Supersymmetric theory of stochastic ABC model

AbstractThe qualitative model presented in this work recovers the onset of the four fields that correspond to those of each floral organ whorl of Arabidopsis flower, suggesting a mechanism for the generation of the positional information required for the differential expression of the A, B and C identity genes according to the ABC model for organ determination during early stages of flower development. Our model integrates a previous model for the emergence of WUS pattern in the apical meristem, and shows that this pre-pattern is a necessary but not sufficient condition for the posterior information of the four fields predicted by the ABC model. Furthermore, our model predicts that LFY diffusion along the L1 layer of cells is not a necessary condition for the patterning of the floral meristem.

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MODEL ABC MULTI-CRITERIA INVENTORY CLASSIFICATION (MCIC) MENGGUNAKAN PEMROGRAMAN LINEAR PADA PRODUK KIMIA

JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK ◽

10.30988/jmil.v3i2.242 ◽

2019 ◽

Vol 3 (2) ◽

pp. 106-118

Author(s):

Eko Pratomo (Politeknik APP - Indonesia)

Keyword(s):

Linear Programming ◽

Lead Time ◽

Multiple Criteria ◽

Traditional Model ◽

Abc Model ◽

Linear Programming Method ◽

Inventory Policies ◽

Chemical Product ◽

Class A ◽

Class B

Abstract In order to maintain their inventory efficiently, enterprises need to prioritize inventory policies considering multiple criteria. A Multi Criteria Inventory Classification (MCIC) is one of the most effective techniques widely used to classify inventory. In this paper, multiple criteria (annual value, lead time, cost per unit) are considered on ABC inventory classification. The aim of this study is classify products considering those multiple criteria. Multiple criteria ABC Classifications methodology developed by Ramanathan-Model and Ng-Model are used and compared with traditional method. Data are collected from annual chemical product transaction on PT XYZ during 2018. In this paper, linear programming method is used to solve ABC MCIC Model. The result of this study show that 12 items (14%) are identified as Class A, 26 items (30%) as class B and the remaining 48 items (56%) as C Class. In our conclution, we propose inventory policies based on the result of the ABC Models. Keywords: ABC Model; MCIC; Traditional Model; Ramanathan-Model; Ng-Model; Linear Programming; Chemical Product.Abstrak Dalam mengelola persediaan secara efisien, perusahaan perlu menentukan prioritas pengelolaan persediaan dengan mempertimbangkan beberapa kriteria. Klasifikasi ABC Multi Kriteria (MCIC) merupakan model klasifikasi persediaan barang yang umum digunakan oleh perusahaan dalam mengelola persediaan dalam jumlah besar. Penelitian ini menggunakan multi kriteria berupa nilai total produk, lead time dan biaya per unit. Tujuan penelitian adalah mengelompokan jenis/kelas barang sesuai dengan tingkat kepentingan dengan mempertimbangkan multi kriteria. Metode Multi kriteria yang telah dikembangkan oleh Ramanathan-Model dan Ng-Model dibandingkan dengan hasil klasifikasi Single criteria ABC (Traditional model). Data yang digunakan adalah data tahunan transaksi produk kimia PT XYZ di tahun 2018. Penyelesaian model ABC multi kriteria (MCIC) dengan pemrograman linear. Terdapat 86 items produk kimia yang diklasifikasikan dengan hasil klasifikasi A sejumlah 12 item (14%), B sejumlah 26 item (30%) dan item C sejumlah 48 item (56%). Pada penelitan ini juga disampaikan kebijakan inventory masing-masing kelas berdasarkan hasil klasifikasi ABC model yang telah dilakukan.Kata Kunci: Model ABC; MCIC; Model tradisional; Model Ramanathan; Model Ng; Pemrograman Linear; Produk kimia.

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Adjusted Bayesian Completion Rates (ABC) Estimation

10.31235/osf.io/at368 ◽

2021 ◽

Author(s):

Bilal Fouad Barakat ◽

Ameer Dharamshi ◽

Leontine Alkema ◽

Manos Antoninis

Keyword(s):

Time Series ◽

Predictive Performance ◽

School Level ◽

Household Surveys ◽

Completion Rates ◽

Abc Model ◽

Informative Priors ◽

Random Walk Process ◽

Meaningful Improvement ◽

Associated Data

Estimating school completion is crucial for monitoring SDG 4 on education, and unlike enrollmentindicators, relies on household surveys. Associated data challenges include gaps between waves, conflictingestimates, age misreporting, and delayed completion. Our Adjusted Bayesian Completion Rates (ABC)model overcomes these challenges to produce the first complete and consistent time series for SDGindicator 4.1.2, by school level and sex, for 153 countries. A latent random walk process for unobservedtrue rates is adjusted for a range of error and variance sources, with weakly informative priors. The modelappears well-calibrated and offers a meaningful improvement in predictive performance.

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