An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case

Abstract

Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real factories. Decision support systems can provide productive tools for production planners to offer a feasible and prompt decision in effective and robust production planning. In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry. In the presence of existing multisource uncertainties, the results of applying the proposed method in the selected case show that the accuracy of daily production planning increases in comparison with the existing method.

A two-stage stochastic rule-based model to determine pre-assembly buffer content

“…To recover the altered sequence due to both intentional and unintentional reasons, a preassembly buffer, i.e., painted body stock (Mehrjoo and Bashiri 2013), is located between paint and FA departments. This buffer restores the sequence by (i) positions of vehicles in the altered sequence, resequencing function and (ii) inserting spare vehicles instead of defective vehicles or late vehicles which fell behind more than buffer capacity, so it is not possible to change the position of a vehicle by just resequencing, storage function.…”

“…Extending these researches on pull-off table, Boysen and Zenker (2013) propose exact and metaheuristic methods to decide FA entrance sequence under minimization of FA constraint violation for mix-bank buffer. Mehrjoo and Bashiri (2013) propose a decision support system to predict the production capability of an automobile company for a given production plan based on the historical shop floor data. They consider the stochastic nature of the production environment in terms of defect occurrences, machine breakdowns, etc.…”

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

“…For more details of PCA method, and its application in cell formation relevant literature such as (Albadawi et al 2005;Chattopadhyay et al 2012;Gupta et al 2012;Hachicha et al 2006Hachicha et al , 2008aLlin et al 2010;Kumar and Jain 2010;Mehrjoo and Bashiri 2013;Min et al 2014) and others can be referred.…”

“…These components recover as much variability in the data as possible and account for near total variance of the data. Principal component analysis is recommended for large sample sizes (Gupta et al 2012;Hachicha et al 2008a;Mehrjoo and Bashiri 2013).The usual progression of PCA starts with the eigenvalues and eigenvector of semi-definite matrix. A brief description on implementation of PCA is as follows:…”

Determining production level under uncertainty using fuzzy simulation and bootstrap technique, a case study

“…Zhang et al (2010) solved a production planning problem with seasonal variable demand. Mehrjoo and Bashiri (2013) proposed a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. Zahraee et al (2014) applied computer simulation to analysis manufacturing system in order to improve the productivity.…”