e-dentel planner_a free.xls

7/23/2019 E-dentel Planner_A free.xls

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Tejidos Especiales e-Dentel. Planner Intr

Aggregate planning

Tejidos Especiales e-Dentel (B) provides a reasonable size exercise on which to test some of the ideas

ou ma have come up with to resolve the ! case" #or$in% with a small exercise has one

advanta%e& if our proposed approach does not wor$' ou find it uic$er' but it also has a disadvanta%e&

an approach which wor$s on a small problem does not provide an %uarantee that it will wor$ on a

real life size problem" The time reuired to find a solution can ma$e it unfeasible"

to obtain from lannin% is not a ver detailed one (e" %"' in terms of which machines will be assi%ned

to this job) but it needs to cover a rather lon% horizon" Therefore' we can use the a%%re%ate plannin%ideas' b drawin% a cumulative chart of promised deliver loads and compare it with the cumulative

available capacit"

Even thou%h at first si%ht it appears that it is possible to deliver the jobs as promised' because there

is enou%h capacit ( *+ machines for , das to manufacture a load of ** machine-das)' a closer loo$

reveals that the last job must be delivered b da ,*" Therefore we onl have *+.,* / *0+ machine-das

of available capacit to satisf a demand of **"

Even if we dela the low priorit jobs (a total of ,1 machine-das of load) to da ,' it is not sufficient

to ma$e it feasible" The chec$ we just made to see if the available capacit in ,* das is enou%h to ma$e

everthin% that needs to be deliver in the first ,* das' must be repeated for ever da" The followin%

pivot table shows the capacit and promised deliveries accordin% to the ori%inal dates' assumin% thatcapacit is limited to *+ machine-das2da"

rior" 3um" 3um 3um"

Deliver 4 5 6 5 7 4 57476 3apac"

1 * 8 9 9 8+

0 9 0 *+ *+ 9+

,8 9 18 00 ,++

8 ** ** 9+ 99 ,*+

: *; ,0 ,*1 ,18 ,0+

9 ,9 ,9 ,; ,:* ,8+

; 00 *+1 *,8 ,9+

,+ *+ **1 *18 *++

,, ,* *1 *09 **+

,* 0 *1; ** *0+

Total 175 64 13

rof" E *++,

This exercise concentrates on the Planning-Salesinterface" 4ote that the information that >ales needs

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The followin% chart presents the cumulative demand and production capacit when all jobs are

scheduled to be delivered at their promised date" #e can see that the demand curve exceeds theavailable suppl alread in da 9"

#e are offered the possibilit of delain% normal jobs b up to * das and low priorit jobs as much

as needed" If we plan for a crash approach (delain% each job as much as possible within theseconstraints)' we obtain a feasible solution

on da can be accepted' what would ou repl?

Question& If a salesman approaches ou as$in% whether a new order for + machine-das to be delivered

0 1 2 3 4 5 6 7 8 9 10 11 1

0

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200

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Original plan as pro!is"d#

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$"la%"d plan )*2+ ,-15#

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4ote that b acceptin% a new order to be delivered on da ' not onl the curve value for da chan%es'

>o' if the new order is accepted' it can be delivered on time' but this will create conflicts to deliver

other orders on time' so the will have to be delaed"

but also those points after da " =emember the curve is the cuulati!edemand up to a %iven da?

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)" ord"r acc"pt"d or da% 5

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oduction

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ma$in% other orders to become delaed"

>o' if the new order is to be accepted' other orders will have to be delaed'

to satisf the capacit constraints of the plant"

"ig#t@@" !cceptin% this order would move not onl the point for da ' but all the points after da '

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)" ord"r acc"pt"d or da% 5

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Tejidos Especiales e-Dentel. Planner Introduction

In fact' the amount available to promise at an time is part of the information reuired b the >ales

department" This information can be presented as a chart' computin% the minimum slac$ available

from each date onwards" Aor the same example' this is the correspondin% chart&

!s can be seen from the chart above' the maximum capacit available to promise in das , to ,* is

,: machine-das"

+ * 0 8 9 ,+ ,* ,0 ,8

+

,+

*+

1+

0+

+

8+

A!aila$le to Proise

Da

Bach-das

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Tejidos Especiales e-Dentel. Planner 5ow to use

In the followin% chart ou will have the opportunit to adjust the ori%inal plan to ma$e it feasible"

Cou can chan%e the promised deliver date of an order' sort the table and chec$ the effects of our

chan%es' both in the cumulative curves (to chec$ feasibilit) and in the scatter chart which compare

the promised deliver date with the currentl planned date"

Cou can wor$ on the wor$sheet either ma$in% chan%es manuall or b applin% some predefined

al%orithms to find a feasible solution"

%anuall&

=eset =esets the table to the ori%inal situation

>ort !fter ma$in% chan%es in the r" Del" (romised Deliver) column' press this button to

sort the table accordin% to the seuence of deliver dates promised" Cou can then

chec$ the possible delas"

3um >hows the cumulative charts for capacit and promised deliveries

3hec$ >hows the scatter dia%ram comparin% promised date with the earliest possible deliver

'ptii(e ffers several pre-pro%rammed solutions" 3hec$ the button below to obtain an explanation

of the details behind the computations

>hortest time first It sorts jobs accordin% to its process time"

in ax Dela >euences the jobs so as to minimize the maximum dela"

in 4umb" Del" >euences the jobs to minimize the number of jobs delaed"

3ritical ratio >chedules jobs accordin% to the rder2Deliver ratio

>lac$ >chedules jobs accordin% to the difference Deliver-rder

3um >hows the cumulative charts for capacit and promised deliveries

3hec$ >hows the scatter dia%ram comparin% promised date with the earliest possible deliver

)o$ine Cou can start with one of the optimized solutions and modif it manuall to obtain a

better solution' e"%"' one that ta$es priorities into account"

In addtion to the charts' the wor$sheet displas several statistics realted to the proposed schedule&

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297265896.xls

297265896.xlsor

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Tejidos Especiales e-Dentel. Planner !l

/euristics 0uic2 and dirt& et#ods or single process sc#eduling

ethod& >T (shortest processin% time)

Do jobs in increasin% order of processin% time

Example& 3onsider the followin% jobs to be processed b a sin%le machine&

o$ Deli!. 'rder o$ Deli!. 'rder Start +inis#

! 1 G * , + ,

B 1 * B 1 * , 1

3 ,, A 9 * 1

D , 0 I *8 * :

E *: ; ! 1 : ,+

A 9 * D , 0 ,+ ,0G * , 3 ,, ,0 ,;

5 0+ : 5 0+ : ,; *8

I *8 * < 1, 9 *8 10

< 1, 9 E *: ; 10 01

,8*

4ote that in the first ,+ periods we have mana%ed to %et half of the jobs throu%h"

(*) This is based on a tutorial taught by Prof. Gene Woolsey at an ORSA conference in mid !"#$s.

1. %iini(e su o copletion ties o su o aiting tie or nu$er o jo$s ali!e

>um of completion times>um of completion times

+ ,+ , *+ * 1+ 1 0+ 0

+

*

0

8

9

,+

,*

*o. o acti!e jo$s

Tie

aximum tardinesssaximum tardinesss00 l

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orithms

Dela&

9

1

,8

,8

+

ate jobsate jobs

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Tedious Especiales e-Dentel. Planner !l

/euristics 0uic2 and dirt& et#ods or single process sc#eduling 0continued

ethod& DD= (Due Date =ule)

Do jobs in order of increasin% Due Dates

o$ Deli!. 'rder o$ Deli!. 'rder Start +inis#

! 1 B 1 * + *

B 1 * ! 1 *

3 ,, A 9 * :

D , 0 3 ,, : ,*

E *: ; D , 0 ,* ,8

A 9 * G * , ,8 ,:G * , I *8 * ,: ,;

5 0+ : E *: ; ,; *9

I *8 * < 1, 9 *9 18

< 1, 9 5 0+ : 18 01

,9

3ompare the results with those of the previous

heuristic"

4ote now that in the first ,+ periods we have mana%ed to %et onl three of the jobs throu%h"

8. %iini(e a9iu tardiness

>um of completion times>um of completion times

+ ,+ , *+ * 1+ 1 0+ 0

+

*

0

8

9

,+

,*

*o. o acti!e jo$s

Tie

aximum tardinesssaximum tardinesss

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orithms

Dela&

,

,

,

1

+

late jobs late jobs

"turn to Intro

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ethod& >lac$ Time =ule

Do jobs in increasin% order of (Due Date - rocessin% time)

o$ Deli!. 'rder Slac2 Start +inis# Dela&

B 1 * , + ,

! 1 * , 0

3 ,, 8 0 ;

A 9 * 8 ; ,, 1

D , 0 ,, ,, ,

E *: ; ,9 , *0< 1, 9 *1 *0 1* ,

G * , *0 1* 11 9

I *8 * *0 11 1 ;

5 0+ : 11 1 0* *

ethod& =atio =ule

Do jobs in increasin% order of (Due Date-rocessin% time)2riorit

4ote& The hi%her the priorit' the bi%%er the priorit number

o$ Deli!. 'rder Priorit& "atio Start +inis# Dela&

B 1 * +"* + *

! 1 1 +"8: *

3 ,, 0 ,"+ ,+

D , 0 *"*+ ,+ ,0

A 9 * * 1"++ ,0 ,8 9

G * , 0"9+ ,8 ,:

I *8 * 1 9"++ ,: ,;

E *: ; , ,9"++ ,; *9 ,

< 1, 9 , *1"++ *9 18

5 0+ : , 11"++ 18 01 1

3. %a9ii(e %iniu Tardiness

4. %inii(e %a9iu Tardiness relati!e to priorit&

late j late j

0 late jobs' alllow priorit

0 late jobs' alllow priorit

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orithms

obs bs

ithith

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ethod& ooresHs method

a) ut jobs in increasin% order of Due Dates

b) >tart doin% the jobs from top to bottom until a late job is found

c) 6oo$ at all the jobs up to an includin% the late one" Arom these jobs' pic$ out the

one with the bi%%est processin% time" =emove it from the list and put it last"

d) If all jobs are not et scheduled' %o to step (b)

e) >ort the jobs ou removed from the list in increasin% order of Due Date"

o$ Deli!. 'rder Start +inis# Dela&

B 1 * + *

! 1 * A 9 * :

3 ,, : ,* ,

D , 0 ,* ,8 ,

G * , ,8 ,:

I *8 * ,: ,;

E *: ; ,; *9 ,

< 1, 9 *9 18

5 0+ : 18 01 1

Arom these jobs' select the one with the bi%%est processin% time and remove it from the list'

puttin% it last" =ecompute delas"

o$ Deli!. 'rder Start +inis# Dela&

B 1 * + *

! 1 *

A 9 * :

D , 0 : ,,

G * , ,, ,*

I *8 * ,* ,0

E *: ; ,0 *1

< 1, 9 *1 1,

5 0+ : 1, 19

3 ,, 19 01 1*

to repeat the process several times"

5. %inii(e t#e nu$er o late jo$s

*ote:in this case' we have had to do onl one iteration' but in other cases ou ma need

Airst late jobAirst late job

4o more late jobsin the list

4o more late jobsin the list

nl , late jobnl , late job

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orithms

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297265896.xls

Pag" 22

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

50

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350

)uulati!e )#art

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+ * 0 8 9 ,+ ,* ,0 ,8

+

*

0

8

9

,+

,*

,0

,8

Proised !s. A!aila$le

Proised

A!aila$le

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$ail% capacit% in !ac'in"=da%s#.

Day Capacity Cum. Cap.

1 20 20

2 20 40

3 20 60

4 20 80

5 20 100

6 20 120

7 20 140

8 20 160

9 20 180

10 20 200

11 20 22012 20 240

13 20 260

14 20 280

15 20 300

e-dentel planner_a free.xls

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