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Journal articles on the topic 'Setup planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Šormaz, Dušan, Mayur Wakhare, and Nur-Ul Arafat. "RULE-BASED PROCESS PLANNING AND SETUP PLANNING WITH CONSIDERATIONS OF GD&T REQUIREMENTS." International Journal "Advanced Quality" 45, no. 1 (2017): 13. http://dx.doi.org/10.25137/ijaq.n1.v45.y2017.p13-20.

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Computer aided process planning is a critical link between design and manufacturing. The ready process plan in integration of design and manufacturing is always important to save manufacturing time, reduction of work in progress. This paper describes a rule based intelligent process planning system which selects necessary manufacturing processes features in the metal mechanical parts. Novel feature of this system is the use of detailed GD&T specification on both process selection and setup planning modules. Process selection module selects the necessary processes for manufacturing the hole feature that depends on feature dimensions, feature precedence, and specified geometric and dimensional tolerances. Setup planning module determines the sequences of the setups of the prismatic part on the machine tools that includes clustering and sequencing of the features in setups. This module uses rules for the following steps: tool approach direction definition, setup formation based on GD&T requirements, resolution of possible cyclic setups, setup sequencing and operation sequencing in setups. Rules can generate valid setups for both 3-axis and 4-axis milling machines. The procedures are illustrated on several examples.

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2

Gupta, S. K., and D. A. Bourne. "Sheet Metal Bending: Generating Shared Setups." Journal of Manufacturing Science and Engineering 121, no. 4 (1999): 689–94. http://dx.doi.org/10.1115/1.2833107.

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Contemporary process planners for sheet metal bending solve the process planning problem for individual parts. Quite often, many different parts can be produced on shared setups. However, plans generated by current process planning systems fail to exploit this commonality between setups and try to generate optimal setups for individual parts. In this paper, we present an algorithm for multi-part setup planning for sheet metal bending. This algorithm takes a set of parts and operation sequences for these parts, and tries to find a shared setup plan that can work for every part in the set. Setup changes constitute a major portion of the production time in batch production environments. Therefore, multi-part setup planning techniques can be used to significantly cut down the total number of setups and increase the overall through-put.

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3

Wakhare, Mayur, and Dušan Šormaz. "Sequencing of Setups in Automated Setup and Fixture Planning." Procedia Manufacturing 5 (2016): 41–57. http://dx.doi.org/10.1016/j.promfg.2016.08.007.

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4

Baan, F., O. de Waard, R. Bruggink, T. Xi, E. M. Ongkosuwito, and T. J. J. Maal. "Virtual setup in orthodontics: planning and evaluation." Clinical Oral Investigations 24, no. 7 (2019): 2385–93. http://dx.doi.org/10.1007/s00784-019-03097-3.

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Abstract Objectives The purpose of this study was to evaluate the clinical accuracy of virtual orthodontic setups by using a new CBCT-based approach. Materials and methods Ten patients who underwent pre-surgical orthodontics were included in this study. Pre-treatment and pre-surgical cone-beam CT (CBCT) scans and digital dental models were available. The pre-treatment digital dental model was used to create an orthodontic virtual setup. The digital dental models were fused with the corresponding CBCT scans, and the two CBCT scans were aligned using voxel-based matching. Moving each individual tooth from the virtual setup to the final outcome allows the calculation of the accuracy of the virtual setup by using an iterative closest point algorithm. Differences between virtual setup and final outcome were recorded as well as the ICC between two observers. Results The inter-observer variability showed a high level of agreement between the observers. The largest mean difference between observers was found in the cranial/caudal direction (0.36 ± 0.30 mm) and the roll rotation (1.54 ± 0.98°). Differences between the virtual setup and final outcome were small in the translational direction (0.45 ± 0.48 mm). Rotational mean differences were larger with the pitch of the incisors (0.00 ± 7.97°) and molars (0.01 ± 10.26°) as largest difference. Excessive extrusion of all upper teeth and more anterior movement than planned were seen for both upper and lower arch. Lower molars showed less extrusion. Clinical relevance The data of this study can be used to obtain more insight in the accuracy and achievability of orthodontic virtual setup. Tooth movement can now be studied in more details which can lead to new insights.

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5

Abedini, Vahid, Mohsen Shakeri, and Mohammad Hasan Siahmargouei. "Automatic Machining Setup Generation for Prismatic Components." Advanced Materials Research 445 (January 2012): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amr.445.953.

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Computer aided process planning is the bridge between CAD and CAM. Setup planning is the major key to transform design concept into manufacturing domain, which is mainly experience based activity in modern manufacturing industry. Setup planning is a complicated non-linear task constrained by many factors such as tool approach direction, geometric feature relationship, fixturing constraint, tolerance requirement and manufacturing practice. Setup planning identifies which features must be machined in each setup and determines locating datum for each setup. This paper focuses on the development of a formalized procedure for automatic generation of feasible setups. For preventing of tolerance stack-up tried to use datum face as reference plane in fixture design. So, this paper presents a new method for setup planning with accurate respect to datum faces in design and machining. For the proposed work the authors have introduced two concepts namely, inferiority face and control face. A rule-based procedure in several steps is used for solving the problem. The system is developed in Visual Basic on a Solid Works platform. Trial runs with industrial parts indicate that the system is applicable for industrial use.

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6

Kannan, Sridhar, Ashish Kumar Singh, and Gaurav Gupta. "An edge over diagnostic setup." APOS Trends in Orthodontics 7 (December 1, 2017): 294–95. http://dx.doi.org/10.4103/2321-1407.219429.

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Diagnostic setup proposed by H.D. Kingsley serves as a practical aid in treatment planning and diagnosis. These setups have some inherent shortcomings. A simple technique of duplication of the setups in dental stone can solve problems encountered before as well as provide many other advantages over the conventional procedure. The diagnostic setup is prepared by the conventional method [Figure 1]. An alginate impression is then taken of the setups and poured in dental stone to obtain the derived treatment model [Figure 2]. The same setup can now be further modified for alternate lines of treatment. Subsequently models could then be obtained as required [Figure 3].

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7

Deja, Mariusz, and Mieczyslaw Siemiatkowski. "Generative Process Planning with Reasoning Based on Geometrical Product Specification." Key Engineering Materials 597 (December 2013): 159–64. http://dx.doi.org/10.4028/www.scientific.net/kem.597.159.

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The focus of this paper is on CAPP for parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centers for parts shaping and grinding machines for finishing. It presents in particular a decision making scheme for setup determination as a part of generative process planning. The planning procedure consists of two stages. The first stage is associated with generic setup determination applying tool accessibility analysis to machined features and reasoning based on geometrical product specification (GPS). The second stage involves machine specific setup planning considering the availability and capability of machines on a shop floor through setup merging. The impact of dimensional and geometric tolerance as well as the surface roughness of relatively complex mechanical element on the generated process plan with several setups was presented. The relevant reasoning mechanism within a decision making scheme on generated process alternatives is shown based on a numeric case.

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8

Huang, Samuel H. "Automated setup planning for lathe machining." Journal of Manufacturing Systems 17, no. 3 (1998): 196–208. http://dx.doi.org/10.1016/s0278-6125(98)80061-7.

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9

ZHANG‡, H. C., S. H. HUANG, and J. MEI. "Operational dimensioning and tolerancing in process planning: setup planning." International Journal of Production Research 34, no. 7 (1996): 1841–58. http://dx.doi.org/10.1080/00207549608905000.

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10

JONEJA, AJAY, and TIEN-CHIEN CHANG. "Setup and fixture planning in automated process planning systems." IIE Transactions 31, no. 7 (1999): 653–65. http://dx.doi.org/10.1080/07408179908969866.

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11

Romero, F., G. M. Bruscas, and J. Serrano. "A New Methodological Approach for the Machining Process Planning." Key Engineering Materials 502 (February 2012): 13–18. http://dx.doi.org/10.4028/www.scientific.net/kem.502.13.

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This document presents a procedure for carrying out the step of selecting the locating surfaces and validating the setup, which is incorporated within a methodology for machining process planning that, unlike other classical approaches, deals with the problem from back to front. The procedure, which uses the typical tolerance graphs and tolerance transfer techniques based on the worst case, is applied first of all to the last setup from the alternative process plan that is being drawn up, and lastly it is applied to the first. This method allows us to consider the effects of the transmission of the variability among setups (machining stages) proposed by the multi-station variability models.

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12

Barreto, Monica S., Jorge Faber, Carlos J. Vogel, and Telma M. Araujo. "Reliability of digital orthodontic setups." Angle Orthodontist 86, no. 2 (2015): 255–59. http://dx.doi.org/10.2319/120914-890.1.

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ABSTRACT Objective: To evaluate the reliability of digital orthodontic setup technology by comparing it with manual setups and models cast at the end of orthodontic treatment. Materials and Methods: Initial models, manual setups, and final models of 20 patients were used. The initial and final models, as well as the manual setups, were scanned using a 3Shape R-700 scanner, while the digital setups were fabricated based on the initial models using 3Shape OrthoAnalyzer software. Evaluation of the models based on the manual setup, digital setup, and final models of each patient was performed using the following linear measurements: intercanine widths, intermolar widths, and length of the upper and lower dental arches. Results: The results disclosed that none of the measures assessed through the manual setup, digital setup, and final models showed statistically significant differences (P &gt; .05). Conclusions: Based on these findings, it can be inferred that digital setups are as effective and accurate as manual setups and constitute a tool for diagnosing and treatment planning that can be reliably reproduced in orthodontic treatments.

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13

Mihaylov, Oleg, Galina Nikolcheva, and Peter Popov. "SETUP GENERATION USING NEURAL NETWORKS." CBU International Conference Proceedings 5 (September 24, 2017): 1169–74. http://dx.doi.org/10.12955/cbup.v5.1090.

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The article presents an unsupervised learning algorithm that groups technological features in a setup for machining process. Setup generation is one of the most important tasks in automated process planning and in fixture configuration. A setup is created based on approach direction of the features. The algorithm proposed in this work generates a neural network that determines the setup each feature belongs to, and the number of setups generated is minimal. This algorithm, unlike others, is not influenced by the order of the input sequence. Parallel implementation of the algorithm is straightforward and can significantly increase the computational performance.

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14

Xu, Nuo, and Samuel H. Huang. "Multiple Attributes Utility Analysis in Setup Plan Evaluation." Journal of Manufacturing Science and Engineering 128, no. 1 (2005): 220–27. http://dx.doi.org/10.1115/1.2117407.

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Setup planning is an intermediate phase of process planning, and automating setup planning constitutes a critical component of computer-aided process planning. The common absence of a clearly defined optimality of setup plans has been a major obstacle to converting automatic setup planning research progress into real advancement in practice. The optimality of a setup plan is a multiple attributes problem associated with uncertainties, and human interaction is vital in clarifying the optimality structure in a dynamic setup planning environment. In this paper, a quantitative setup plan evaluation system driven by multiple-attributes utility analysis coupled with manufacturing error simulation is proposed to serve three purposes: (1) to clarify what is optimality of setup plans, (2) to provide a systematic method of evaluating setup plan alternatives quantitatively, and (3) to incorporate in existing automatic setup planning systems a human interface to fulfill their potential values.

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15

Haddadzade, Mohammad, Mohammad Reza Razfar, and Mohammad Hossein Fazel Zarandi. "Multipart setup planning through integration of process planning and scheduling." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 230, no. 6 (2015): 1097–113. http://dx.doi.org/10.1177/0954405414565138.

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16

Mohapatra, P., Lyes Benyoucef, and M. K. Tiwari. "Realising process planning and scheduling integration through adaptive setup planning." International Journal of Production Research 51, no. 8 (2013): 2301–23. http://dx.doi.org/10.1080/00207543.2012.715770.

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17

GAO, Bo. "Setup Planning Method Based on Memetic Algorithm." Journal of Mechanical Engineering 51, no. 3 (2015): 162. http://dx.doi.org/10.3901/jme.2015.03.162.

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18

Kim, In-Ho, Jung-Soo Oh, and Kyu-Kab Cho. "Computer aided setup planning for machining processes." Computers & Industrial Engineering 31, no. 3-4 (1996): 613–17. http://dx.doi.org/10.1016/s0360-8352(96)00226-4.

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19

Lauven, Lars-Peter. "An optimization approach to biorefinery setup planning." Biomass and Bioenergy 70 (November 2014): 440–51. http://dx.doi.org/10.1016/j.biombioe.2014.07.026.

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20

Rong, Y., X. Liu, J. Zhou, and A. Wen. "Computer-Aided Setup Planning and Fixture Design." Intelligent Automation & Soft Computing 3, no. 3 (1997): 191–206. http://dx.doi.org/10.1080/10798587.1997.10750702.

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21

Zhang, Y., W. Hu, and Y. Rong. "TOLERANCE ANALYSIS BASED COMPUTER-AIDED SETUP PLANNING." Journal of the Chinese Institute of Industrial Engineers 20, no. 3 (2003): 247–57. http://dx.doi.org/10.1080/10170660309509233.

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22

Cai, Ningxu, Lihui Wang, and Hsi-Yung Feng. "GA-based adaptive setup planning toward process planning and scheduling integration." International Journal of Production Research 47, no. 10 (2009): 2745–66. http://dx.doi.org/10.1080/00207540701663516.

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23

Asgher, Umer, Riaz Ahmad, and Shahid Ikramullah Butt. "Mathematical Modeling of Manufacturing Process Plan, Optimization Analysis with Stochastic and DSM Modeling Techniques." Advanced Materials Research 816-817 (September 2013): 1174–80. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.1174.

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The first job of the manufacturing workforce as they get novel drawings is to carry out the process planning. This task, once finished, usually direct both the organization and manufacturing setups. Process planning in manufacturing setup offers a specific and clear chronological path regarding how the product should be running and fabricated in a manufacturing system. In highly developed manufacturing setups, this would persuade, how the setup will be planned and laid out in grounding for the novel product. In this research work, fundamental process plan is developed for a side plate manufacturing together with all design and user requirements. Mathematically modeling is done using progressive closed loop approach. Research then searches the capabilities of optimization techniques like DSM (dependency structure matrix) and a novel stochastic search to provide the best approximate process planning solution. Finally the global optimization is analysed in both the techniques and one technique reaches at optimum solution.

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24

ZHANG, FA PING. "RESEARCH ON GRAPH THEORY-BASED MANUFACTURING SETUP PLANNING." Journal of Advanced Manufacturing Systems 07, no. 02 (2008): 313–18. http://dx.doi.org/10.1142/s0219686708001541.

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Automatic generation of manufacturing setup planning is studied in this work. The mathematical model to describe the tolerance information and datum-machining feature relationship has been formulated based on extended graphics. By means of the transformation between the feature tolerance relationship graph(FTG) and the datum-machining feature relationship graph (DMG), the algorithm to automatically generate setup planning has been given based on the tolerance analysis and manufacturing resources capability model. The algorithm can identify the machining feature and datum, optimize setup groups based on the manufacturing resource capability and tolerance analysis, as well as minimize the influence of the locating error stack-up on the machining quality. Finally, a case study of setup planning is presented to verify the feasibility of the methodology.

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Kibouka, Guy Richard, Donatien Nganga-Kouya, Jean-Pierre Kenné, Vladimir Polotski, and Victor Songmene. "Maintenance and setup planning in manufacturing systems under uncertainties." Journal of Quality in Maintenance Engineering 24, no. 2 (2018): 170–84. http://dx.doi.org/10.1108/jqme-11-2016-0069.

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PurposeThe purpose of this paper is to find the optimal production and setup policies for a manufacturing system that produces two different types of parts. The manufacturing system consists of one machine subject to random failures and repairs. Reconfiguring the machine to switch production from one type of product to another generates a non-production time and a significant cost.Design/methodology/approachThis paper proposes an approach based on the development of optimal production and setup policies, taking into account the possibilities of undertaking the setup for all modes of the machine, and covering them at the end of setup. New optimality conditions are developed in terms of modified Hamilton-Jacobi-Bellman (HJB) equations and recursive numerical methods are applied to solve such equations.FindingsThe proposed approach led to determine more realistic production rates of both parts and setup sequences for the different modes of the machine that significantly influence the inventory and the system capacity. A numerical example and sensitivity analysis are used to determine the structure of the optimal policies and to show the helpfulness and robustness of the results obtained.Practical implicationsFollowing the steps of the proposed approach will provide the control policies for industrial manufacturing systems with setup permitted at all modes of the machine, and when the setup does not necessarily restore the machine to its operational mode. The proposed optimal policy takes into account the stochastic nature of the machine mode at the end of setup and we show that ignoring it leads to non-natural policies and underestimates significantly the safety stock thresholds.Originality/valueConsidering the assumptions presented in this paper leads to a new structure of the control laws for the production planning of manufacturing systems with setup.

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Fuss, Martin, Sean X. Cavanaugh, Cristina Fuss, Dennis A. Cheek, and Bill J. Salter. "Daily Stereotactic Ultrasound Prostate Targeting: Inter-user Variability." Technology in Cancer Research & Treatment 2, no. 2 (2003): 161–69. http://dx.doi.org/10.1177/153303460300200213.

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We analyzed the inter-user variability of patient setup for prostate radiotherapy using a stereotactic ultrasound-targeting device. Setup variations in 20 prostate cancer patients were analyzed. Users were a radiation oncologist, a medical physicist, four radiation technologists (RTT) and a radiologist. The radiation oncologist, radiologist, physicist and two RTTs were experienced users of the system (>18 months of experience); two RTTs were users new to the system. Gold standard for this analysis was a control CT acquired immediately following ultrasound targeting. For inter-user variability assessments, the radiation oncologist provided a set of axial and sagittal freeze-frames (standard freeze-frames) for virtual targeting by all users. Additionally each user acquired individual freeze-frames for target alignments. We analyzed the range of virtual setups in each patient along the principal room axes based on standard and individual freeze-frames. The magnitude of residual setup error and percentage of setup change for each user was assessed by control CT/planning CT comparison with individual virtual shifts. A total of 184 alignments were analyzed. The range of virtual shifts between users was 2.7±1.4, 3.6±1.1, and 4.4±1.4 mm (mean±SD) in x, y and z-direction for setups based on standard freeze-frames and 3.9±2.6, 6.0±4.7, and 5.4±2.7 mm for setups based on individual freeze-frames. When only virtual shifts of experienced users were analyzed, the mean ranges were reduced by up to 2.4 mm. Average magnitude of initial setup error before ultrasound targeting was 14.3 mm. Average improvement of prostate setup was 63.1±23.4% in experienced and 35.14±37.7% in inexperienced users, respectively (p<0.0001). Only 5 of 184 (2.7%) virtual alignments would have introduced new larger setup errors (mean 3.2 mm, range 0.2 to 9.5 mm) than the magnitude of the initial setup error. We conclude that ultrasound guided treatment setup for patients treated for prostate cancer can be performed with high inter-user consistency and does lead to improved treatment setup in more than 97% of attempted setups. Experienced use is correlated with a reduced range of setups between users and higher degree of setup improvement when compared with users new to the system.

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27

Attila, Rétfalvi, Michael Stampfer, and Szegh Imre. "Fixture and Setup Planning and Fixture Configuration System." Procedia CIRP 7 (2013): 228–33. http://dx.doi.org/10.1016/j.procir.2013.05.039.

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Ong, S. K., and A. Y. C. Nee. "Application of Fuzzy Set Theory to Setup Planning." CIRP Annals 43, no. 1 (1994): 137–44. http://dx.doi.org/10.1016/s0007-8506(07)62182-9.

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29

Deo, Shantanu, Roya Javadpour, and Gerald M. Knapp. "Multiple setup PCB assembly planning using genetic algorithms." Computers & Industrial Engineering 42, no. 1 (2002): 1–16. http://dx.doi.org/10.1016/s0360-8352(01)00062-6.

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30

De’, Rahul, and Jerrold H. May. "Using operational planning horizons for determining setup changes." Omega 26, no. 5 (1998): 581–92. http://dx.doi.org/10.1016/s0305-0483(98)00001-2.

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31

Chen, J., Y. F. Zhang, and A. Y. C. Nee. "Setup planning using Hopfield net and simulated annealing." International Journal of Production Research 36, no. 4 (1998): 981–1000. http://dx.doi.org/10.1080/002075498193480.

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32

WU, H. C., and T. C. Chang. "Automated setup selection in feature-based process planning." International Journal of Production Research 36, no. 3 (1998): 695–712. http://dx.doi.org/10.1080/002075498193642.

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33

Liu, Q., and S. H. Huang. "Rigorous Application of Tolerance Analysis in Setup Planning." International Journal of Advanced Manufacturing Technology 21, no. 3 (2002): 196–207. http://dx.doi.org/10.1007/s001700300022.

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34

Sakurai, Hiroshi. "Automatic setup planning and fixture design for machining." Journal of Manufacturing Systems 11, no. 1 (1992): 30–37. http://dx.doi.org/10.1016/0278-6125(92)90015-8.

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35

Huang, Samuel H., and Hong-Chao Zhang. "Tolerance analysis in setup planning for rotational parts." Journal of Manufacturing Systems 15, no. 5 (1996): 340–50. http://dx.doi.org/10.1016/0278-6125(96)84197-5.

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36

Yao, S., X. Han, Y. Yang, et al. "Computer aided manufacturing planning for mass customization: part 2, automated setup planning." International Journal of Advanced Manufacturing Technology 32, no. 1-2 (2006): 205–17. http://dx.doi.org/10.1007/s00170-005-0328-y.

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37

Zhang, Guo Zheng, Jiang Han, and You Yu Liu. "Approach to Machining Setup Process Planning of NC Machine Tools Based on Graph Theory." Advanced Materials Research 542-543 (June 2012): 1190–95. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1190.

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Setup datum of fixture design is an important factor of Numerical Control (NC) machining process planning. Using graph theory of FTG (Feature Tolerance relationship Graph) and DMG (Datum and Machining feature relationship Graph) technology, the feature relationship was proposed to construct a relational table of DMMT (Datum and Machining and Machine Tools) in NC machining setup process. The degree of graph as a discriminant evidence of the machining setup datum was given, so that the machining setup process planning was correctly designed. Combined with the capability of NC machine tools, the unit normal vector of feature facet between the feature datum and machining facet were multiplied to determine the fixture capability, which provide a reasonable proposal of machining setup process for multi-position rotation and multi-piece fixture design. Finally, a case study of machining setup process planning and multi-piece fixture equipment design from corporation was presented to verify the feasibility of the method that was based on the graph theory.

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Hung, Y. F., C. C. Shih, and C. P. Chen. "Evolutionary Algorithms for Production Planning Problems with Setup Decisions." Journal of the Operational Research Society 50, no. 8 (1999): 857. http://dx.doi.org/10.2307/3010344.

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39

Sakane, Shigeyuki, Tomomasa Sato, and Masayoshi Kakikura. "Setup Planning of Active Visual Sensing for Autonomous Robots." Journal of Robotics and Mechatronics 3, no. 3 (1991): 196–200. http://dx.doi.org/10.20965/jrm.1991.p0196.

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40

Bandyopadhyay, Rajat. "Patient setup variations for treatment planning for breast cancer." Journal of Marine Medical Society 22, no. 1 (2020): 73. http://dx.doi.org/10.4103/jmms.jmms_79_19.

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41

Xue, Guisen, O. Felix Offodile, Hong Zhou, and Marvin D. Troutt. "Integrated production planning with sequence-dependent family setup times." International Journal of Production Economics 131, no. 2 (2011): 674–81. http://dx.doi.org/10.1016/j.ijpe.2011.02.012.

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42

Coffey, Mary, and Aude Vaandering. "Patient setup for PET/CT acquisition in radiotherapy planning." Radiotherapy and Oncology 96, no. 3 (2010): 298–301. http://dx.doi.org/10.1016/j.radonc.2010.07.020.

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Hung, Y.-F., C.-C. Shih, and C.-P. Chen. "Evolutionary algorithms for production planning problems with setup decisions." Journal of the Operational Research Society 50, no. 8 (1999): 857–66. http://dx.doi.org/10.1057/palgrave.jors.2600778.

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44

Huang, S. H., H. C. Zhang, and W. J. B. Oldham. "Tolerance analysis for setup planning: A graph theoretical approach." International Journal of Production Research 35, no. 4 (1997): 1107–24. http://dx.doi.org/10.1080/002075497195579.

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Hebbal, S. S., and N. K. Mehta. "Setup planning for machining the features of prismatic parts." International Journal of Production Research 46, no. 12 (2008): 3241–57. http://dx.doi.org/10.1080/00207540601070937.

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Kibouka, Guy-Richard, Donatien Nganga-Kouya, Jean-Pierre Kenne, Victor Songmene, and Vladimir Polotski. "Production Planning of a Failure-Prone Manufacturing System under Different Setup Scenarios." Journal of Applied Mathematics 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4930817.

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This paper presents a control problem for the optimization of the production and setup activities of an industrial system operating in an uncertain environment. This system is subject to random disturbances (breakdowns and repairs). These disturbances can engender stock shortages. The considered industrial system represents a well-known production context in industry and consists of a machine producing two types of products. In order to switch production from one product type to another, a time factor and a reconfiguration cost for the machine are associated with the setup activities. The parts production rates and the setup strategies are the decision variables which influence the inventory and the capacity of the system. The objective of the study is to find the production and setup policies which minimize the setup and inventory costs, as well as those associated with shortages. A modeling approach based on stochastic optimal control theory and a numerical algorithm used to solve the obtained optimality conditions are presented. The contribution of the paper, for industrial systems not studied in the literature, is illustrated through a numerical example and a comparative study.

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DiCostanzo, Dominic J., and Emily S. Patterson. "Using Incident Reports to Advance Patient Safety and System Resilience in Radiation Oncology Treatment Planning." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (2021): 286–90. http://dx.doi.org/10.1177/2327857921101256.

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We used incident reports to improve treatment planning in radiation oncology to advance patient safety. First, a cluster of related reports enabled discovery of communication issues for setup instructions stemming from a text box with a limited character size. A redesigned process used structured data and image formats to reduce the proportion of inaccurate setup instructions. An upward trend in inaccurate setup instructions identified that some rotating staff were unaware of which equipment to use. Implementing default disease site setups ensured that when a patient was to commence treatment for a specific disease site, it was known which immobilizing equipment should be utilized as the basis before customization. Second, inappropriate plans were identified for proactive improvement. The process resilience was enhanced with targeted validations of generated treatment plans. The automated checks incorporated data automatically pulled from the electronic health record. Useful methods included clustering reports into categories to proactively see patterns in reported issues, collaborating with clinical experts from different perspectives, surveillance monitoring to identify new risks as conditions change, improving communications across disciplines through health information technology, and increasing system resilience with targeted validation of known gaps and variation in plans. The implementation of ILS frameworks, careful review of events and categorization, which culminate with interventions targeting the most frequent or highest severity incidents reported, increased patient safety within our department.

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Khamjan, Sakda, and Supachai Pathumnakul. "Collaborative Production Planning in Multi-Plant Feed Mill Industry." Applied Mechanics and Materials 781 (August 2015): 663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.781.663.

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In animal feed production, there are several feed products to be processed through mixer and pelleting machines. To avoid the contamination between two feed products, machines are required to be cleaned (i.e., machine setup) when switching production between different feed products. The cleaning process lessens the machine capacity. In this paper, the production planning based on the collaboration between two feed plants is studied in order to reduce unnecessary machine setup time. The problem is to optimally allocate products to be processed in each plant with suitable production lot size. The trade-off between inter-plant transferring cost and the setup cost is considered. The mathematical model is developed and solved with small size problems. The results indicate that the collaborative plan provides the better plan with the cost 16.05% less than the non-collaborative production plan.

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Kibouka, Guy Richard, Jean Brice Mandatsy Moungomo, and Adoum Traoré Ndama. "Simultaneous Planning of Production, Setup and Maintenance for an Unreliable Multiple Products Manufacturing System." European Journal of Engineering and Technology Research 6, no. 5 (2021): 24–34. http://dx.doi.org/10.24018/ejers.2021.6.5.2495.

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The work presented in this paper addresses the problem of joint optimization of the production, setup and corrective maintenance activities of a manufacturing system. This system consists of a machine subject to breakdowns and repairs and producing two types of parts. A corrective maintenance strategy whose repair rate depends on the number of setup operations already performed on the production system is considered in this work. The objective of this research is to propose a policy that controls production, setup, and corrective maintenance. The contribution of this paper is through the control of the repair rate, combined with the planning of production and setup in a dynamic and stochastic context. Optimality conditions in the form of Hamilton-Jacoby-Bellman (HJB) equations are obtained and a numerical approach is proposed in order to deal with the joint optimization issues. Extensive simulations are performed to address many scenarios that illustrate the interactions between production, setup and maintenance activities.

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Eshete, Akine, and Yohannes Adissu. "Women’s Joint Decision on Contraceptive Use in Gedeo Zone, Southern Ethiopia: A Community Based Comparative Cross-Sectional Study." International Journal of Family Medicine 2017 (March 7, 2017): 1–9. http://dx.doi.org/10.1155/2017/9389072.

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A community based comparative cross-sectional study design was employed to assess the mutual consent of women about family planning use in urban and rural villages of Gedeo zone. Two-thirds (67.4%) of women made joint decision on contraceptive use, varying between urban (70.9%) and rural (63.4%) settings. This difference was statistically significant where women in urban setup had a 41% (AOR, 1.41; 95% CI (1.15, 2.01) added chance of making joint decision than the rural counterpart. In both settings, attitude towards contraceptive method was an independent predictor of joint contraceptive decision (AOR = 2.85) in urban and (AOR = 2.81) rural women. Contrarily, different factors were found to be associated with joint contraceptive decision in either setup. In urban, having better knowledge about contraceptive methods (AOR = 2.9) and having lower age difference (AOR = 2.2) were found to be strong predictors of joint decision on contraceptive use, while having too many children (AOR = 2.2) and paternal support (AOR = 7.1) in rural setups. Lower level of joint decision making on contraceptive use was reported in both setups. Factors associated with joint decision varied between the two setups, except for attitude towards contraceptive methods. Future family planning program should address sociocultural, knowledge and attitude factors.

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