The document summarizes a study on determining the optimal disassembly sequence of a cell phone to minimize the number of workstations and idle times. Data on disassembling 25 parts of a Samsung cell phone by 3 workers was analyzed using a sequencing algorithm in operations research. The analysis found the total estimated time was 154 minutes, with idle times of 9, 15 and 13 minutes for each worker respectively. This allows placing workers to minimize idle times in a disassembly line.

1. OPERATIONS RESEARCH(MEE437)
SLOT C1
FINAL REPORT
ON PROJECT BASED LEARNING
DISASSEMBLY SEQUENCING PROBLEM
A CASE STUDY OF A CELL PHONE
SUBMITTED BY SUBMITTED TO
D.VIKRANTH REDDY PROF:JEEVA P.A
12BME0026
C1 SLOT

2. Disassembly Sequencing Problem: A Case Study of a Cell Phone
ABSTRACT:
(THIS ALL THE DISASSEMBLY IS DONE BY THREE WORKERS IN DIFFERENT TIME FOR DIFFERENT
PARTS)
Selection of an optimal disassembly sequence is essential for the efficient processing of a
product at the end of its life. Disassembly sequences are listings of disassembly actions (such
as the separation of an assembly into two or more subassemblies, or removing one or more
connections between components). Disassembly takes place in remanufacturing, recycling,
and disposal with a disassembly line being the best choice for automation. In this paper, the
disassembly sequencing problem is solved for a cell phone case on a disassembly line,
seeking a sequence which is feasible, minimizes the number of workstations (and hence idle
times), provides for early removal of high demand/value parts, provides the removal of parts
that lead to the access of greatest number of still-installed parts, and early removal of
hazardous parts as well as for the grouping of parts for removal having identical part removal
directions. Since finding the optimal sequence is computationally intensive due to factorial
growth, a heuristic method is used taking into account various disassembly-specific matters.
Using the experimentally determined precedence relationships and task times of areal-world
cell phone, a table of sequencing and a sequencing solution is generated. Finally, Design for
Disassembly (DFD) improvements are recommended with respect to environmentally
conscious manufacturing.

3. INTRODUCTION
Following the industrial revolution, manufacturing started to influence everyday life with
new products rapidly entering the marketplace. Since then, products have been routinely
disposed of in landfills at the end of their lives. Until recently, there was no major concern
over landfill growth or hazardous materials entering the environment, nor was there
arealization of the value of used parts, components and materials. In the last decade, however,
environmentally conscious manufacturing (ECM) and product recovery have became an
obligation for many companies due to new government regulations and consumer interest .
Consequently, companies have grown increasingly interested in how to efficiently
disassemble the products they manufacture. They have invested time, money and engineering
expertise in determining how customers return end of life (EOL) products (reverse logistics)
and how to make the product recovery process profitable,or atleast be conducted at minimum
cost.
Within these researches, numerous ways for efficiently performing disassembly processes
have been proposed. The most promising of these recommendations is the creation of
disassembly lines, similar in concept to assembly lines, but with several significant
differences. Small products, especially those with hazardous and/or valuable parts, are
especially suited to the disassembly line due to their ease of collection, portability, and
typically larger production numbers when compared to large products which are often more
convenient to disassemble in place rather than relocating them on a line. Larger products are
also prone to repair and maintenance as well as modification, making successful and
consistently efficient disassembly on a line more of a challenge.
In the past decade, the use of cellular phones has grown significantly. Due to rapid changes in
usage, technology (e.g.,analog to digital), and features (e.g., color, web access), dozens of
new models regularly enter the market. Consumers have responded by willingly replacing
their old phones with newer technologies, often with their current phone not yet at the end of
its useful life and still fully functional. These unwanted cell phones eventually end up in
landfills and typically contain numerous hazardous parts including: mercury, cadmium, lead,
gallium arsenide and beryllium. These materials,if improperly disposed, can pose a
significant threat to the environment.In this example we will use sequencing problem in
operations research to solve and we will extract the results.

4. Operations research, or operational research in British usage, is a discipline that deals
with the application of advanced analytical methods to help make better decisions. It is often
considered to be a sub-field of mathematics. The terms management science and decision
science are sometimes used as synonyms.
Employing techniques from other mathematical sciences, such as mathematical modeling,
statistical analysis, and mathematical optimization, operations research arrives at optimal or
near-optimal solutions to complex decision-making problems. Because of its emphasis on
human-technology interaction and because of its focus on practical applications, operations
research has overlap with other disciplines, notably industrial engineering and operations
management, and draws on psychology and organization science. Operations research is
often concerned with determining the maximum (of profit, performance, or yield) or
minimum (of loss, risk, or cost) of some real-world objective. Originating in military efforts
before World War II, its techniques have grown to concern problems in a variety of
industries.
SEQUENCING PROBLEM:
It is nothing but finding out the optimal order or sequence in which ‘n’ jobs have to be
processed on definite number of facilities. So that the total elapsed time is minimum.
This is related to waiting line theory and is applicable when the facilities are fixed, but the
order of servicing may be controlled. The scheduling of service or the sequencing of jobs is
done to minimize the relevant costs and time
SEQUENCING PROBLEM ALGORITHM:
Even though we can still confine our attention to permutation schedules when seeking to
minimize the makespan, the three machine problem is already too hard for a general optimal
algorithm. There are many special cases for which simple process have been proposed;almost
all are which simple procedures have been proposed; almost all are situations in which the
middle machine turns out to be an on bottle neck stage: no job ever has to wait for another
1atmachine2.However,experimentshaveshownthatonlyone of these situations is at all likely to
arise in practice (unless the shop has a special structure, such asconstant processing times in
one of the stages). The following condition was found to hold in almost half the randomly
generated test problems.
Define a two-machine problem with p'jl = Pjl + Pj2and p'j2 = Pj2+Pj3' and solve it using
Algorithm. Suppose this produces a makespan of M, while the same jobsequence results in a
makespan of M' for the original three-machine problem. If M' = M+Ijpj 2' then the sequence
is optimal.
Incidentally, the artificial two-machine problem defined above gives reasonable schedules
even when they are not--optimal.

5. FLOW CHART TO DETERMINE SEQUENCING

6. LITERATURE REVIEW:
Brennan, et al. provide an overview of disassembly while Gungor and Gupta present a
survey of environmentally conscious manufacturing and product recovery . Disassembly
optimization using goal programming and considering financial and environmental factors
are demonstrated in and . Veerakamolmal and Gupta analyze design efficiency for the
disassembly of electronic products by building up an index for measuring and comparing
efficiency. Moore et al. use Petri nets with products having complex AND/OR precedence
relationships Gungor and Gupta study disassembly in processes that included possible task
failures and demonstrate how various factors, including component value and hazardous
parts content, could be accommodated to balance a paced disassembly line Lambert provides
a timely and thorough survey of disassembly sequencing [. Metaheuristic techniques are
applied to the disassembly line balancing problem in , and while deterministic algorithms are
developed in MATLAB.
Here we have collected the data of Samsung mobile as mentioned below from a journal.There
are are three employees working for the dissambly of the Samsung mobile.There are twenty
five different types of parts and their dismantling by three employees are given below.By
seeing this table and by using Sequencing operation in Operations Research we will mention
the Total Estimated Time(TET)of all the three workers and idle times individually.This helps
us to place workers accordingly in batching process.
Problem assumptions include the following:
A single product type is to be disassembled on a disassembly line,
The supply of the end-of-life product is infinite, The exact quantity of each part available in
the product is known and constant,
A disassembly task cannot be divided between two workstations,
Each part has an assumed associated resale value which includes its market value and
recycled material value,
Disassembly tasks are to be assigned to a sequence of workstations without violating
precedence relationships among the tasks,and
Complete disassembly is performed on the product.

7. MODEL DESCRIPTION
DATA TAKEN FROM A JOURNAL
The 2001 model year Samsung SCH-3500 is selected for analysis. Collected data on the
SCH-3500 is listed in table
25PARTS WISE
DISASSEMBLY
TIME BY
WORKER 1
TIME CONSUMED
BY WORKER 2
TIME CONSUMED
BY WORKER 3
1.Remove Antenna 3 4 5
2.Remove Battery 2 5 8
3.Discard antenna
Guide path
3 7 4
4.Remove bolt type1A 10 7 4
5.Remove bolt type1B 10 7 6
6.Remove bolt type21 15 7 11
7.Remove bolt type22 15 7 12
8.Remove bolt type23 15 7 13
9.Remove bolt type24 2 7 10
10.Remove clip 2 6 3
11.Remove rubber seal 2 6 3
12.Remove speaker 2 6 4
13.Disconnectwhite
cable
2 7 8
14.Disconnect red/blue
cable
2 4 8
15.Disconnectorange
cable
2 2 3
16.Remove metaltop 2 5 1
17.Remove front cover 2 6 6
18.Remove back cover 3 7 2
19.Remove circuiboard 18 7 10
20.Remove plast screen 5 4 4
21.Remove keyboard 5 3 2
22.Discard lcd 5 5 3
23.Remove sub-keyboard 15 6 3
24.Remove internal IC
board
2 3 4
25.Remove microphone 2 4 4

8. CALCULATION:
Adding Column 1 and Column 2 for X and column 2 and Column 3
for Y
25PARTS WISE
DISASSEMBLY
X(C1+C2) Y(C2+C3)
1.Remove Antenna 7 9
2.Remove Battery 7 13
3.Discard antenna
Guide path
10 11
4.Remove bolt type1A 17 11
5.Remove bolt type1B 17 13
6.Remove bolt type21 22 18
7.Remove bolt type22 22 19
8.Remove bolt type23 22 20
9.Remove bolt type24 9 17
10.Remove clip 8 9
11.Remove rubber seal 8 9
12.Remove speaker 8 10
13.Disconnectwhite
cable
9 15
14.Disconnect red/blue
cable
6 12
15.Disconnectorange
cable
4 5
16.Remove metaltop 7 6
17.Remove front cover 8 12
18.Remove back cover 10 9
19.Remove circuiboard 25 17
20.Remove plast screen 9 8
21.Remove keyboard 8 5
22.Discard lcd 10 8
23.Remove sub-
keyboard
21 9
24.Remove internal IC
board
5 7
25.Remove microphone 6 8
According to sequencing,the order is as shown below
X 15 24 14 25 2 1 17 12 10 11 9 13 3 8 7 6 19 5 4 18 23 20 22 16 21 Y

9. FINAL TABULATION
DISSASSEMBLY
RANKINGS
W1 W1 W2 W2 W3 W3
IN OUT IN OUT IN OUT
15.Disconnect orange cable 0 2 2 4 4 7
24.Remove Internal IC board 2 4 4 7 7 11
14. Disconnect red/blue cable 4 6 7 11 11 19
25. Remove microphone 6 8 11 15 19 23
2. Remove Battery 8 10 15 20 23 31
1. .Remove Antenna 10 13 20 24 31 36
17. Remove front cover 13 15 24 30 36 42
12. Remove speaker 15 17 30 36 42 46
10. Remove clip 17 19 36 42 46 49
11. Remove rubber seal 19 21 42 48 49 52
9. Remove bolt type24 21 23 48 55 55 65
13. Disconnectwhite cable 23 25 55 62 65 73
3. Discard antenna
Guide path
25 28 62 69 73 77
8. Remove bolt type23 28 43 69 76 77 90
7. Remove bolt type22 43 58 76 83 90 102
6. Remove bolt type21 58 73 83 90 102 113
19. Remove circuiboard 73 91 91 98 113 123
5. Remove bolt type1B 91 101 101 108 123 129
4. Remove bolt type1A 101 111 111 118 129 133
18. Remove back cover 111 114 118 125 133 135
23. Remove sub-keyboard 114 129 129 135 135 138
20. Remove plast screen 129 134 135 139 139 143
22. Discard lcd 134 139 139 144 144 147
16. Remove metaltop 139 141 144 149 149 150
21. Remove keyboard 141 145 149 152 152 154
TOTAL ESTIMATED TIME=154mins
IDLE TIME WORKER 1 =154-145=9mins
IDLE TIME WORKER 2=2+1+3+3+4+2=15mins
IDLE TIME WORKER 3=4+3+1+1+2+2=13mins

10. SOFTWARE DEVELOPMENT
A MATLAB program was developed for assigning the cell phone disassembly tasks to a
minimum number of workstations while preserving precedence and addressing the function
weights. MATLAB is a high-level technical computing language and interactive environment
for algorithm development, data visualization, data analysis, and numerical computation.
The program requires the following inputs:
• Precedence relationships,
• Task times,
• Resale value,
• Hazardous binary value,
• Direction of disassembly,
• Number of predecessors to each task,
and provides these outputs:
• The individual tasks assigned to each workstation and their sequence,
• Number of workstations,
• Idle time at each workstation.
RESULTS AND DISCUSSION:
TOTAL ESTIMATED TIME=154mins
IDLE TIME WORKER 1 =154-145=9mins
IDLE TIME WORKER 2=2+1+3+3+4+2=15mins
IDLE TIME WORKER 3=4+3+1+1+2+2=13mins
Thus after seeing the numerical results we extracted above using sequencing problem the
Total estimated time and idle time of employees are individually found out.We can easily
say that the first worker has the capacity of easy and good dismantling.Likewise the idle
times of worker 3 and worker 2 stands next after worker 1.
IDLE TIMES=WORKER 1<WORKER 3<WORKER 2

11. Though not seen with the SCH-3500 cell phone data, an important weakness of the developed
algorithm is its inability to consistently balance the workstations, specifically in the last
workstation. This can be attributed to the use of the Next-Fit rule (developed for the bin-
packing problem) in assigning tasks to workstations, but can be addressed by trial and error
through increasing or decreasing c in increments of 1.
While this heuristic approach to the disassembly sequencing problem cannot assure an
optimum solution, it is able to rapidly generate near-optimum solutions and its result is much
faster than searching the N! permutations required to ensure the optimum sequence.
The SCH-3500 cell phone has already been designed and manufactured for the market.
However, if new productionversions of the SCH-3500 are made, one could make some
improvements in the design for ease of disassembly. Onerecommended improvement would
be a redesign of the antenna guide path. The current antenna guide path design doesnot allow
access to one of the bolts required for disassembly. Thus, until disassembling the antenna and
its guide path,one cannot obtain access to any other internal components, limiting options and
mandating the early removal of thiscomponent.
CONCLUSION
In this paper, a disassembly case study of an existing, contemporary cell phone was
described. A weighted algorithm was developed and programmed in MATLAB that provided
a feasible part removal sequence while attempting to find the sequence having the minimum
number of workstations (minimum idle time) as well as providing consideration for early
removal of high demand/value parts, whose removal provides access to the greatest number
of still-installed parts,hazardous parts, and removing parts with identical part removal
directions adjacent to each other in the problem described above case study.
Although a sub-optimum heuristic, this method quickly generated a feasible solution to this
complex, real-world problemexample, while the experimentally determined data provides a
new evaluation tool for future disassembly algorithmdevelopment.

12. THANK YOU