Relevant bibliographies by topics / Production scheduling Mathematics

Academic literature on the topic 'Production scheduling Mathematics'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Production scheduling Mathematics"

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Budiyantoro and Y. Kerlooza. "Priority Strategy in Clothing Production Scheduling Using Mathematics Model." IOP Conference Series: Materials Science and Engineering 407 (September 26, 2018): 012142. http://dx.doi.org/10.1088/1757-899x/407/1/012142.

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Huang, Min, Ruixian Huang, Bo Sun, and Linrong Li. "Research on the Production Scheduling Optimization for Virtual Enterprises." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/492158.

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Production scheduling is a rather difficult problem in virtual enterprises (VE) for the tasks of production which would be executed by some distributed and independent members. Factors such as the timing constraints of task and ability restrictions of the members are considered comprehensibly to solve the global scheduling optimization problem. This paper establishes a partner selection model based on an improved ant colony algorithm at first, then presents a production scheduling framework with two layers as global scheduling and local scheduling for virtual enterprise, and gives a global scheduling mathematical model with the smallest total production time based on it. An improved genetic algorithm is proposed in the model to solve the time complexity of virtual enterprise production scheduling. The presented experimental results validate the optimization of the model and the efficiency of the algorithm.
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Bierwirth, Christian, and Dirk C. Mattfeld. "Production Scheduling and Rescheduling with Genetic Algorithms." Evolutionary Computation 7, no. 1 (March 1999): 1–17. http://dx.doi.org/10.1162/evco.1999.7.1.1.

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A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.
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Gao, Yan, Xin Zhang, and Jian Zhong Xu. "An Improved Production Scheduling Algorithm Based on Resource Constraints." Applied Mechanics and Materials 455 (November 2013): 619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.455.619.

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For resource-constrained project scheduling problems, with aircraft assembly as its background, we established its mathematics model as constraint satisfaction problem. An improved critical path scheduling algorithm is proposed, considering the constraints of precedence relations, resource constraints and space constraints, through the two stages of planning, reaching for aircraft assembly task scheduling optimization objectives. Through the given numerical example results show that, when the objective consists in minimizing the project duration, the algorithm has better performance.
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Zhu, Haihua, Yi Zhang, Changchun Liu, and Wei Shi. "An Adaptive Reinforcement Learning-Based Scheduling Approach with Combination Rules for Mixed-Line Job Shop Production." Mathematical Problems in Engineering 2022 (September 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/1672166.

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Affected by economic globalization and market diversification, more manufacturing enterprises realize that large-scale production cannot adapt to the current market environment. The new trend of multivariety customized mixed-line production brings a higher level of disturbances and uncertainties to production planning. Traditional methods cannot be directly applied to the classic flexible job shop scheduling problem (FJSP). Therefore, this paper presents an adaptive scheduling method for mixed-line job shop scheduling. First, the scheduling problem caused by combined processing constraints is studied and transformed by introducing the definition of virtual operation. According to the situation of the coexistence of trial-production and batch production, the disturbance processing mechanism is established. And a scheduling decision model is established based on contextual bands (CBs) in reinforcement learning to overcome the shortcoming of poor performance of traditional single dispatching rule strategy. Through continuous trial and error learning, each scheduler can select the most suitable scheduling rules according to the environment state. Finally, we benchmark the performance of the scheduling algorithm with scheduling methods based on a variety of single scheduling rules. The results show that the proposed algorithm not only improves the performance in the mixed production scheduling problem but also effectively copes with emergency trial-production orders.
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Zaremba, Marek B. "Scheduling of production processes." Control Engineering Practice 4, no. 1 (January 1996): 141–42. http://dx.doi.org/10.1016/s0967-0661(96)90035-0.

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Aloise, Dario J., Daniel Aloise, Caroline T. M. Rocha, Celso C. Ribeiro, José C. Ribeiro Filho, and Luiz S. S. Moura. "Scheduling workover rigs for onshore oil production." Discrete Applied Mathematics 154, no. 5 (April 2006): 695–702. http://dx.doi.org/10.1016/j.dam.2004.09.021.

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Chen, Xiaowu, Guozhang Jiang, Yongmao Xiao, Gongfa Li, and Feng Xiang. "A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED." Mathematics 9, no. 18 (September 14, 2021): 2256. http://dx.doi.org/10.3390/math9182256.

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Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.
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Zandieh, M., and S. Molla-Alizadeh-Zavardehi. "Synchronizing production and air transportation scheduling using mathematical programming models." Journal of Computational and Applied Mathematics 230, no. 2 (August 2009): 546–58. http://dx.doi.org/10.1016/j.cam.2008.12.022.

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Zhang, Tao, Yue Wang, Xin Jin, and Shan Lu. "Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties." Algorithms 12, no. 6 (June 10, 2019): 120. http://dx.doi.org/10.3390/a12060120.

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Production planning and scheduling are important bases for production decisions. Concerning the traditional modeling of production planning and scheduling based on Resource-Task Network (RTN) representation, uncertain factors such as utilities are rarely considered as constraints. For the production planning and scheduling problem based on RTN representation in an uncertain environment, this paper formulates the multi-period bi-level integrated model of planning and scheduling, and introduces the uncertainties of demand and utility in planning and scheduling layers respectively. Rolling horizon optimization strategy is utilized to solve the bi-level integrated model iteratively. The simulation results show that the proposed model and algorithm are feasible and effective, can calculate the consumption of utility in every period, decrease the effects of uncertain factors on optimization results, more accurately describe the uncertain factors, and reflect the actual production process.
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Dissertations / Theses on the topic "Production scheduling Mathematics"

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Srinivasan, Sudharshana. "Spatial Scheduling Algorithms for Production Planning Problems." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3406.

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Spatial resource allocation is an important consideration in shipbuilding and large-scale manufacturing industries. Spatial scheduling problems (SSP) involve the non-overlapping arrangement of jobs within a limited physical workspace such that some scheduling objective is optimized. Since jobs are heavy and occupy large areas, they cannot be moved once set up, requiring that the same contiguous units of space be assigned throughout the duration of their processing time. This adds an additional level of complexity to the general scheduling problem, due to which solving large instances of the problem becomes computationally intractable. The aim of this study is to gain a deeper understanding of the relationship between the spatial and temporal components of the problem. We exploit these acquired insights on problem characteristics to aid in devising solution procedures that perform well in practice. Much of the literature on SSP focuses on the objective of minimizing the makespan of the schedule. We concentrate our efforts towards the minimum sum of completion times objective and state several interesting results encountered in the pursuit of developing fast and reliable solution methods for this problem. Specifically, we develop mixed-integer programming models that identify groups of jobs (batches) that can be scheduled simultaneously. We identify scenarios where batching is useful and ones where batching jobs provides a solution with a worse objective function value. We present computational analysis on large instances and prove an approximation factor on the performance of this method, under certain conditions. We also provide greedy and list-scheduling heuristics for the problem and compare their objectives with the optimal solution. Based on the instances we tested for both batching and list-scheduling approaches, our assessment is that scheduling jobs similar in processing times within the same space yields good solutions. If processing times are sufficiently different, then grouping jobs together, although seemingly makes a more effective use of the space, does not necessarily result in a lower sum of completion times.
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Park, Malcolm McKenzie. "Flowshop sequencing : a graphical approach /." Connect to thesis, 1990. http://eprints.unimelb.edu.au/archive/00001755.

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Hardin, Jill Renea. "Resource-constrained scheduling and production planning : linear programming-based studies." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/24857.

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Shieh, Alireza. "A simulated annealing approach for flexible flowshop scheduling to maximize flexibility." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://etd.wvu.edu/etd/controller.jsp?moduleName=documentdata&jsp%5FetdId=18.

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Thesis (M.S.)--West Virginia University, 2004.
Title from document title page. Document formatted into pages; contains xii, 112 p. : ill. Includes abstract. Includes bibliographical references (p. 95-99).
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Crowder, Bret. "Minimizing the makespan in a flexible flowshop with sequence dependent setup times, uniform machines, and limited buffers." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4521.

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Thesis (M.S.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains viii, 136 p. : ill. Includes abstract. Includes bibliographical references (p. 96-106).
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Possani, Edgar. "Lot streaming and batch scheduling : splitting and grouping jobs to improve production efficiency." Thesis, University of Southampton, 2001. https://eprints.soton.ac.uk/50621/.

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This thesis deals with issues arising in manufacturing, in particular related to production efficiency. Lot streaming refers to the process of splitting jobs to move production through several stages as quickly as possible, whereas batch scheduling refers to the process of grouping jobs to improve the use of resources and customer satisfaction. We use a network representation and critical path approach to analyse the lot streaming problem of finding optimal sublot sizes and a job sequence in a two-machine flow shop with transportation and setup times. We introduce a model where the number of sublots for each job is not predetermined, presenting an algorithm to assign a new sublot efficiently, and discuss a heuristic to assign a fixed number of sublots between jobs. A model with several identical jobs in an multiple machine flow shop is analysed through a dominant machine approach to find optimal sublot sizes for jobs. For batch scheduling, we tackle the NP-hard problem of scheduling jobs on a batching machine with restricted batch size to minimise the maximum lateness. We design a branch and bound algorithm, and develop local search heuristics for the problem. Different neighbourhoods are compared, one of which is an exponential sized neighbourhood that can be searched in polynomial time. We develop dynamic programming algorithms to obtain lower bounds and explore neighbourhoods efficiently. The performance of the branch and bound algorithm and the local search heuristics is assessed and supported by extensive computational tests.
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Bester, Margarete Joan. "Design of an automated decision support system for scheduling tasks in a generalized job-shop." Thesis, Stellenbosch : Stellenbosch University, 2006. http://hdl.handle.net/10019.1/21734.

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Burdett, Robert. "Sequencing and scheduling theory for mixed-model multi-stage assembly environment." Thesis, Queensland University of Technology, 2001.

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Britton, Matthew Scott. "Stochastic task scheduling in time-critical information delivery systems." Title page, contents and abstract only, 2003. http://web4.library.adelaide.edu.au/theses/09PH/09phb8629.pdf.

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"January 2003" Includes bibliographical references (leaves 120-129) Presents performance analyses of dynamic, stochastic task scheduling policies for a real- time-communications system where tasks lose value as they are delayed in the system.
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Na, Byungsoo. "Optimization of automated float glass lines." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/39637.

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Motivated by operational issues in real-world glass manufacturing, this thesis addresses a problem of laying out and sequencing the orders so as to minimize wasted glass, called scrap. This optimization problem combines aspects of traditional cutting problems and traditional scheduling and sequencing problems. In so far as we know, the combination of cutting and scheduling has not been modeled, or solved. We propose a two-phase approach: snap construction and constructing cutting and offload schedules. Regarding the second phase problem, we introduce FGSP (float glass scheduling problem), and provide its solution structure, called coveys. We analyze simple sub-models of FGSP considering the main elements: time, unit, and width. For each model, we provide either a polynomial time algorithm or a proof of NP-completeness. Since FGSP is NP-complete, we propose a heuristic algorithm, Longest Unit First (LUF), and analyze the worst case performance of the algorithm in terms of the quality of solutions; the worst case performance bound is {1+(m-1)/m}+{1/3-1/(3m)} where m is the number of machines. It is 5/3 when m=2. For the real-world problem, we propose two different methods for snap construction, and we apply two main approaches to solve cutting and offloading schedules: an MIP approach and a heuristic approach. Our solution approach produces manufacturing yields greater than 99%; current practice is about 95%. This is a significant improvement and these high-yield solutions can save millions of dollars.
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Books on the topic "Production scheduling Mathematics"

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Klein, Robert. Scheduling of Resource-Constrained Projects. Boston, MA: Springer US, 2000.

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Tanaev, Vi͡acheslav Sergeevich. Scheduling theory. Dordrecht: Kluwer Academic Publishers, 1994.

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Tanaev, Vi͡acheslav Sergeevich. Scheduling theory. Dordrecht: Kluwer Academic Publishers, 1994.

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Baptiste, Philippe. Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems. Boston, MA: Springer US, 2001.

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Kimms, Alf. Mathematical Programming and Financial Objectives for Scheduling Projects. Boston, MA: Springer US, 2001.

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Mathematical programming and financial objectives for scheduling projects. Boston: Kluwer Academic Publishers, 2001.

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Time continuity in discrete time models: New approaches for production planning in process industries. Berlin: Springer, 2005.

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Claude, Le Pape, and Nuijten Wim, eds. Constraint-based scheduling: Applying constraint programming to scheduling problems. Boston: Kluwer Academic, 2001.

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service), SpringerLink (Online, ed. Scheduling: Theory, Algorithms, and Systems. 4th ed. Boston, MA: Springer US, 2012.

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Kogan, Konstantin. Scheduling: Control-based theory and polynomial-time algorithms. Dordrecht: Kluwer Academic Publishers, 2000.

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Book chapters on the topic "Production scheduling Mathematics"

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Engell, Sebastian, Edmund Handschin, Christian Rehtanz, and Rüdiger Schultz. "Capacity Planning and Scheduling in Electrical Power Systems and in Chemical and Metallurgical Production Plants." In Production Factor Mathematics, 279–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11248-5_15.

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Teksan, Zehra Melis, Ali Tamer Ünal, and Z. Caner Taşkın. "Integrated Production Planning, Shift Planning, and Detailed Scheduling in a Tissue Paper Manufacturer." In Springer Proceedings in Mathematics & Statistics, 151–83. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5574-5_9.

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Engl, Heinz W., and Gerhard Landl. "A Scheduling Problem in the Production Line “Steel Making — Continuous Casting — Hot Rolling”." In Proceedings of the Second European Symposium on Mathematics in Industry, 301–17. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-2979-1_19.

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Ratli, Mustapha, Rachid Benmansour, Rita Macedo, Saïd Hanafi, and Christophe Wilbaut. "Mathematical Programming and Heuristics for Scheduling Problems with Early and Tardy Penalties." In Metaheuristics for Production Scheduling, 183–223. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118731598.ch8.

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Dauzère-Péres, Stéphane, and Jean-Bernard Lasserre. "Production Planning and Scheduling." In Lecture Notes in Economics and Mathematical Systems, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-46804-9_1.

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Liu, Ming, Yecheng Zhao, Feng Chu, Feifeng Zheng, and Chengbin Chu. "A Mathematical Model for Bus Scheduling with Conditional Signal Priority." In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems, 274–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85906-0_31.

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Virgílio, Bárbara Esperança, Marta Castilho Gomes, and Ana Paula Barbosa-Póvoa. "Optimization of Production Scheduling in the Mould Making Industry." In Lecture Notes in Economics and Mathematical Systems, 165–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-20430-7_21.

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Dengiz, Asiye Ozge, Kumru Didem Atalay, and Fulya Altiparmak. "Multiple Service Home Health Care Routing and Scheduling Problem: A Mathematical Model." In Advances in Manufacturing, Production Management and Process Control, 289–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20494-5_27.

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Stadtler, Hartmut. "Hierarchical Production Planning: Tuning Aggregate Planning with Sequencing and Scheduling." In Lecture Notes in Economics and Mathematical Systems, 197–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-51693-1_11.

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Dang, Quang-Vinh, Izabela Nielsen, and Kenn Steger-Jensen. "Mathematical Formulation for Mobile Robot Scheduling Problem in a Manufacturing Cell." In Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management, 37–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33980-6_5.

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Conference papers on the topic "Production scheduling Mathematics"

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Kusumawati, Miftah A., and Dwi Ertiningsih. "Optimization of production and maintenance multiple machines scheduling in batch production system: A case study on the pharmaceutical industry in Central Java, Indonesia." In INTERNATIONAL CONFERENCE OF MATHEMATICS AND MATHEMATICS EDUCATION (I-CMME) 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0115860.

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Johar, F., S. Z. Nordin, and C. N. Potts. "Coordination of production scheduling and vehicle routing problem with due dates." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954571.

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Sun, Dan, and Yong Yao. "Algorithm based on an improved profile-fitting tabu search for vaccine production scheduling." In International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), edited by Ke Chen, Nan Lin, Romeo Meštrović, Teresa A. Oliveira, Fengjie Cen, and Hong-Ming Yin. SPIE, 2022. http://dx.doi.org/10.1117/12.2628112.

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Iamsumang, Nuntiya, and Wipawee Tharmmaphornphilas. "A mathematical model for double layer precast production scheduling." In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2017. http://dx.doi.org/10.1109/ieem.2017.8289856.

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Farkas, G. A., and P. Martinek. "Scheduling of printed circuit board production with mathematical solvers." In 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2017. http://dx.doi.org/10.1109/siitme.2017.8259868.

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Singhtaun, Chansiri, and Wichitra Rungraksa. "Pump Scheduling for Water Supply Production using Mathematical Programming." In MSIE 2020: 2020 2nd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3396743.3396761.

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Oh, Yosep, and Sara Behdad. "An Optimal Quantity of Scheduling Model for Mass Customization-Based Additive Manufacturing." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97913.

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Abstract The purpose of this study is to optimize production planning decisions in additive manufacturing for mass customization (AMMC) systems in which customer demands are highly variable. The main research question is to find the optimal quantity of products for scheduling, the economic scheduling quantity (ESQ). If the scheduling quantity is too large, the time to collect customer orders increases and a penalty cost occurs due to the delay in responding to consumer demands. On the other hand, if the scheduling quantity is too small, the number of parts per jobs decreases and parts are not efficiently packed within a workspace and consequently the build process cost increases. An experiment is provided for the case of stereolithography (SLA) and 2D packing to demonstrate how the build time per part increases as the scheduling quantity decreases. In addition, a mathematical framework based on ESQ is provided to evaluate the production capacity in satisfying the market demand.
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He, Guoxi, Yongtu Liang, Limin Fang, Qi Zheng, and Liying Sun. "Optimization of Planning and Scheduling of Refinery Product Based on Downstream Requirements." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64150.

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The disconnect between the optimization systems of upstream production and downstream demand poses a legitimate problem for China’s refined oil industry in terms of overproduction waste. Established methods only partially model the refinery system and are unable to integrate detailed production plans or meet market demands. Therefore, the research on production scheduling optimization combined with the demand of downstream pipeline network has very real applications that not only reduce the consumption of human/material resources, but also increase economic efficiency. This paper aims to optimize the production scheduling of refined oil transportation based on the demand of downstream product pipelines by analyzing the relationships between crude oil supply, refinery facility capacities and refinery tanks storage. The new model will minimize the refined production surplus therefore minimizing refinery costs and wastage. This is done by implementing models custom designed to optimize the three subsystems of the overall process: oil product blending scheduling optimization, producing and processing equipment scheduling optimization, and mixed crude oil scheduling optimization. We first analyzed the relationship between all the production units from the crude oil to the distributional destinations of oil products. A mathematical model of the refinery production scheduling was then built with minimum total surplus inventory as the objective function. We assumed a known downstream demand and used a step by step model to optimize oil stocks. The oil blending plan, production scheduling, amount of crude oil, and refined oil mixing ratios were all derived from the model using three methods: a nonlinear method called Particle Swarm Optimization (PSO), the simplex method and the enumeration method. The evidence laid out in this paper verifies our models functionality and suggests that systems can be significantly optimized by using these methods which can provide solutions for industries with similar challenges. Optimization of the refinery’s overall production process is achieved by implementing models for each of the three distinguished subsystems: oil blending model, plant scheduling model, and the mixed crude oil refining model. The demand dictates the final production quantities. From those figures we are able to place constraining limits on the input crude oil. The refined oil production scheme is continuously enhanced by determining the amount of constituent feed on the production equipment according to the results of previous production cycle. After optimization, the minimum surplus inventory of the five oil components approach their lower limits that were calculated using our models. We compare the literature on scheduling optimization challenges both in China and abroad while providing a detailed discussion of the present situation of Chinese refineries. The interrelationships of production processes on each other are revealed by analyzing the system and breaking it down to three fundamental parts. Basing the final production predictions on the downstream demand, we are able to achieve a minimum refinery surplus inventory by utilizing a comprehensive refinery scheduling model composed of three sub-models.
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Nezhadshahmohammad, Farshad, Firouz Khodayari, and Yashar Pourrahimian. "Draw rate optimisation in block cave production scheduling using mathematical programming." In First International Conference on Underground Mining Technology. Australian Centre for Geomechanics, Perth, 2017. http://dx.doi.org/10.36487/acg_rep/1710_24_pourrahimian.

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Tsukanov, M. A., and O. A. Kovrizhnykh. "Analysis of Algorithms for Scheduling Complex Production Systems." In 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA). IEEE, 2019. http://dx.doi.org/10.1109/summa48161.2019.8947598.

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Reports on the topic "Production scheduling Mathematics"

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Davis, Wayne, and Albert Jones. Mathematical decomposition and simulation in real-time production scheduling. Gaithersburg, MD: error:, January 1987. http://dx.doi.org/10.6028/nbs.ir.87-3639.

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