Abstract:
The role of assembly lines has never been more critical as it is now
with the world entering the 4th Industrial Revolution, commonly referred to as
Industry 4.0. If the focus of the previous industrial revolution was on mass production,
the focus of Industry 4.0 is on mass customization. One of the major
changes mass customization brings about to an assembly line is the need for them
to be autonomous. An autonomous assembly line needs to have the following key
features; ability to provide a ubiquitous input, the ability to optimize the model in
real time and achieve product variety. Product variety, in this context, refers to
different variants of the same product as determined by the user. Assembly lines
that make provision for introducing product variety are termed as mixed-model
assembly lines. Mixed-model assembly lines become stochastic in nature when
the inputs are customized as time cannot be predetermined in a stochastic process.
The challenge, as it stands, is that there are limited discussions on real-time
optimization of mixed model stochastic assembly lines. This paper aims to
highlight this challenge by considering the case study of optimizing a mixed
model assembly line in the form of a water bottling plant. The water bottling plant,
which needs to produce two variants of the bottled water, 500 ml, and 750 ml,
takes customer inputs through a web interface linked to the model, thereby making
it stochastic in nature. The paper initially details how the model replicating the
functioning of the water bottling plant was developed in MATLAB. Then, it
proceeds to show how the model was optimized in real time with respect to certain
constraints. The key results of the study, among others, showcase how the optimization
of the model is able to significantly reduce production time.
