MIXED MODEL PRODUCTION LINES
Coimbatore Selvraj Kanagaraj
Masters of Science Graduate Student
Submitted in Partial Completion of the Requirements of
INDEN 5303
Advanced Manufacturing Systems Design
This paper was developed to assist students in partial fulfillment of course
requirements. No warranty of any kind is expressed or implied. Readers of this
document bear sole responsibility for verification of its contents and assume any/all
liability for any/all damage or loss resulting from its use.
TABLE OF CONTENTS
ABSTARCT ....................................................................................................................... 1
Keywords ........................................................................................................................ 1
INTRODUCTION TO MIXED MODEL PRODUCTION LINES.............................. 1
THE EVOLUTION OF SHORT-TERM PRODUCTION PLANNING
TECHNIQUES .................................................................................................................. 2
Optimization era ............................................................................................................ 2
Heuristic era ................................................................................................................... 3
Artificial intelligence era ............................................................................................... 3
Interactive schedulers era ............................................................................................. 4
IMPORTANCE OF MIXED MODEL PRODUCTION LINE SEQUENCING ........ 5
SOME MAJOR GOALS OF MIXED MODEL PRODUCTION/ASSEMBLY LINES ...................... 5
PROBLEM STATEMENT .............................................................................................. 7
DESIGN OF AN AUTOMATED SEQUENCING SYSTEM (ASS) ........................................... 8
STEPS FOR SEQUENCING AND LINE CONTROL ................................................................ 9
BENEFITS OF PROPOSED SYSTEM ........................................................................ 10
LIMITATIONS OF THE PROPOSED SYSTEM ....................................................... 10
FUTURE ISSUES ........................................................................................................... 11
CONCLUSIONS ............................................................................................................. 11
BIBLIOGRAPHY ........................................................................................................... 12
LIST OF FIGURES
FIGURE 1. ......................................................................................................................... 4
Page i
FIGURE 2. ......................................................................................................................... 7
FIGURE 3. ......................................................................................................................... 8
LIST OF TABLES
TABLE 1. ........................................................................................................................... 3
Page ii
ABSTRACT
Mixed model production lines have become more prevalent in today’s manufacturing
facilities. The production planning techniques used in optimizing these lines have also
developed to fulfill the requirements of the drastically changing manufacturing
environment. An overview on the history and development of these techniques is
provided. The mixed model production line sequencing is taken into consideration in this
paper and its importance and goals are discussed. A production line in a manufacturing
line is taken and these techniques are implemented to show the its advantages and
benefits in this dynamically changing manufacturing environment.
Keywords:
Production, Sequencing, Mixed-model production lines.
INTRODUCTION TO MIXED MODEL PRODUCTION LINES
Due to the unpredictable change in today’s market, manufactures are under a constant
pressure to operate their production units having every single line capable of making
several different parts for a given period of time. These single lines capable of making
several different parts for a given period of time are called as the mixed model production
lines.
Mixed model production lines are similar to mixed model assembly lines are adopted in
many manufacturing facilities today for production flexibility. These production lines
help the manufacturing facility to meet the diverse demand of the consumer market. Due
to the changes in demand the production lines experience an uneven flow of workload.
Hence, it is very essential to keep the load on the production lines equally spread.
Processes like scheduling, sequencing and balancing are used to keep the load spread
equally in mixed model production lines.
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Single-model lines are used to assemble large numbers of a product whereas multi-model
lines are used to assemble different models of the same general product in batches with
large lot sizes. Mixed-model lines are used to assemble different models of a product; the
models are launched to the line one after another [1]. When some different types of
products are manufactured at the mixed-model assembly line and assembly times are
significantly different among these product types, the production efficiency usually
reduces due to the line stoppage [2, 3].
These mixed model production lines use various production planning techniques to
achieve the goals of today’s manufacturing facilities e.g. to function without stoppages.
These production-planning techniques use different mathematical equations and formulas
and algorithms to deliver the optimal solution.
THE EVOLUTION OF SHORT-TERM PRODUCTION PLANNING
TECHNIQUES
The evolution of short-term production planning techniques followed through four
principal eras. These eras are divided as

Optimization,

Heuristic,

Complexity,

Interactive schedulers periods [4].
These techniques, which evolved in various periods of time, with their various attributes
like control, approach and techniques are represented in the table below
Optimization era:
The Seventies and Eighties were the ages of Computer Integrated Manufacturing. Its
characteristic was strongly a hierarchical top-down control and full automation of
manufacturing like integrated optimization. The application of this technique was not a
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great success in the real world due to differences in mono-objective approach and multiobjective approach [4].
Era
Control
Optimization
Approach
Hierarchical
Automatic
Technique
Optimization or
Heuristic
Heuristic
Hierarchical
Automatic
Heuristic
Artificial Intelligence
Hierarchical
Automatic
Heuristic
Neural Networks
Hierarchical
Automatic
Heuristic
Genetic Algorithms
Hierarchical
Automatic
Heuristic
Autonomous Agents
Hierarchical
Automatic
Heuristic
Interactive Schedulers
Distributed
Interactive
Heuristic + operator
Complexity
Table 1. Synoptic table of scheduling techniques [4]
Heuristic era:
Next came the heuristic era, in the Eighties. It concentrated on modeling of reality and an
efficient decision support instrument. Due to its static nature and inefficiency in
interpreting all possible failures and events the focus was on a more dynamic
techniques[4].
Artificial intelligence era:
Then came the artificial intelligence era, also known as virtual manufacturing era,
extending from the second half of the Eighties to the present. This era has given birth to
different techniques like
a) Expert systems,
b) Neural networks,
c) Genetic algorithms,
d) Autonomous agent architectures.
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These techniques seem to be the natural (but yet complex) answer to the need of
interpreting complex manufacturing systems. The main draw back of this era was the
complexity of the systems [4].
Interactive schedulers era:
Nineties are the ages of lean/agile/versatile manufacturing. The limitations of CIM
paradigm, on one hand and the heuristic approach on the other, have spurred the search of
simplicity in short-term production planning system [4].
This is the era of interactive schedulers: they are the simplest kind of scheduling system
because the plan is not made by a machine but the planner himself, while the system
checks for the feasibility of the decision makers choices [4].
Lowly dynamic techniques
Highly dynamic techniques
Effectiveness
Effectiveness
(optimality)
(optimality)
CIM
Artificial
intelligence
Interactive
schedulers
Heuristics
Efficiency
(processing time)
Efficiency
(processing time)
Fig. 1. Short-term production planning techniques [4].
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Fig. 1 classifies the above-presented techniques according to their dynamism (i.e. the
possibility of adapting technique application to the environment evolution) and their
trade-off between effectiveness and efficiency [4].
IMPORTANCE OF MIXED MODEL PRODUCTION LINE
SEQUENCING
Computer-Integrated Manufacturing (CIM) is defined as,
“Systems which enable the integrated, rationalized design, development,
implementation, operation and improvement of production facilities and their output
over the life cycle of the product. These systems identify and use appropriate
technology to achieve their goals at minimum cost and effort” [5].
The above definition clearly states the importance design, and improvement of the
production facilities role in enabling system to achieve its goals.
Here mixed model production lines help in reducing inventories, improving the overall
efficiency and increasing profits. The main purpose of mixed models on the production
line is to keep the constant usage of every part of the production line. It supports the justin-time (J.I.T) concept of Toyota’s production system.
SOME MAJOR GOALS OF MIXED MODEL PRODUCTION/ASSEMBLY LINES
Mixed-model assembly lines are widely used in many manufacturing firms to meet
diversified demands of consumers without possessing large product inventories.
Sequencing products to be manufactured at the mixed-model assembly line is recognized
as an important work for improving its performance [6, 7, 8]. The following three
different objectives of sequencing of mixed model production lines:
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a) To keep a constant rate of part usages,
b) To level the loads (assembly times) at each work station through the line, and
c) To keep a constant rate of feeding products into the line [9].
Sequencing for mixed models to be assembled on the conveyor is recognized as an
important work for improving the performance of an assembly line or production line.
The sequence may vary, depending on the goals of a company [10]. Some of the major
goals are
1. The first goal is to level the load (total operation time) at each workstation on the
assembly/production line, so as to maximize the operators' efficiency or minimize the risk
of stopping the conveyor. This goal has been discussed by, for example, Yano and
Rachamadugu [11].
2. The second goal is to keep the constant usage of every part used in the
assembly/production line, which is a good way of fitting the just-in-time (JIT) concept in
Toyota production system. This goal was discussed by Bautista, Companys and
Corominas [12], Leu, Matheson and Rees [13], Miltenburg and Sinnamon [14, 15, 16],
Monden [17], Morabito and Kraus [18], Steiner and Yeomans [19], Sumichrast and
Clayton [20], Sumichrast and Russell [21] and Sumichrast, Russell and Taylor [22].
3. The third goal is to keep the constant feeding of every model fed into the
assembly/production line, which is derived from the second goal. This goal has been
discussed by, for example, Kubiak [23].
4. The fourth goal is to minimize the total conveyor stoppage time, which emerged from
the autonomation concept in the Toyota production system. This goal has been discussed
by, for example, Xiaobo and Ohno [24].
To fulfill these goals in today’s manufacturing industry, many algorithms are developed
for scheduling, sequencing, and batching of mixed model production lines.
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PROBLEM STATEMENT
In a manufacturing facility, which is involved in fabrication of sheet metals, has a
production line consisting a press shop and a surface treatment zone among various other
units. Conveyors are used for handling the parts that flow between the press shop and the
surface treatment zone. There are six conveyers that run through three baths in the
surface treatment zone. The conveyer pauses till the parts in the baths get processed. The
manual operation carried out in sequencing the parts according to the demand and
process time. Mainly the parts with high demand and low processing times are given
priority. The sequencing operation of the parts is done manually. To avoid the manual
operation of sequencing between the press shop and the surface treatment process, an
automated intelligent sequencing system is designed. Which can sense the type of part
arriving and directed it to the surface treatment zone by considering various factors like
demand and process time.
Processing Lines
Bath 1
Automated
Bath 2
Sequencing
System (ASS)
Bath 3
Press Shop
Surface Treatment Zone
Fig 2. Basic flow of parts
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To accomplish this task, the basic design and an processing steps were borrowed from
Choi Wonjoon and Shin Hyunoh [25] and the model was modified to provide a solution
to the above manufacturing facility.
DESIGN OF AN AUTOMATED SEQUENCING SYSTEM (ASS)
As the fabricated sheets finish the de-burring process and gets transported to the surface
treatment zone it enters the automated sequencing area. Here, to avoid the manual
operation of sequencing between the press shop and the surface treatment process, as well
improve the efficiency; an Automated Sequencing System (ASS) is designed. This
automated sequencing system is similar to the paint body storage (PBS) system used by
Choi Wonjoon and Shin Hyunoh [25]. A system called central production control (CPC)
system maintains the parts information. The information in the central production control
(CPC) system is updated dynamically every day with respect to the market demand. The
automated sequencing system gets information sent by the central production control
(CPC) system and compares it with the part data obtained from the sensor placed at the
entry point of the system. The parts are sequenced with respect to the data obtained from
the central production control system.
Central Production Control (CPC) System
Towards Surface
From Press Shop
Treatment Zone
Processing lines
Programmable Logic Controllers (PLC)
Sensor at Exit
Sensor at Entry
Automatic Sequencing System (ASS)
Fig 2. Automatic Sequencing System
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The sequencing process in based mainly on two conditions:
1) Based on the current demand
2) Based on the processing time
Programmable logic controllers (PLC) receive the data from the automated sequencing
system and direct the parts to the respective process lines. Another sensor is placed at the
end of the line, it is used to sense and the number of parts waiting in queue to get
processed. The feedback from this sensor is taken to the automated sequencing system for
it sequencing process.
STEPS FOR SEQUENCING AND LINE CONTROL
Most of literature in mixed – model sequencing is for a static problem, that is, the initial
creation of the production sequence [17]. An exception is the Toyota’s Goal Coordination
Method, which is an extension of the well-known Goal Chasing Method, and was
implemented with an expert system [22]. A new dynamic sequencing method is
developed to control of the production line [25].
The sequencing and line control algorithm maintains a short list called TDL (To-DoList) which is the linked list of the parts that are already determined to be taken out but
still remaining on the process lines. The algorithm tries to keep constant the number of
the parts in TOL and the bodies moving to or being accumulated at the entry points of
process lines proceeding to the surface treatment zone. Hence, as a part is fed to the
assembly line, additional body is appended to TDL [25].
The steps of the output algorithm are as follows:
1) Select the processing line t.
2) Set L, the list of parts, which can be fed to the processing line t now.
3) Select the best part from L.
4) Insert the selected part to TDL.
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At Step 1, the processing line t is the processing line which has smaller 'depletion time.'
The depletion time of the processing line i (i=1,2) means the length of interval until all
the bodies heading for the assembly line i will be depleted. The depletion time of the
processing line i (i=1,2) is calculated as follows:
(a) + (b) + (c)
.
Throughput rate of the assembly line i per hour
Where
(a) = The number of parts accumulated at the entry point of the Processing line i,
(b) = The number of parts of processing line i moving on the output conveyor,
(c) = The number of parts of the processing line i in TDL[25].
At Step 2, L consists of the bodies, which are located at the forefront among the bodies
not belonging to TDL for each lane. Step 3 is elaborated in the following lines [25].
Since all the parts flowing through the processing lines are of similar dimensions there is
no spacing constraint when parts are assigned to the processing lines. So in Step 3 the
best part is selected with respect to the criterion explained before. In step 4 the selected
part is inserted in the respective processing line, less numbers parts waiting in queue.
BENEFITS OF PROPOSED SYSTEM
In the current system the sequencing is done manually, but the proposed system works in
a more efficiently by using the automated sequencing technique to evenly spreading the
load in the system. The proposed system helps to keep a constant rate of part usages. The
loads (processing times) at each workstation through the line are leveled or equally
distributed and the parts feed into the processing lines are constant, which in turn helps in
maximizing the overall efficiency of the system and reducing costs.
LIMITATIONS OF THE PROPOSED SYSTEM
The current proposed system does not incorporate certain features like variation in the
size of parts arriving into the system, since the spacing constraints were already fixed.
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
Increasing the system capacity.

Adding more processing lines.
Are some of the areas where considerable amount of Research & Development remains
to be analyzed and accomplished.
FUTURE ISSUES
The performance analysis of the proposed system can be simulated using simulation
packages available in the market. The simulation model can be developed with some
experimental conditions and the statistics can be gathered for various conditions. The
simulation study might help in studying the capabilities of the present system under
various testing conditions. From the data obtained further possibilities in improving and
modifying the present system can be analyzed.
CONCLUSION
The mixed model production lines are the diving force in many manufacturing facilities.
Where a variety of models are produced in the same production line [25]. Since there is a
lot of variation in the demand level in today’s business environment, the manufacturing
facility has to be dynamic in its production efforts and have flexible production lines. An
existing production line is taken to find new vistas for improvement. An automated
sequencing system in proposed for the above system. The idea is derived from interactive
schedulers, because it most efficient technique for today’s dynamic environment. This
technique seems to be highly powerful for today’s manufacturing industries. This
technique has a good potential for further research and development in near future for its
extensive utilization.
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