Optimization by heuristic procedure of scheduling constraints in manufacturing system

Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Print) ISSN 2225-0581(Online)
Vol 1, No.4, 2011

Optimization by Heuristic procedure of Scheduling
Constraints in Manufacturing System

Bharat Chede1*, Dr C.K.Jain2, Dr S.K.Jain3, Aparna Chede4

1. Reader, Department of Mechanical Engineering, Mahakal Institute of Technology and Management,

Ujjain

2. Ex Principal, Ujjain Engineering College, Ujjain

3. Principal, Ujjain Engineering College, Ujjain

4. Lecturer, Department of Mechanical Engineering, Mahakal Institute of Technology, Ujjain

* Email of the corresponding author : bharat_chede@rediffmail.com Phone 91-9827587234

Abstract
This article provides an insight for optimization the output considering combinations of scheduling
constraints by using simple heuristic for multi-product inventory system. Method so proposed is easy to
implement in real manufacturing situation by using heuristic procedure machines are arranged in optimal
sequence for multiple product to manufacture. This reduces manufacturing lead-time thus enhance profit.

Keywords: Optimization, Scheduling, Heuristic, Inventory, Layouts

1. Introduction

Constraints in industry lead to reduction of profit to industry especially some constraints like budgetary
and space when imposed on system. These constraints has impact on cost incurred in that product basically
considering the scheduling as constraints specially in case when we have to adopt in change in product mix,
demand and design.

2. Traditional approach

There are many relations for EOQ

Q = √ 2 Ci .Yi i = 1,2,3 _ _ _n ----------------- (i)
Bi
For ith product Ci = order cost per unit
Yi = demand per unit

11

Vol 1, No.4, 2011

Bi = the stock holding cost per unit per unit time
There are chances when all n products have to be replenished at same time. If restriction of maximum
capital investment (W) at any time in inventory is active, then (A) is valid if Q (i= 1.2. - - - n) satisfy the
constraints
n
∑ Vi. Qi ≤ W ------------------------------ (ii)
i =1

Where V is the value of unit of product i otherwise Q ( i = 1,2, ---- n)

To satisfy (ii) Lagrange multiplier technique is used to obtain modified Q

Q = √ 2 Ci .Yi i = 1,2,3 _ _ _n ----------------- (iii)
Bi + 2µVi

µ - Lagrange multiplier associated with capital investment constraints

But by these also we may not necessarily get optimality.

The proposed simple heuristic rule for staggering of the replenishments of product under equal order
interval method and provide a simple formula for obtaining the upper limit of maximum investment in
inventory we assume that each product is replenished at equal time interval during common order interval T,
i.e. for n product we order at a time interval of T/n and upper limit of maximum investment in inventory
can be obtained by making following arrangement.
Arranging the item in descending order of demand of each item and its unit value (i.e. max [Vi,Yi] is
for i=I) If INV denote the capital investment required at time (j - 1) T/n j = 1,2, --- n then INVj can be
expressed as

n
INVj = T/n ∑ rjk. V k . Y k j = 1,2----n
k=1
rjk = n+k-j if k ≤ j
= k-j if k ≥ j

MLUL = max (INVj) gives upper limit of maximum investment in cycle duration j.

To have exact utilization of invested budget the estimated value of T should be obtained from W = MLUL
Comparison of optimal result by both methods. It is clear that heuristic rule provide better cost performance

12

Vol 1, No.4, 2011

than Lagrange multiplier method.(Table1)

3. Numerical example

Considered the company nearby Indore (India) (Table2) for the Demand rate, order cost, stock holding
cost, Value and EOQ.
Maximum allowable investment W = Rs 15000/- If EOQ used ignoring budgetary constraint, the maximum
investment in inventory would be 17,120/- which is greater than W. To find optimal solution by Lagrange
multiplier technique which when applied gives(Table 3)
Q L1 = 88, Q L2 = 139, Q L3 = 98 Total cost = Rs 3541 when µ = 0.03
From the result it ensures that heuristic rule performs well. Also the proposed rule in easy to implement in
practice.

3.1 In case of Scheduling as constraints we can classify the Layout as

• Liner (single and double row machine layout) (Figure 1)
• Loop Layout (Figure 2)
• Ladder layout (reduces travel distance)(Figure 3)
• The Carousel Layout (Part flow in one direction around loop The Load and unload stations are
placed at one end of loop)(Figure 4)
• The Open Field Layout (Consist of Ladder and Loop)(Figure 5)

Scheduling is considered as Static Scheduling where a fixed set of orders are to be scheduled either using
optimization or priority heuristics also as dynamic scheduling problem where orders arrives periodically.

Process of Solution for problem
Pi be a partial schedule containing i schedule operations.
Si The set of Schedulable operation at stage i corresponding to given Pi
Pj The earliest time at which operation j and s could be started.
Q Earliest time at which operation J, sj could be completed.

4. Priority Rule Used

• SPT (Select operation with minimum processing time)
• MWKR (Most work remaining)

13

Vol 1, No.4, 2011

• Random (Select operation at random)

4.1 Algorithm

Step 1 : Let t=0 assume Pt = {Ǿ}
Step 2 : Determine q* = min {q}and corresponding machine m* on which q* could be realized.
Step 3 : For operation which belongs to S that requires machine m* and satisfy the condition p < q*
identify an operation according to specified priority add this operation to pi for next stage.
Step 4 : For each new partial schedule p t+1 created in step 3 update the data set as follows
i) Remove operation j from Si.
ii) From St+1 by adding the direct successor of operation j from si.
iii) Increment by 1
Step 5 : Repeat step 2 to step 4 for each p t+1 created in steps and continue in this manner until all
active schedules are generated.
Step 6 : From To chart is developed from routing data the chart indicated number of parts moves between
the machines.
Step 7 : Adjust flow matrix is calculated from frequency matrix, distance matrix and cost matrix.
Step 8 : From to Sums are determined from the adjusted flow matrix.
Step 9 : Assign the a machine to it on minimum from sums. The machine having the smallest sum is
selected. If the minimum value is to sum, then the machine placed at the beginning of sequence.
If the minimum value is from sum, then machine placed at end of sequence.

5. Example

Arrangements of machine are considered for Liner layout, loop layout and ladder layout. The input
data required in inter slot distance load unload distances and unit transportation cost, processing times for
different jobs, processing sequence of jobs on different machines.(Table 4)(Table 5)

• Inters lot distance and load, unload matrices for Liner layout (Table 6,7)
• Inter slot distance and load, unload matrix for Loop Layout(Table 8,9)
• Inter slot Distance and Load, Unload Matrices for Ladder Layout (Table 10,11)

6. Results
• Waiting time of machine (Table 12)
• Waiting time for Job (Table 13) Production time for a batch of component = 2600 hrs
• Sequence of job manufactured on different machine for minimum production time.(table 14)
• Machine order and total cost for different types of layouts (Table 15)

7. Conclusion

14

Vol 1, No.4, 2011

The traditional method for determining optimal order quantities and the optimal reorder points in
multi-product inventory system with constraints are not easy to implement in reality but heuristic rule is not
only easy to implement but also give better result than traditional method. By using heuristic procedure
with scheduling as constraint layout is optimized. The other parameters such as flow time, job sequence to
manufactured, Machine sequence, total transportation cost, Machine and job waiting time are determined.

References

Brown G.G, Dell R.F, Davis R.L and Dutt R.H (2002) “Optimizing plant line schedules and an
application at Hidden valley Manufacturing Company”, INTERFACES INFORMS Volume 32
No.-3, pp 1-14.

Oke S.A.,Charles E. and Owaba O.E.(2007) “A fuzzy linguistic approach of preventive
maintenance scheduling cost optimization”, Kathmandu University Journal of science and
Engineering and Technology Volume 1 No.3 January pp 1-13

Osman K, Nagi R. Christopher R.M. (2001 “New Lot sizing formulation for less nervous
production schedules”, Computers and Operation Research 27, pp 1325-1345.

Pegels C.C and Watrous C. (2005) “Application of theory of constraints to bottleneck operation in
manufacturing plant”, Journal of Manufacturing Technology Management. Vol 16 No-03, pp
302-311.

Solimanpar.M,Vrat .P and Shankar R,(2004) “ A heuristic to minimize make span of cell
scheduling problem”, International journal of Production Economics 88,pp 231-241.

First Author. Bharat Chede has received Bachelor’s degree from Amravati University, India and Masters
Degree in Mechanical Engineering (Production Engineering) from Shivaji University, India, He is currently
pursuing PhD from Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India. He is working as Head of
Department (Mechanical Engineering) at Mahakal Institute of Technology and Management Ujjain India.
His Current area of research is Optimization in manufacturing techniques using fuzzy logics.

15

Vol 1, No.4, 2011

Second Author. Dr C.K.Jain Phd in Production Engineering from IIT Rourkee. A renowned
academician, was responsible for making trendsetting transformations during his last stint as Principal,
UEC. Having received a Gold Medal in M.Tech and an award winning research scholar during his PhD. His
Current area of research is Casting methods optimization.

Third Author. Dr S.K.Jain. Phd from APS university Rewa India. He is principal Ujjain Engineering
College Ujjain, India . His current areas of research are Fuzzy logic Technique in Engineering

Fourth Author. Aparna Chede has received Bachelors and Masters Degree in Mechanical Engineering
from Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India. She is currently working as Lecturer in
Mechanical Engineering at Mahakal Institute of Technology and Science Ujjain India. Her current areas
of research are Industrial Engineering techniques.

Method of T Q1 Q2 Q3 Total Cost
solution
Langrange --- 145 44 114 Rs 4063
Multiplier
Heuristic 0.108 216 54 108 Rs 3919

Table No 1: (Comparison of optimal result by both methods)

Product(i) 1 2 3
Demand rate (units per year) Y 2000 1000 1000
Order cost C (Rs per unit per year) 50 50 50
Stock holding cost B (Rs Per unit per 16 10 4
year)
Value V (Rs per unit) 80 150 20
EOQ 112 100 158

Table 2: (Demand rate, order cost, stock holding cost, value and EOQ.)

Method of solution T Q1 Q2 Q3 Total Cost
Langrange Multiplier --- 98 88 139 Rs 3541
Heuristic 0.079 158 79 79 Rs 3716

16

Vol 1, No.4, 2011

Table 3: (Optimal solution by Lagrange multiplier technique)

Job number J1 J2 J3 J4 J5 J6
Batch size 50 40 60 30 30 70

Table 4: (Job and Batch Size)

Job First Second Third Fourth Fifth Sixth
Operation Operation Operation Operation Operation Operation
M/C Time M/C Time M/C Time M/C Time M/C Time M/C Time
A M1 8 M2 7 M3 14 M4 9 M4 3 M4 4
B M2 10 M3 17 M3 6 M5 13 M4 4 M1 3
C M4 18 M3 16 M4 11 M1 12 M5 3 M2 2
D M4 16 M1 7 M2 11 M3 4 M5 4 M4 13
E M2 12 M2 15 M4 9 M1 11 M4 3 M1 4
F M4 8 M5 7 M4 9 M1 6 M2 11 M2 12

Table 5: (Machines with Operation time)

Slots S1 S2 S3 S4 S5 S6
S1 0 4 6 8 10 12
S2 4 0 4 6 8 10
S3 6 4 0 4 6 8
S4 8 6 4 0 4 6
S5 10 8 6 4 0 4
S6 12 10 8 6 4 0

Table 6: (Inter slot distance for Liner Layout)

Load Station 3 5 7 9 11 13

Unload Station 13 11 9 7 5 3

17

Vol 1, No.4, 2011

Table 7: (Load Unload Matrices for Liner layout)

S1 0 4 6 8 10 12
S2 4 0 4 6 8 10
S3 6 4 0 4 6 8
S4 8 6 4 0 4 6
S5 10 8 6 4 0 4
S6 12 10 8 6 4 0

Table 8: (Inter slot distance for Loop Layout)

Unload 14 12 10 8 6 4
Station

Table 9: (Load Unload Matrices for Loop layout)

S1 0 6 8 10 12 14
S2 6 0 6 8 10 12
S3 8 6 0 6 8 10
S4 10 8 6 0 6 8
S5 12 10 8 6 0 6
S6 14 12 10 8 6 0

18

Vol 1, No.4, 2011

Table 10: (Inter Slot distance for Ladder Layout)
Unload Station 13 11 9 7 5 1

Table 11: (Load unload Matrices for Ladder Layout)

Machine M1 M2 M3 M4 M5 M6
No 4 6 6 8 8 6
Waiting time in minutes 6 8 6 8 6 4

Table 12: (Waiting Time of machine)

Job No J1 J2 J3 J4 J5 J6
Waiting time in 3210 3340 1740 3810 3840 1750
minutes

Table 13: (Waiting time for job)

M/C No Job Sequence
M1 J1 J6 J4 J3 J5 J2
M2 J2 J1 J4 J5 J1 J3
M3 J3 J2 J5 J1 J4 J4
M4 J3 J6 J4 J5 J1 J2
M5 J6 J2 J4 J1 J3 J5
M6 J6 J1 J2 J1 J5 J1

Table 14: (Job Sequence for minimum production time)

Type of Layout Machine arrangement Sequence Total
Transportation
Cost
Liner M4 M6 M1 M5 M2 M1 3370
Loop M4 M6 M1 M5 M2 M1 3382
Ladder M4 M1 M5 M6 M2 M1 6324
Open Field M4 M1 M5 M6 M1 M1 4212

19

Vol 1, No.4, 2011

Table 15: (Machine order and cost for different layout)

Load 2 2 2 2 Unload 2
2
l 1 1 1
1 1 S1 S21 S3 S4 S5 S6

Figure 1 Liner (single and double row machine layout)

S1 S2

1 1 2 1

Load 1 S3
2 1

Load S4
2
1 1

S6 S5

20

Optimization by heuristic procedure of scheduling constraints in manufacturing system

More Related Content

What's hot (20)

Similar to Optimization by heuristic procedure of scheduling constraints in manufacturing system (20)

More from Alexander Decker (20)

Recently uploaded (20)

Optimization by heuristic procedure of scheduling constraints in manufacturing system