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- | Your website/: WWW.mbsetools.com | + | ---- |

+ | ====== Feedback ====== | ||

+ | Please, use this section to provide feedback. : | ||

+ | * A good solid effort for the first iteration. | ||

- | This notice below EXPIRES ON: Nov 26, 2020!. | + | \\ |

+ | \\ | ||

+ | \\ | ||

+ | ---- | ||

+ | ====== TO-DO Task List ====== | ||

+ | ---- | ||

+ | ===== General ===== | ||

+ | * The helper function ' | ||

+ | * Tim identified a good way to improve the SimWrapper_ functions (which enable a Simulink model to be called like a MATLAB function), but there' | ||

+ | * Whether workstation models or inventory models, everything concerns a single-product system. | ||

+ | * One idea to help this open-source effort grow: For a final project in graduate-level classes, require a meaningful contribution to the open-source project (a new analytical approximation, | ||

- | We have not obtained a settlement from you. | + | ---- |

- | We have actually attempted to call you but were incapable to reach you. | + | ===== Inventory Models ===== |

+ | * In the Base Stock and QR inventory simulation models, better ideas are needed for collection and visualization of backorder data; right now what's there is a bit of a hack. | ||

+ | * For the EOQ, Base Stock, and QR inventory simulation models, use regression to fit a multi-dimensional surface approximation (sometimes called a " | ||

+ | * Each system has a variety of things which can change (demand mean, scv, distrib type, cost parameters ...); make these the inputs. | ||

+ | * Each system has a variety of things which can be measured (costs, fill rate, inventory level ...); make these the outputs. | ||

+ | * If using multiple linear regression, then multiple outputs requires fitting multiple regression models, one per output. | ||

- | Please Go To: https:// | + | ---- |

+ | ===== Single Workstation Models ===== | ||

+ | * ALWAYS USE SCV, not variance. | ||

+ | * The big three performance measures are Work-In-Process, | ||

+ | * The MM1, MMk, GG1, GGk analytical approximation functions are vectorized in either (1) arrival & processing time means & variances, or (2) number-of-servers K. However, the functions are not simultaneously vectorized in both sets of parameters, e.g. either one set or the other must be scalars. | ||

+ | * Hopp & Spearman in section 9.4.2 (ed. 2) derive analytical formulas for cycle time of a single workstation with process batching. | ||

- | For info as well as to make a optional settlement for domain solutions. | + | ---- |

+ | ===== Production Line Models ===== | ||

+ | * Implement analytical formulas in Hopp & Spearman (ch. 7, ed. 2) for best-case, worst-case, and practical worst-case performance of a production line. The create experiements to test best-case, worst-case, and practical worst-case analytical results with simulation results. | ||

+ | * Hopp & Spearman include equations 8.10-8.11 (ed. 2) to characterize the variability of a single workstation' | ||

- | 11262020212536 | + | ==== Push Vs. Pull Dispatch Control ==== |

+ | * Implement analytical formulas in Hopp & Spearman (ch. 10, ed. 2) for analysis of CONWIP lines using mean-value analysis. | ||

+ | * Tim observed that a true comparison of push versus pull would initialize the same blocks in side-by-side simulation models with the same random number seeds, e.g. use common random numbers. | ||

+ | * Advanced: | ||

+ | * Advanced: |

feedback.txt · Last modified: 2021/05/08 07:31 by 136.243.4.209