This document discusses optimal assembly line balancing. It defines assembly line balancing as assigning tasks to workstations while complying with precedence relationships and minimizing idle time. There are two types of assembly line balancing problems: SALBP-1 focuses on minimizing workstations given a cycle time, while SALBP-2 focuses on minimizing cycle time given a number of workstations. The document presents the tabu search algorithm as an AI technique for solving these assembly line balancing problems, which generates random solutions within a search radius to find an optimal balance.

1. OPTIMAL ASSEMBLY LINE
BALANCING
By
SUNIL KUMAR K.P.
IEM, BMSCE.

2. Introduction
• The manufacturing assembly line balancing was
first introduced in the early 1900’s.
• There are 2 types of assembly line balancing
problems.
-SALBP-1.
-SALBP-2.
• The tabu search (TS) is one of the most powerful
AI search techniques,which is used to solve the
assembly line problems.

3. What is Assembly Line
Balancing (ALB)?
ALB is the procedure to assign tasks to workstations
so that:
• Precedence relationship is complied with.
• No workstation takes more than the cycle time to
complete.
• Operational idle time is minimized.

4. Definition of ALB:
• An Assembly line is a sequence of work
stations connected together by material
handling.
• Goal of ALB:
-minimize number of work stations.
-minimize work load variance.
-minimize idle time.
-maximize the line efficiency.

5. Assembly line Principles:
• Division of labor principle.
• Interchangeable parts principle.
• Material workflow principle.
• Line pacing principle.

6. How to Balance a Line:
• Specify the task relationships and their order of
precedence.
• Draw and label a precedence diagram.
• Calculate the desired cycle time (Cd).
• Calculate the theoretical minimum number of
workstations (N).

7. How to Balance a Line(cont’d)
• Group elements into workstations recognizing
cycle time & precedence.
• Evaluate the efficiency of the line (E).
• Repeat until desired line efficiency is reached

8. Cycle Time :
• Calculate the desired cycle time (Cd).
– If Joe’s Sub Shop has a demand of 100 sandwiches
per day.
– The day shift lasts 8 hours.
Cd =
production time available
desired units of output
Cd =
8 hours x 60 minutes/hour
100 sandwiches
Cd = 4.8 minutes

9. Minimum Work Stations:
• Calculate the theoretical minimum number of
workstations (N).
– If Cd = 4.8 minutes
N =
S ti
Cd
j
i =1
ti = completion time for
task i
j = number of tasks
Cd = desired cycle time

10. Line Efficiency:
• Evaluate the efficiency of the line (E).
– If Cd = 4 minutes and N = 4 work stations.
E =
j
S ti
i =1
N Cd
ti = completion time for
task i
j = number of tasks
Cd = actual cycle time
N = actual number of
workstations

11. A Tabu Search Procedure:
• For SALBP-1:Given the cycle time c,
minimize the number N of work stations.
• For SALBP-2: Given the N of work
stations, minimize the cycle time.

12. Ts algorithm:
• Step1. Initialize a search space (), TL, search
radius (R), count, and countmax.
• Step2. Randomly select an initial solution So from
a certain search space . Let So be a current local
minimum.
• Step3. Randomly generate N solutions around So
within a certain search radius R. Store the N
solutions, called neighborhood, in a set X.

13. Ts algorithm (cont’d):
• Step4. Evaluate a cost function (or the objective
value) of each member in X via the objective
function. Set S1 as a member that gives the
minimum cost in X.
• Step5. If f(S1) < f(S0), put S0 into the TL and set
S0 = S1, otherwise, store S1 in the TL instead.
• Step6. If the termination criteria are met (count =
countmax) stop the search process. S0 is the best
solution, otherwise go back to step 2.

14. Case Study:a company of the
Fiat group
• a company of the Fiat group, plays a
leading role in the production of industrial
Vehicles and diesel engines.
• This group includes Lancia special vehicles,
Unic, & Magirus.
• it manufactures many types of industrial
vehicles, fire fighting & military vehicle

15. The Solution: By Fiat group.
• Guided definition of assembly constraints based
on product characteristics.
• Establishment of optimal sequence for assembly
lines, using appropriate GA.
• Easy & quick modeling of new products and
implementation for new plants.
• The simple and user-friendly interface allows for
visual review of planning effectiveness.

16. Conclusion:
• Simply Assembly Line Balancing is a valid
method to optimize assembly lines. By using tabu
search we can easily optimize the ALB problems.
balanced assembly line help to increase the
productivity of organization.

17. THANK YOU

18. References:
• [1] ISSN 1750-9653, England, UK
International Journal of Management
Science and Engineering Management Vol.
3 (2008) No. 1, pp. 3-18.
• [2] www.springerlink.com,
SPRINGLERLINK. Istanbul technical
University, Istanbul turkey(16/02/09).