A survey of simulation modeling techniques in production planning and control (PPC)

The manufacturing sector as a whole has undergone remarkable changes in terms of scale, complexity and technology over the past decades and this applies across most modern high-technology manufacturing such as electronics, semiconductor, aerospace and automotive industries. In order to remain competitive, manufacturers have to produce high-quality products at low cost, and at the same time retain sufficient flexibility and to meet rapidly changing customer demands. Production planning and control (PPC) is a key role which enables the manufacturer to gain visibility and control over all aspects of manufacturing activities. PPC in itself forms a subject of study, within which simulation techniques have proven themselves to be one of the most practical methodologies available to investigate and evaluate manufacturing issues. In this review paper, we focus on state-of-the art applications of simulation techniques in PPC to demonstrate their applicability to modern manufacturing issues. The review reports on academic publications on simulation applications in manufacturing from 2002 to 2014, incorporating surveys of peer-reviewed literature. The review covers three types of simulation techniques (system dynamic, discrete event simulation and agent-based simulation) and eight PPC issues (facility resource planning, capacity planning, job planning, process planning, scheduling, inventory management, production and process design, purchase and supply management). Literature survey is analysed on the basis of simulation application to PPC problems which can give a guideline for simulation technique selection and also can help for simulation modelling in PPC problemsWould you consider changing the term “modeling” to “modelling” in the title. Please check, and correct if necessary.     

A bivariate mixture of negative binomial distributions and its applications

Abstract

We construct a new bivariate mixture of negative binomial distributions which represents over-dispersed data more efficiently. This is an extension of a univariate mixture of beta and negative binomial distributions. Characteristics of this joint distribution are studied including conditional distributions. Some properties of the correlation coefficient are explored. We demonstrate the applicability of our proposed model by fitting to three real data sets with correlated count data. A comparison is made with some previously used models to show the effectiveness of the new model.     

An R package for the simulation of correlated discrete variables

A package for the stochastic simulation of discrete variables with assigned marginal distributions and correlation matrix is presented and discussed. The simulating mechanism relies upon the Gaussian copula, linking the discrete distributions together, and an iterative scheme recovering the correlation matrix for the copula that ensures the desired correlations among the discrete variables. Examples of its use are provided as well as three possible applications (related to probability, sampling, and inference), which illustrate the utility of the package as an efficient and easy-to-use tool both in statistical research and for didactic purposes.     

A discrete/continuous choice model on a nonconvex budget set

Decreasing block rate pricing is a nonlinear price system often used for public utility services. Residential gas services in Japan and the United Kingdom are provided under this price schedule. The discrete/continuous choice approach is used to analyze the demand under decreasing block rate pricing. However, the nonlinearity problem, which has not been examined in previous studies, arises because a consumer’s budget set (a set of affordable consumption amounts) is nonconvex, and hence, the resulting model includes highly nonlinear functions. To address this problem, we propose a feasible, efficient method of demand estimation on the nonconvex budget. The advantages of our method are as follows: (i) the construction of an Markov chain Monte Carlo algorithm with an efficient blanket based on the Hermite–Hadamard integral inequality and the power-mean inequality, (ii) the explicit consideration of the (highly nonlinear) separability condition, which often makes numerical likelihood maximization difficult, and (iii) the introduction of normal disturbance into the discrete/continuous choice model on the nonconvex budget set. The proposed method is applied to estimate the Japanese residential gas demand function and evaluate the effect of price schedule changes as a policy experiment.     

Effectiveness of Job Aids and Post Performance Review on Staff Implementation of Discrete Trial Instruction

Discrete-trial instruction (DTI) is a well-established instructional method for teaching children with autism. Accurate implementation of DTI procedures requires that staff be well trained. This study examined the effectiveness of job aids followed by post performance review of strengths, errors, and accuracy on discrete trial instructional accuracy in three participants who conducted DTI within their current job positions. Additionally, primary training procedures were embedded within a meta-shaping procedure, which involved the gradual and systematic introduction of three levels of behavioral components required for accurate implementation of the DTI sequence with mastery criteria set at 90% accuracy across all three levels. As demonstrated in a changing criterion within a multiple baseline design, staff demonstrated increases in DTI accuracy following the initiation of the job aid condition; however, some degree of performance-based feedback was required to establish high levels of procedural fidelity across the DTI sequence. Performance accuracy on maintenance probes remained at high levels. These findings provide support that job aids followed by performance-based feedback may be an effective and efficient method for shaping high levels of DTI procedural fidelity in staff. Additionally, gradual shaping of the DTI accuracy may scaffold performance, minimize errors, and increase social validity.     

On maxima of chi-processes over threshold dependent grids

In this paper, with motivation from Piterbarg VI [Discrete and continuous time extremes of Gaussian processes. Extremes. 2004;7(2):161–177] and the considerable interest in stationary chi-processes, we derive asymptotic joint distributions of maxima of stationary strongly dependent chi-processes on a continuous time and a uniform grid on the real axis. Our findings extend those for Gaussian cases and give three involved dependence structures via the strongly dependence condition and the sparse, Pickands and dense grids.     

影响系统前端播出质量的外部因素分析

对影响前端播出质量的诸多外部因素加以分析 ,着重阐述了太阳黑子、雨雪天气、干扰源等对前端造成的影响以及预防和解决方法。     

Confidence intervals for discrete distributions based on a single observation

Confidence intervals are developed for the mode of a discrete unimodal distribution in the case where only a single observation is available. These intervals are centered on either the observation, X, or a weighted average of X with a constant, b, chosen by the investigator. Intervals are derived for nonrestricted unimodal distributions, for unimodal distributions with a symmetry property, and for a family of two-sided truncated geometric distributions.     

An information improvement generating function

Here we define an information improvement generating function whose derivative at point 1 gives Theil's measure of information improvement which has wide applications in Economics. It contains Guiasu and Reischeir's relative information generating function and Golomb's information generating function as particular cases. Simple expressions for important discrete distributions have been obtained. It has also been shown that the information improvement generating function suggests a new information indicator as the standard deviation of the variation of information.     

Performance analysis of serial supply chain networks considering system disruptions

The supply chain networks could be very fragile in global environment due to unexpected events such as emergencies, normal disruptions and industrial accidents. The supply chain members may lose their production capacities temporarily, which might have significant impacts on the performance of the whole supply chain network. This article proposes a discrete time model to characterise the unreliable production capacity in serial supply chain networks. Based on the proposed model, the exact probability distributions are available for the performance analysis of a single-stage system in the lost sales scenario. The iterative methods are developed to derive the approximate performance measures for single-stage systems in the backorder scenario and multi-stage systems in both the lost sales and backorder scenarios. The proposed methods are verified through a series of numerical experiments. The analysis results suggest that the performance of the supply chain network suffers more from the downstream-stage unreliability than the upstream-stage unreliability. Furthermore, some application examples are illustrated to show the possible solutions for practical problems.