MENENTUKAN METODE INPUT PROBABILITY DISTRIBUTION DALAM PEMODELAN DAN SIMULASI DI ANTRIAN KASIR TOKO BUKU PT. XYZ

PT XYZ merupakan perusahaan yang memiliki banyak toko penjualan buku yang tersebar di Indonesia. PT XYZ berusaha melakukan perbaikan yang berkesinambungan dalam melakukan pelayanan terhadap pelanggan salah satunya adalah antrian di kasir. Sistem dapat diperbaiki dengan cara membuat skenario-skenario yang kemudian dilakukan pemodelan dan simulasi. Kita harus bisa memilih metode pemodelan dan simulasi yang terbaik yang memiliki dampak error terkecil. Memodelkan dan simulasi sistem nyata dapat dilakukan dengan beberapa pendekatan dalam input probability distribution antara lain metode trace driven, metode empris, dan metode teoritis. Pada penelitian ini dilakukan pemodelan dan simulasi di antrian kasir di PT XYZ dengan menggunakan ketiga metode pendekatan tersebut. Dari ketiga hasil simulasi didapatkan bahwa semua hasil waiting time dari ketiga metode tidak memiliki perbedaan yang berarti antara data sistem nyata dengan model sistem nyata. Kemudian dari selisih total waiting time, utilisasi, dan jumlah pelanggan yang terlayani metode trace driven adalah metode terbaik.

Tolerance simulation of composite wingbox assembly considering preloading-modified distribution

Purpose Tolerance simulation’s reliability depends on the concordance between the input probability distribution and the real variation. The prescribed clamp force introduced changes in parts’ variation, which should be reflected in the input probability distribution for the tolerance simulation. The paper aims to present a tolerance analysis process of the composite wingbox assembly considering the preloading-modified distribution and especially focuses on the spring-in deviation of the thin-walled C-section composite beam (TC2B). Design/methodology/approach Based on finite element analysis model of TC2B, the preloading-modified probability distribution function (PDF) of the spring-in deviation is obtained. Thickness variations of the TC2B are obtained from the data of the downscaled composite wingbox. These variations are input to the computer-aided tolerance tools, and the final assembly variations are obtained. The assembly of the downscaled wingbox illustrates the effect of preloading on the probability distribution of the spring-in deviation. Findings The results have shown that the final assembly variations estimated with the modified probability distribution is more reliable than the variation of the initial normal distribution. Originality/value The tolerance simulation work presented in the paper will enhance the understanding of the composite parts assembling with spring-in deviations, improve the chance to choose assembling processes that allow specifications to be met and help with tolerance allocation in composites assembly.

Improving Precision and Reducing Runtime of Microscopic Traffic Simulators through Stratified Sampling

This paper examines the application of Latin Hypercube Sampling (LHS) and Antithetic Variables (AVs) to reduce the variance of estimated performance measures from microscopic traffic simulators. LHS and AV allow for a more representative coverage of input probability distributions through stratification, reducing the standard error of simulation outputs. Two methods of implementation are examined, one where stratification is applied to headways and routing decisions of individual vehicles and another where vehicle counts and entry times are more evenly sampled. The proposed methods have wider applicability in general queuing systems. LHS is found to outperform AV, and reductions of up to 71% in the standard error of estimates of traffic network performance relative to independent sampling are obtained. LHS allows for a reduction in the execution time of computationally expensive microscopic traffic simulators as fewer simulations are required to achieve a fixed level of precision with reductions of up to 84% in computing time noted on the test cases considered. The benefits of LHS are amplified for more congested networks and as the required level of precision increases.

A fingerprint method for variability and robustness analysis of stochastically controlled cellular actuator arrays

In this paper we present a ‘fingerprint method’ for modeling and subsequently characterizing stochastically controlled actuator arrays. The actuator arrays are built from small actuator cells with structural elasticity. These cells are controlled using a bistable stochastic process wherein all cells are given a common input probability (control) value which they use to determine whether to actuate or relax. Arranging the cells in different networks gives different actuator array properties, which must be found before the actuator arrays can be applied to manipulators. The fingerprint method is used to describe and automatically generate every possible stochastic actuator array topology for a given number of cells, and to calculate actuator array properties such as: travel, required actuator strength/displacement, force range, force variance, and robustness for any array topology. The properties of several illustrative examples are shown and a discussion covers the importance of the properties, and trends between actuator array layouts and their properties. Finally, results from a validation experiment using a stochastically controlled solenoid array are presented.