ABSTRACT

Today the industries face many problems in mass productions. These issues

give rise to methodological studies and implementations of different production

systems. Assembly line production is one of the widely used basic principles in

production systems. These problems deals with distribution of activities among

different work stations with cost effective minimum balancing losses

Balancing assembly lines becomes one of the most important part for the

industrial manufacturing systems. The success of archiving goal of production is

influenced by balancing assembly. There are many best methods and techniques

are implemented to keep the line balanced

A brief literature on these issues of line balancing has been so far published

but they mainly deal with minimization of idle time. This paper deals with all the

methodologies of line balancing and its implementation. A brief study on all

balancing approach such as largest candidate rule (LCR) method, kill bridge&

wester column (KWC) method, ranked position (RPW) method have been dealt

with. The Proper selection of particular method has been choosed for the line

balancing.

Further study of line balancing is done by genetic algorithm, hybrid

algorithm, ant and bee colony algorithm and simulated annealing algorithm

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CHAPTER 2

INTRODUCTION

2.1 ASSEMBLY LINE

An assembly line is a manufacturing process (most of the time called a

progressive assembly) in which parts (usually interchangeable parts) are added as

the semi-finished assembly moves from work station to work station where the

parts are added in sequence until the final assembly is produced. By mechanically

moving the parts to the assembly work and moving the semi-finished assembly

from work station to work station, a finished product can be assembled much faster

and with much less labor than by having workers carry parts to a stationary piece

for assembly.

Assembly lines are the common method of assembling complex items such

as automobiles and other transportation equipment, household appliances and

electronic goods. Assembly lines are designed for the sequential organization of

workers, tools or machines, and parts. The motion of workers is minimized to the

extent possible. All parts or assemblies are handled either by conveyors or

motorized vehicles such as forklifts, or gravity, with no manual trucking. Heavy

lifting is done by machines such as overhead cranes or forklifts. Each worker

typically performs one simple operation.

Assembly Line Balancing (ALB) is the term commonly used to refer to the

decision process of assigning tasks to workstations in a serial production system.

The task consists of elemental operations required to convert raw material in to

finished goods. Line Balancing is a classic Operations Research optimization

technique which has significant industrial

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importance in lean system. The concept of mass production essentially involves the

Line Balancing in assembly of identical or interchangeable parts or components

into the final product in various stages at different workstations. With the

improvement in knowledge, the refinement in the application of line balancing

procedure is also a must. Task allocation of each worker was achieved by assembly

line balancing to increase an assembly efficiency and productivity.

2.2 TYPES OF LINE BALANCING

2.2.1 LINE BALANCING

Line Balancing is leveling the workload across all processes in a cell or

value stream to remove Bottlenecks and excess capacity. A constraint slows

theprocess down and results if waiting for downstreamoperations and excess

capacity results in waiting andabsorption of fixed cost.

2.2.1.1 SINGLE-MODEL ASSEMBLY LINE

In early times assembly lines were used in highlevel production of a single

product. But now theproducts will attract customers without any differenceand

allows the profitable utilization of Assembly Lines.An advanced technology of

production which enablesthe automated setup of operations and it is negotiatedtime

and money. Once the product is assembled in thesame line and it won’t variant the

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setup or significantsetup and it’s time that is used, this assembly system iscalled as

Single Model Line.

2.2.1.2MIXED MODEL ASSEMBLY LINE

In this model the setup time between the models would be decreased sufficiently

and enough to

be ignored. So this internal mixed model determines the assembled on the same

line. And the type of assembly line in which workers work in different models of a

product in the same assembly line is called Mixed Assembly Line.

2.2.1.3 MULTI MODEL ASSEMBLY LINE

In this model the uniformity of the assembled products and the production system

is not that much sufficient to accept the enabling of the product and the production

levels. To reduce the time and money this assembly is arranged in batches, and this

allows the short term lot-sizing issues which made in groups of the models to

batches and the result will be on the assembly levels.

2.3 HEURISTIC METHODS OF LINE BALANCING

Moodie -Young Method

Killbridge and Wester Heuristic

Hoffmans or Precedence Matrix

Immediate Update First Fit Method

Ranked Position Weighted Method (RPW)

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2.4 ALGORITHM USED IN LINE BALANCING

2.4.1 GENETIC ALGORITHM

Genetic algorithm (GA) is a search heuristic that mimics the process of natural

selection. This heuristic (also sometimes called a meta heuristic) is routinely used

to generate useful solutions to optimization and search problems. Genetic

algorithms belong to the larger class of evolutionary algorithms (EA), which

generate solutions to optimization problems using techniques inspired by natural

evolution, such as inheritance, mutation, selection, and crossover.

2.4.2 BEES ALGORITHM

Bees Algorithm is a population-based search algorithm which was developed in

2005. It mimics the food foraging behavior of honey bee colonies. In its basic

version the algorithm performs a kind of neighborhood search combined with

global search, and can be used for both combinatorial optimization and continuous

optimization. The only condition for the application of the Bees Algorithm is that

some measure of topological distance between the solutions is defined. The

effectiveness and specific abilities of the Bees Algorithm have been proven in a

number of studies.

2.4.2 SIMULATED ANNEALING

Simulated annealing (SA) is a generic probabilistic metaheuristic for the global

optimization problem of locating a good approximation to the global optimum of a

given function in a large search space. It is often used when the search space is

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discrete (e.g., all tours that visit a given set of cities). For certain problems,

simulated annealing may be more efficient than exhaustive enumeration —

provided that the goal is merely to find an acceptably good solution in a fixed

amount of time, rather than the best possible solution.The name and inspiration

come from annealing in metallurgy, a technique involving heating and controlled

cooling of a material to increase the size of its crystals and reduce their defects.

Both are attributes of the material that depend on its thermodynamic free energy.

LITERATUR REVIEW

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3.1 Scope of the research

The literature review of this research analysis is based on types of assembly line,

algorithm used to solve line balancing

3.1.1JOURNALS ON BASED ON TYPES OF LINE BALANCING

Nai-Chieh Wei.et al [1].Type E simple assembly line balancing problem (SALBP-

E) that combines models SALBP-1 and SALBP-2. This study develops the

solution for the proposed model.Testproblems were taken from a website

established byScholl, Boysen, Fliedner, and Klein (1995).SALBP-E model by

combining SALBP-1 and SALBP-2 models. The proposed model minimizes the

total idle time to optimize the assembly line balancing efficiency.To achieve a

faster solution, the proposed model adds two variables, Ei and Li, and re-defines

the model of SALBP-2test problems with a task number ranging from 11 to 53; the

proposed method is capable of obtaining the optimal range of workstations

Jaydeep Balakrishna.et al [2].this paper study of U-assembly line is made . in this

paper 13 single-pass heuristics and effectiveness of various heuristics under

different problem conditions are studied. An extensive computational study is

carried out to help identify the best heuristics. Comparing of recent U-line

procedures with a single-pass heuristic using some literature problems were made.

Basedon a single-pass heuristic, we compare the configurations of a straight- and

U-line. The computational stydy is made on c and made to run on sun workstation

platform.The Excellentgroup includes MAXDUR and MAX (DUR/UB); the poor

group includes MAXIFOL, RANDOM, MINLB, MINTSKNO; and the fair group

includes all remaining heuristics including MAXRPW,

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MAXTFOL, MINTSLK, MINUB, MAXAVGRPW, MIN(UB/TFOL) and

MAX(TFOL/SLK).

DanialKhorasanian et al[3].This paper, suggest an index for calculating the value

of the relationship between each two tasks, and it defines the performance

criterion is called ‘assembly line tasks consistency’ for calculating the average

relationship between the tasks assigned to the stations of each solution. In this

paper simulated annealing algorithm is used for solving the two-sided assembly

line balancing Problems. They Consider the three performance criteria are number

of stations, number of mated-stations, and assembly line tasks consistency. Also,

the simulated annealing algorithm is modified for solving the two-sided assembly

line balancing problem without considering the relationships between tasks. This

modification give a five new best solutions for the number of stations performance

criterion and ten new best solutions for the number of mated-stations performance

criterion for benchmark instances

Yeo Keunet al.[4] In this two-sided assembly line the line is often producinglarge

products such as trucks and buses. This paper presents a mathematical model and a

genetic algorithm (GA) for two-sided assembly line balancing (two-ALB). The

mathematical model can be used as a foundation for further practical development

in the design of two-sided assembly lines. The GA,experimental results show that

the proposed GA outperforms the heuristic method

3.1.2 JOURNALS BASED ON TYPE OF ALGORITHM USED

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SenerAkpınarG.at al [5].Hybrid genetic algorithm is used solve mixed model

assembly line balancingproblem of type I (MMALBP-I). There are three objectives

which are achieved: to minimize the number of workstations, maximize the

workload smoothness between workstations, and maximize the workload

smoothness within workstations. The proposed approach is able to address some

particular features of the problem such as parallel workstations and zoning

constraints. The genetic algorithm may lack the capability of exploring the solution

space effectively.so hybrid genetic algorithm is used.hybridize the traditional GA

with three well known heuristics in order

to improve its performance for large size MMALBP-I with parallel workstations

and zoning constraints and traditional method like WESTER& KILLBRIDGE,

RPW AND MOODIE YOUNG method were studied in paper and

compared.Besides minimization of number of workstations, maximizing workload

smoothnesses between workstations and within workstations were also considered

R.B. Breginski, et al [6].Study was conducted at a large multinational enterprise

of the automotive industry, located in the state of Paraná. In this paper due to

information confidentiality, specific characteristics of the enterprise and its

products will not be detailed. In this paper eight method of heuristics were stdied

and compared The assembly Line consists of five stations, where 23 operators

work. Comparison of eight methods were discussed below

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Santosh T. Ghutukade etal[7]The main purpose of this paper is to represent use of

RPW method to develop the assembly line and balancing that line. With this study

it is found that RPW method is useful when the less data is available. In

thisStudy in made onKhedkar Tech India, Kolhapur is manufacturing Cashew Nut

Shelling Machine.machine that de overall 90 percent of cashew nuts which

fed in the machine for de-shelling. authors have found out that assembly line

for machine manufacturing assembly Cashew Nut Shelling Machine single

work station, hence time required for the assembly of Cashew Nut Shelling

Machine more. Company has the prospect of mass production. For that purpose,

Ranked Positional weight method (RPW) is proposed and studied .after

implementing the RPW method, production rate was increased by 38% with

36 machines per month.

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Christian Blum et al [8]. simple assembly line balancing problem (SALBP)

concerns the assignment of tasks with pre-defined processing times to work

stations that are arranged in a line. Hereby, precedence constraints between the

tasks must be respected. The optimization goal of the SALBP-2 variant of the

problem concerns the minimization of the so-called cycle time, that is, the time in

which the tasks of each work station must be completed. In this work we propose

to tackle this problem with an iterative search method based on beam search. The

proposed algorithm is able to generate optimal solutions, respectively the best

upper bounds, for 283 out of 302 test cases. Moreover, for 9 further test cases the

algorithm is able to improve the currently best upper bounds.

NedaManavizadeh,et al [9]. In the study balancing a mixed-model U-line in a Just-

In-Time (JIT) production system. The research intends to reduce the number of

stations via balancing the workload and maximizing the weighted efficiency,

which both are considered as the objectives of this research paper. After balancing

the line and determining the number of stations, the labor assignment paper. To

solve this problem, Simulated Annealing algorithm was applied and followed in

three stages. First, the balancing problem was solved and the number of stations

was determined. Second, workers were assigned to the workstations in which they

are qualified to work. Following that, an alert system based on the kanban system

was designed to balance the work in the process inventory.SA algorithm still have

a better reliability. To show the

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efficiency of the proposed SA algorithm, an axel assembly company was studied.

To satisfy demands and reduce backlogging, a mixed model assembly line was

designed for this case study. The results shows that

mixed model assembly line designed using the SA algorithm had good efficiency

Pavel A. Borisovsky et al [10].roblem of designing a reconfigurable machining

line. Such a line is composed of a sequence of workstations performing specific

sets of operations. Each workstation is comprised of several identical CNC

machines (machining centers).A genetic algorithm is proposed. This algorithm is

based on the permutation representation of solutions.Computational experiments

confirmed the suitability of this approach. The results on relatively small-sized test

instances showed quite competitive performance of the GA comparing to CPLEX

solver.Results are quite comparable, in all cases but two the GA was at least as

good as CPLEX, and in three cases it found better solutions.

Jing Zha et al [11].U-line rebalancing problem is formalized and to have a

minimum moving cost of machines and labor cost. The walking time of

operators is considered to avoid generating awkward walking path. A new

hybrid algorithm of ant colony optimization and filtered beam search is

presented to solve the problem. The hybrid algorithm adopts the framework of

ant colony optimization. In the process of constructing path, each ant

explores several nodes for one step and chooses the best one by global and

local evaluation at a given probability. Computational results show that the

proposed algorithm performs

quite effectively for solving U-line balancing problems in the literature by

comparing to the existing solutions. Finally, the proposed algorithm for

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solving U-line rebalancing problem is demonstrated with an example and

also yields optimal solutions

Pinar Tankan et al [12]. Bees algorithm and artificial bee colony algorithm

have been applied to the fully constrained two-sided assembly line balancing

problem so as to minimize the number of workstations and to obtain a

balanced line. An extensive computational study has also been performed

and the comparative results have been evaluated . Two sided ALB is extended

by

Taking into account additional constraints. Although, the addition

Of positional, zoning and synchronous task constraints constitute

the problem to be much more complex, and these factors are important for

practical applications.further studies, it is aimed to implement different swarm

intelligence based heuristic algorithms, such as ant colony and particle swarm

optimization, with multiple objectives in order to improve the results

presented in this study

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CHAPTER 4

PROBLEM IDENTIFICATION

4.1 PROBLEM IDENTIFICATION:

From the literature survey, problems in line balancing are given and

problems identified are

Improper line balancing

Zone constraint

Smoothness between workstation

To minimize no of work station

4.2 PROBLEM STATEMENT

To increase maximum efficiency of assembly line

To form easy movement of work piece

Reduction of work station

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4.3 METHODOLOGY

CHAPTER 5

15

MATHEMATICAL MODELING

5.1 Heuristic method for solving line balancing

To experience-based techniques for problem solving, learning, and discovery that

give a solution which is not guaranteed to be optimal. Where the exhaustive search

is impractical, heuristic methods are used to speed up the process of finding a

satisfactory solution via mental shortcuts to ease the cognitive load of making a

decisioN

5.2 PRCEDENCE DIAGRAM FOR THE ASSEMBLY LINE

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The method were studied are

Largest candidate rule

Wester&kill bridge

Ranked positional weight method

Demand= 480 units

Cycle time =10

Model A=20 UNITS

MODEL B=28 UNITS

Tasks 21 and 22 cannot be executed on the same workstation

TASK tA tB

1 9.5 9.5

2 13 9.5

3 4.8 4.8

4 3.3 3.3

5 1.5 1.7

6 4.5 4.1

7 3.6 3.6

8 0.0 2.0

9 12.3 12.3

10 0.0 8.0

11 2.5 4.3

12 4.3 4.3

13 6.5 0.0

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14 1.7 1.7

15 7.0 7.0

16 1.4 1.4

17 7.8 7.8

18 2.9 2.9

19 1.6 1.6

20 7.0 7.0

21 8.7 8.7

22 3.9 4.1

23 6.4 6.4

24 2.8 2.7

25 8.5 8.5

26 6.7 6.7

27 1.9 1.9

28 9.9 9.9

29 4.6 0.0

30 4.0 4.2

5.3 TOTAL TASK TIME

TASK ELEMENT A ELEMENT B TOTAL TIME

1 190 266 456

2 26 36.4 62.4

3 96 134.4 230.4

4 66 92.4 158.4

5 30 47.6 77.6

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6 90 114.8 204.8

7 72 100.8 172.8

8 0.0 56 56

9 246 344.4 590.6

10 00 224 224

11 50 70 120

12 86 120.4 206.4

13 130 00 130

14 34 47.6 81.6

15 140 196 336

16 28 39.2 67.2

17 156 218.4 374.4

18 58 81.2 139.2

19 32 44.8 76.8

20 140 196 336

21 174 243.6 417.6

22 78 114.8 192.8

23 128 179.2 307.2

24 56 75.6 131.6

25 170 238 408

26 134 187.6 321.6

27 198 277.2 475.2

28 38 53.2 31.2

29 92 0 92

30 `80 117.6 197.6

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5.4 WESTER &KILLBRIDGE METHOD

FROM ABOVE TABLE NO OF WORKSATATION FOR THIS METHOD IS

ALIGNED

STATION ELEMENT

1 1

2 10,2

3 3,12

4 17,5

5 4,11,16

6 13,18

7 6,7,4

8 19,15

9 20,8

10 9,24

11 21

12 25

13 22

14 26

15 23,27

16 28

17 29,30

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5.5 RANKED POSTIONAL WEIGHT METHOD

RPW solution represents a more efficient way toassign the work elements to

station than any other methods mentioned above. In RPW method, one can

assign cycle time and then calculate the work stations required for production

line or vice versa. This

cannot be done in any other method of line balancing

STATION ELEMENT

1 2,12,11

2 3,16

3 13,14

4 20

5 1

6 5,6,4

7 10

8 7,8,24

9 21

10 9,15

11 25

12 22

13 26

14 23,27

15 17

16 28

17 29,18,19

21

18 30

5.6 LARGEST CANDIDATE RULE METHOD

STATION ELEMENT

1 1

2 10,2

3 3,12

4 17,5

5 4,11,16

6 13,18

7 6,7,14

8 18,15

9 20,8

10 9,24

11 21

12 25

13 22

14 26

15 23,27

16 28

17 29,30

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CHAPTER 6

CONCLUSION AND INFERENCE

We can infer from outputs from literature review that line balancing problems were

solved by heuristic methods and algorithm

Fromthis paper, ranked positional weight method is better than largest candidate

rule method and wester &kills bridge method. Further study can be done using the

algorithm for the assembly line balancing problems.the number of workstations

found by the Kilbridge&Wester Heuristic, Phase-I of Moodie&Young Method, and

RPWT Technique are greater.

CHAPTER 7

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