheduling. The benefits of 4D simulations come from improving communication between stakeholders. While traditional 4D modelling connects an existing BIM model with an existing schedule, a move from this type of passive 4D visualisation toward social co-creation enables all stakeholders to be involved in the scheduling process. This connection is further enhanced with the use of Virtual Reality (VR). Recent research has shown VR to enhance understanding and perception of the space and details and, thus, a better understanding of the project. This paper maps collaborative planning and scheduling method and traditional 4D scheduling using process modelling. This mapping is followed by implementing the 4D collaborative planning and scheduling method in the virtual project planning system with support for a multi-user interactive VR environment. The system enables social co-creation both during the planning and scheduling and during 4D modelling; it improves understanding of the project and the planning and scheduling.
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Khazaei, Soroush, and Yashar Pourrahimian. "Mathematical Programming Application in Sublevel Caving Production Scheduling." Mining 1, no. 2 (August 9, 2021): 180–91. http://dx.doi.org/10.3390/mining1020012.
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Production scheduling determines the most beneficial mining sequence over the life of a mine. Developing a schedule that meets all mining aspects can substantially reduce mining costs and increase profitability. Among all underground mining methods, the sublevel caving method is a common method with moderate development requirements, a high production rate, and a high degree of mechanization and flexibility. None of the manual planning methods and heuristic algorithms used in commercial software will lead to a truly optimal schedule. Mathematical programming models, particularly mixed-integer programming (MIP), have been applied to provide an operationally feasible multi-time-period schedule in sublevel caving. However, confined blasting conditions, chaotic material flow, and frequent mixing of ore and waste while loading broken ore at the drawpoint make the sublevel caving method unique when producing a holistic plan. This paper reviews all mathematical programming models presented in sublevel caving production scheduling, highlights the inherent characteristics of the sublevel caving that affect production, and puts forward some promising ideas for future works.
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Ibn Majdoub Hassani, Zineb, Abdellah El Barkany, Abdelouahhab Jabri, Ikram El Abbassi, and Abdel Moumen Darcherif. "Hybrid approach for solving the integrated planning and scheduling production problem." Journal of Engineering, Design and Technology 18, no. 1 (August 28, 2019): 172–89. http://dx.doi.org/10.1108/jedt-11-2018-0198.
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Purpose This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs. Design/methodology/approach The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes. Findings The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing. Originality/value This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.
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Graves, Robert J., Leon F. McGinnis, and Rodney A. Robinson. "Shipyard Production Standards." Journal of Ship Production 4, no. 01 (February 1, 1988): 65–69. http://dx.doi.org/10.5957/jsp.1988.4.1.65.
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Almost every shipyard is involved with labor standards, either as part of an incentive program or for work method analysis. Far less common is the application of standard data as an integral part of production planning and control. This paper addresses the higher-level standards application, and focuses on standards for production scheduling. The presentation is based on the authors' involvement in a number of projects in several shipyards.
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Dorn, Jürgen. "Business Service Scheduling." International Journal of Applied Logistics 1, no. 4 (October 2010): 52–63. http://dx.doi.org/10.4018/jal.2010100104.
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The management and predictive planning of the processes to create business services is more difficult than the planning of production processes, because services cannot be produced in stock and customers are involved in their creation. In this paper, the author proposes a method for service scheduling and optimization based on an ontology to describe business services and related concepts. The author schedules operations required to create a service. With each service process and its operations, soft and hard constraints on the execution of operations and the required resources are posted. These constraints are derived from service level agreements. A legal plan must then satisfy all hard constraints. All soft constraints are matter of optimization. Using a tabu search, a near-optimal solution of the service scheduling problem is achieved.
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Zheng, Hongyu, Juan Gao, Juxia Xiong, Guanglei Yao, Hongjiang Cui, and Lirong Zhang. "An Enhanced Artificial Electric Field Algorithm with Sine Cosine Mechanism for Logistics Distribution Vehicle Routing." Applied Sciences 12, no. 12 (June 19, 2022): 6240. http://dx.doi.org/10.3390/app12126240.
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Aiming at the scheduling problem of logistics distribution vehicles, an enhanced artificial electric field algorithm (SC-AEFA) based on the sine cosine mechanism is proposed. The development of the SC-AEFA was as follows. First, a map grid model for enterprise logistics distribution vehicle path planning was established. Then, an enhanced artificial electric field algorithm with the sine cosine mechanism was developed to simulate the logistics distribution vehicle scheduling, establish the logistics distribution vehicle movement law model, and plan the logistics distribution vehicle scheduling path. Finally, a distribution business named fresh enterprise A in the Fuzhou Strait Agricultural and Sideline Products Trading Market was selected to test the effectiveness of the method proposed. The theoretical proof and simulation test results show that the SC-AEFA has a good optimization ability and a strong path planning ability for enterprise logistics vehicle scheduling, which can improve the scheduling ability and efficiency of logistics distribution vehicles and save transportation costs.
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Jiang, Nan-Yun, and Hong-Sen Yan. "Integrated optimization of production planning and scheduling in uncertain re-entrance environment for fixed-position assembly workshops." Journal of Intelligent & Fuzzy Systems 42, no. 3 (February 2, 2022): 1705–22. http://dx.doi.org/10.3233/jifs-211159.
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For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.
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Duda, J., and A. Stawowy. "Application of Interval Arithmetic to Production Planning in a Foundry." Archives of Foundry Engineering 17, no. 1 (March 1, 2017): 41–44. http://dx.doi.org/10.1515/afe-2017-0008.
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Abstract A novel approach for treating the uncertainty about the real levels of finished products during production planning and scheduling process is presented in the paper. Interval arithmetic is used to describe uncertainty concerning the production that was planned to cover potential defective products, but meets customer’s quality requirement and can be delivered as fully valuable products. Interval lot sizing and scheduling model to solve this problem is proposed, then a dedicated version of genetic algorithm that is able to deal with interval arithmetic is used to solve the test problems taken from a real-world example described in the literature. The achieved results are compared with a standard approach in which no uncertainty about real production of valuable castings is considered. It has been shown that interval arithmetic can be a valuable method for modeling uncertainty, and proposed approach can provide more accurate information to the planners allowing them to take more tailored decisions.
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Ni, Yanting, and Yi Wang. "A double decoupling postponement approach for integrated mixed flow production systems." Kybernetes 44, no. 5 (May 5, 2015): 705–20. http://dx.doi.org/10.1108/k-10-2014-0229.
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Purpose – In a mixed flow production environment, interactions between production planning and scheduling are critical for mixed flow distributed manufacturing management. The purpose of this paper is to assist manufacturers in achieving real-time ordering and obtaining integrated optimization of shop floor production planning and scheduling for mixed flow production systems. Design/methodology/approach – A double decoupling postponement (DDP) approach is presented for production dispatch control, and an integrated model is designed under an assemble to order (ATO) environment. To generate “optimal” lots to fulfil real-time customer requests, constant work in progress (CONWIP) and days of inventory dispatching algorithms are embedded into the proposed DDP model, which can deal with real-time ordering and dynamic scheduling simultaneously. Subsequently, a case study is conducted, and experiments are carried out to verify the presented method. Findings – The proposed DDP model is designed to upgrade a previous CONWIP method in the case study company, and the proposed model demonstrates better performance for the integration of production planning and scheduling in mixed flow manufacturing. As a result, the presented operation mechanism can reflect real-time ordering information to shop floor scheduling and obtain performance metrics in terms of reliability, availability and maintainability. Research limitations/implications – The presented model can be further proliferated to generic factory manufacturing with the proposed logic and architecture. Originality/value – The DDP model can integrate real-time customer orders and work in process information, upon which manufacturers can make correct decisions for dispatch strategies and order selection within an integrated system.
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Monjezi, Nasim, Mohammad Javad Sheikhdavoodi, Hasan Zakidizaji, Afshin Marzban, and Mahmood Shomeili. "Operations Scheduling of Sugarcane Production Using Classical Gert Method (Part Ii: Preserve Operations, Harvesting and Ratooning)." Journal of Agricultural Studies 3, no. 2 (May 11, 2015): 85. http://dx.doi.org/10.5296/jas.v3i2.7421.
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Graphical Evaluation and Review Technique (GERT) is a systems analysis technique for project management. GERT provides a visual picture of the system and helps to analyse the system in a less inductive manner. Therefore, the purpose of this paper is studying the application of project scheduling in agriculture, for operations scheduling of sugarcane production (preserve operations, harvesting and rationing) using classical GERT method in Khuzestan province of Iran. Results showed that the network model was able to answer any statistic all the questions concerning the project. GERT networks are increasingly becoming a powerful tool for modelling, scheduling, planning, controlling and analysing of agricultural mechanization projects.
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Ni, Jun, Wen Cheng Tang, and Yan Xing. "Computer-Aided Production Control of Automobile Injection Molds Based on Process Perception." Advanced Materials Research 655-657 (January 2013): 1682–86. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1682.
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This paper focuses on major issues in production process control of automobile injection molds based on process perception, such as manufacture resource perception, exchange in three-dimensional model (3D), production planning and scheduling, manufacture process planning (tool path and assembly sequence). After a survey of them, we propose a method for the construction of computer-aided production process control platform. 1) Define the networked manufacture semantic/data model and an encapsulating approach to efficiently manipulate manufacturing resources. 2) Develop structural multi-view data models for the design information of automobile molds transferring in 3D. 3) Propose a production planning and scheduling arithmetic. 4) Develop a variation accumulation model for manufacturing process control. 5) Integrate the tool path and assembly sequence planning of injection molds by using a variation accumulation model. 6) Integrate the existing and/or developed models to the new system. Finally we introduce a validation method and discuss the prospect to the accurate control of production process.
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Mao, Xuezhang, Jinghua Li, Hui Guo, and Xiaoyuan Wu. "Research on Collaborative Planning and Symmetric Scheduling for Parallel Shipbuilding Projects in the Open Distributed Manufacturing Environment." Symmetry 12, no. 1 (January 13, 2020): 161. http://dx.doi.org/10.3390/sym12010161.
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In the current distributed manufacturing environment, more extensive enterprise cooperation is an effective means for shipbuilding companies to increase the competitiveness. However, considering the project scale and the uneven production capacity between the collaborative enterprises, a key issue for shipbuilding companies is to effectively combine the product-oriented project tasks and the specialized production-oriented plants. Due to information privatization, the decision-making process of project planning and scheduling is distributed and symmetric. Existing project scheduling methods and collaboration mechanisms in the shipbuilding industry are somehow inefficient. The aim of the research is to provide an assistant decision-making method to support effective task dispatching and multi-party cooperation for better utilization of the distributed resources and to help project managers control the shipbuilding process. The article initially establishes an agent-based complex shipbuilding project collaborative planning and symmetric scheduling framework, simulating the distributed collaborative decision-making process and bridging the multi-project planning with the individual project scheduling in much detail, which fills the research gap. A negotiation method based on iterative combination auction (ICA) is further proposed to solve the integration problem of project planning and task scheduling, and an illustrative example is conducted to demonstrate the effectiveness and rationality of the methods. Finally, an application case using a prototype system on shipbuilding projects collaborative planning and scheduling will be reported as a result.
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Simroth, Axel, Denise Holfeld, and Renè Brunsch. "Job Shop Production Planning under Uncertainty: A Monte Carlo Rollout Approach." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2015): 175. http://dx.doi.org/10.17770/etr2015vol3.617.
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<p>The Monte Carlo Rollout method (MCR) is a novel approach to solve combinatorial optimization problems with uncertainties approximatively. It combines ideas from Rollout algorithms for combinatorial optimization and the Monte Carlo Tree Search in game theory. In this paper the results of an investigation of applying the MCR to a Scheduling Problem are shown. The quality of the MCR method depends on the model parameters, search depth and search width, which are strong linked to process parameters. These dependencies are analyzed by different simulations. The paper also deals with the question whether the Lookahead Pathology occurs. </p>
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Wang, Guo Lei, Lin Lin, and S. S. Zhong. "Task-Oriented Hierarchical Planning and Scheduling for Discrete Manufacturing Enterprise." Applied Mechanics and Materials 10-12 (December 2007): 114–20. http://dx.doi.org/10.4028/www.scientific.net/amm.10-12.114.
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Considering the complexity and uncertainty of production planning in large manufacturing enterprise, hierarchical planning and scheduling is an effective solution to overcome the difficulty of scheduling both in long term and short term. Choosing a discrete task-oriented manufacturing factory as prototype, a hierarchical planning and scheduling model is developed, which consists of key resource leveling, detailed operation scheduling and workshop control. The upper level optimizes the bottleneck resource allocation based on aggregate networks of product, to make a feasible long term plan; short term schedule is then generated in medium level, using more detailed network; the schedule is finally executed in bottom level, which introduces a re-scheduling strategy to eliminate the plan error. To solve the model, the universal aggregate task network model and the algorithm for each level are researched. The simulation result demonstrated that the hierarchical method is valid and valuable in practice.
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Chu, Yunfei, Fengqi You, John M. Wassick, and Anshul Agarwal. "Integrated planning and scheduling under production uncertainties: Bi-level model formulation and hybrid solution method." Computers & Chemical Engineering 72 (January 2015): 255–72. http://dx.doi.org/10.1016/j.compchemeng.2014.02.023.
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