Jou nal o Indus ial Enginee ing and Managemen
JIEM, 2016 – 9(3): 834-859 – Online ISSN: 2013-0953 – P in ISSN: 2013-8423
h p://dx.doi.o g/10.3926/jiem.1653
Minimizing Was e (O -cu s) Using Cu ing S ock Model:
The Case o One Dimensional Cu ing S ock P oblem in
Wood Wo king Indus y
Gbemileke A. Ogun an i1 , Ayodeji E. Oluleye2
1D exel Uni e si y (Uni ed S a es)
2Uni e si y o Ibadan (Nige ia)
gao32@d exel.edu, [email protected]
Recei ed: Augus 2015
Accep ed: Augus 2016
Abs ac :
Pu pose:
The main objec i e o his s udy is o de elop a model o sol ing he one dimensional
cu ing s ock p oblem in he wood wo king indus y, and de elop a compu e p og am o i s
implemen a ion.
Design/me hodology/app oach:
This s udy adop s he pa e n o ien ed app oach in he
o mula ion o he cu ing s ock model. A pa e n gene a ion algo i hm was de eloped and coded
using Visual basic.NET language. The cu ing s ock model de eloped is a Linea P og amming
(LP) Model cons ained by nume ous easible pa e ns. A LP sol e was in eg a ed wi h he
pa e n gene a ion algo i hm p og am o de elop a one - dimensional cu ing s ock model
applica ion named GB Cu ing S ock P og am.
Findings:
Applying he model o a eal li e op imiza ion p oblem signi ican ly educes ma e ial
was e (o -cu s) and minimizes he o al s ock used. The esul yielded abou 30.7% cos sa ings
o company-I when he o al s ock ma e ials used is compa ed wi h he o me cu ing plan.
Also, o e alua e he e iciency o he applica ion, Case I p oblem was sol ed using wo op
comme cial 1D-cu ing s ock so wa e. The esul s show ha he GB p og am pe o ms be e
when ela ed esul s we e compa ed.
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Resea ch limi a ions/implica ions:
This s udy ound up he linea p og amming solu ion o
he numbe o pa e n o cu .
P ac ical implica ions:
F om Manage ial pe spec i e, implemen ing op imized cu ing plans
inc eases p oduc i i y by elimina ing calcula ing e o s and d as ically educing ope a o mis akes.
Also, inancial bene i s ha can annually amoun o millions in cos sa ings can be achie ed
h ough signi ican ma e ial was e educ ion.
O iginali y/ alue:
This pape de eloped a linea p og amming one dimensional cu ing s ock
model based on a pa e n gene a ion algo i hm o minimize was e in he wood wo king indus y.
To implemen he model, he algo i hm was coded using VisualBasic.ne and linea p og amming
sol e called lpsol edll (dynamic link lib a y) was in eg a ed o de elop a one dimensional cu ing
s ock P og am.
Keywo ds:
1D-cu ing s ock p oblem, cu ing s ock model, pa e n gene a ion algo i hm, linea
p og amming
1. In oduc ion
A cu ing s ock p oblem (CSP) basically consis s o cu ing la ge pieces a ailable in s ock o p oduce
smalle pieces (called i ems) in o de o mee a gi en demand. The cu ing is planned o minimize was e
o he s ock ma e ial (o he objec i es may a ise). These objec i es may be o minimize im loss, he
numbe o cu ing lines o p oduc ion cos , maximize p o i and so on. In a cu ing plan, he equi ed se
o pieces om he a ailable s ock leng hs mus be ob ained. The objec i e is o minimize he numbe o
used s ock leng hs o , equi alen ly, im loss (was e) (Mu a , U a & Ahme , 2011). This ype o p oblem
occu s in se e al indus ies such as pape , aluminium, s eel, glass, and u ni u e indus y among o he s.
Kall a h, Rebennack, Kall a h and Kusche (2014) sol ed cu ing s ock p oblem in he pulp and pape
indus y wi h he objec i e o minimizing he numbe o olls and he pa e ns, he eby p e en ing
o e p oduc ion.
The one-dimensional cu ing s ock p oblem (1D-CSP) can be s a ed as ollows: Gi en a se o i ems I,
each i em i ∈{1, ..., m} o leng h li and demand o bi pieces, o be cu ou o a i ually in ini e supply o
s ock leng h L (whe e 0 < li ≤ L,  i  I), in o de o minimize he numbe o s ock objec s used
(Ga a a, Salassa, Vanc oonenbu g, Be ghe & Wau e s, 2014). The p oblem conside ed in his pape is o
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ind a cu ing plan ha would minimize he was e ma e ial when a se o o de s di e en in leng h and
quan i y is o be cu om a pack o s ocks wi h cons an leng h (Yang, Sung & Weng, 2006).
The pu pose o his pape is o de elop a ma hema ical model o sol ing he one dimensional
cu ing s ock p oblem in he wood wo king indus y and de elop a use iendly compu e p og am
o i s implemen a ion. Many wood wo king companies a e seeking ways o minimizing p oduc ion
was e (and maximize p o i ) bu aced wi h lack o echnical know-how and compe ency o so do.
Thus, he e is need o a e y easy o ope a e and in e p e cu ing s ock p og am o imp o ing
ope a ional e iciency. Based on he p oblem encoun e ed, a me hodology o in eg a ing he cu ing
pa e ns gene a ion algo i hm wi h LP sol e was de eloped. In o de o achie e he was e
minimiza ion objec i e, he cu ing s ock model was coded in o a compu e applica ion using Visual
Basic.NET language.
This pape is o ganized as ollows: sec ion 2 e iews he ela ed wo k on he cu ing s ock p oblem. In
sec ion 3, he me hodology and solu ion app oach a e p esen ed. Sec ion 4 p o ides he p oblems sol ed
o demons a e p ac ical applica ion o he model. Sec ion 5 p esen s esul s ob ained by applying he one
dimensional cu ing s ock model. Finally, he las sec ion concludes he s udy and op imal cu ing using
he 1D-cu ing s ock p og am was p oposed.
2. Li e a u e Re iew
2.1. Cu ing and Packing P oblems
Cu ing and packing p oblems a e combina o ial op imiza ion p oblems. As wi h many o he
p oblems o his kind, hey a e easy o s a e, and di icul o sol e. Ma sumo o, Ume ani and
Nagamochi (2011) also e e ed o hese p oblems as NP ha d. The s anda d p oblem is de ined as
ollows: gi en a se o small and la ge objec s, how he small objec s should be ob ained om he
la ge ones in o de o op imize a gi en c i e ion. The ypical es ic ions o which a cu ing o packing
plan is submi ed a e he impossibili y o he small objec s o o e lap, and he limi ed capaci y o
leng h o he la ge objec s.
The e a e wo main app oaches o sol e his p oblem: exac and heu is ic me hods. Exac algo i hms a e
mainly based on linea /dynamic p og amming and b anch-and-bound echniques. Vande beck (1999)
in oduced a b anch-and-p ice algo i hm based on column gene a ion app oach o sol ing he cu ing
s ock p oblem. The au ho ocus on how s anda d b anch- and-bound enhancemen ea u es such as
ea ly b anching, a iable ixing, and he use o cu ing planes can be inco po a ed in he b anch-and-p ice
algo i hm. Then, how o selec app op ia e b anching p io i ies, and implemen a ounding heu is ic.
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Mo e ecen ly, Ka ak, Ripa i, Schei haue and Ku z (2014) used algo i hmic app oach based on
exhaus i e enume a ion and in ege linea p og amming o sol e one-dimensional cu ing s ock p oblem
(1CSP) wi h espec o he in ege ound-up p ope y (IRUP).
Mo eo e , heu is ic me hods ha e g ea e lexibili y in aking in o accoun p oblem speci ic cons ain s
and o e a ade-o be ween he quali y o a solu ion and i s compu a ional e o . They a e equi ed o
p o ide good, bu no necessa ily op imal solu ions. Some o he new heu is ics me hods ound in he
cu en li e a u e a e: (Dikili, Takinacı & Pek, 2008; Ma sumo o e al., 2011; A aujo, Poldi & Smi h, 2014;
Che i, A enales & Yanasse, 2013; Ga a a e al., 2014; Cui, Zhong & Yao, 2015). A aujo e al. (2014)
p esen s a heu is ics me hod based on gene ic algo i hm o sol e one-dimensional cu ing s ock p oblem
by conside ing wo con lic ing objec i e unc ions: minimiza ion o bo h he numbe o objec s and he
numbe o di e en cu ing pa e ns used.
P e iously, Dyckho (1990) de ined a o mal ypology o cu ing and packing p oblems by
sys ema ically in eg a ing a ious kinds o p oblems and no ions. This ypology was imp o ed by
Wäesche , HauBne and Schumann (2007) wi h he de ini ion o new ca ego iza ion c i e ia. Table 1
shows he ypology ound in he li e a u e.
Dyckho ’s C&P ypology Waesche ’s imp o ed C&P ypology
Dimensionali y
1
2
3
N
One-dimensional
Two-dimensional
Th ee-dimensional
N-dimensional
1
2
3
N
One-dimensional
Two-dimensional
Th ee-dimensional
N-dimensional
Kind o assignmen
B
V
All la ge objec s and a selec ion o small objec s
A selec ion o la ge objec s and all small objec s
OM
IM
Ou pu alue maximiza ion
Inpu alue minimiza ion
Asso men o la ge objec s
O
I
D
One la ge objec
Many iden ical la ge objec s
Di e en la ge objec s
O
SO
One La ge objec
OA all Fixed dimensions
OO one a iable dimension
OM mo e a iable dimensions
Se e al La ge objec s
SI Iden ical La ge objec s
SW weakly he e ogeneous asso men
SS s ongly he e ogeneous asso men
Asso men o small objec s
F
M
R
C
Few small objec s o di e en igu es
Many small objec s o many di e en igu es
Many small objec s o ela i ely ew di e en igu es
Many iden ical small objec s
IS
W
S
Iden ical small i ems
Weakly he e ogeneous asso men
S ongly he e ogeneous asso men
Table 1. Dyckho ’s (1990) and Waesche ’s (2007) ypologies o C&P p oblems
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Dyckho classi ies he solu ion o one dimensional cu ing s ock p oblem in o wo: i em o ien ed and
pa e n-o ien ed app oaches. I em o ien ed app oach is cha ac e ized by indi idual ea men o e e y i em
o be cu . In he pa e n o ien ed app oach, a i s , o de leng hs a e combined in o cu ing pa e ns, o
which - in a succeeding s ep - he cu ing equencies a e de e mined in o de o sa is y he demands. The
cons ain s in he pa e n-o ien ed app oach a e based on he algo i hm o Gilmo e and Gomo y (1961,
1963). Howe e , a pa e n-o ien ed app oach is possible only when he s ock is o he same leng h o o
se e al s anda d leng hs. An i em-o ien ed app oach is used when all s ock leng hs a e di e en and
equencies canno be de e mined.
2.2. One Dimensional Cu ing and Packing Models
In he li e a u e, esea che s ha e used di e en exac and heu is ics solu ion app oaches o suppo
cu ing s ock decision making in he indus y (Kall a h e al., 2014). Mos ecen ly, Delo me, Io i and
Ma ello (2016) p o ides a comp ehensi e e iew o he main ma hema ical models and algo i hms
de eloped o exac ly sol ing he one-dimensional bin packing and cu ing s ock p oblems. The p oblems
conside ed in hei e iew a e classi ied as 1-dimensional SBSBPP (Single Bin Size Bin Packing P oblem)
and 1-dimensional SSSCSP (Single S ock Size Cu ing S ock P oblem). Also, a su ey o cu ing s ock
p oblems wi h usable le o e s can be ound in Che i, A enales, Yanasse, Poldi and Vianna (2014) as
hese p oblems does no i he classi ica ion based on he ypology p oposed by Wäsche e al. (2007).
Mos o he app oaches o sol ing he s anda d cu ing and packing p oblems in a single dimension
based on linea p og amming models ha ha e been p oposed can be di ided in o ou ca ego ies:
a) The Assignmen Fo mula ions
b) The Pa e n-O ien ed Fo mula ions
c) The One-Cu Fo mula ions
d) The Flow Models
2.2.1. The Assignmen Fo mula ions
The i s in ege linea p og amming o mula ion was p oposed by Kan o o ich (1960), based on
assignmen o a iables as ollows.
(1)
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Subjec o:
(2)
(3)
(4)
(5)
Whe e: he abo e model has bina y and gene al in ege a iables; wi h yj been he choice o oll j, Xij is
he numbe o i ems o size wi assigned o oll j and Wi is he capaci y o he bin i. Equa ion 3 is he
knapsack cons ain s, one o each oll, while equa ion 2 is he demand (bi) cons ain s wo king as he
linking cons ain s. Whe e n is a known uppe bound on he numbe o olls needed, yj = 1, i oll k is
used, and 0 o he wise, and a iable Xij is he numbe o imes i em i is cu in oll j. A lowe bound o he
op imum can be ob ained om he op imum o i s LP elaxa ion, which esul s om subs i u ing he wo
las cons ain s o 0 ≤ yj ≤ 1 and Xij ≥ 0.
Ma ello and To h (1990) showed ha he lowe bound p o ided by he LP elaxa ion can be e y weak.
This model’s main d awback is ha i p oduces poo lowe bound wi h la ge was e. Good quali y lowe
bounds a e o i al impo ance when using LP based app oaches o sol e in ege p oblems (Ca alho,
2002).
2.2.2. The Pa e n-O ien ed Fo mula ions
The pa e n o ien ed model o Gilmo e and Gomo y (1961) ollowed he assignmen o mula ion o
Kan o o ich. Gilmo e and Gomo y (1961) o mula ed he cu ing s ock p oblem as an in ege
p og amming p oblem. The cu ing s ock p oblem can be modelled as:
(6)
Subjec o:
(7)
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(8)
(9)
(10)
Whe e,
L is he leng h o he objec s in s ock;
li is he leng h o he i ems, i = 1, 2, …, m;
bi ep esen s he demand o i em i, i = 1, 2, …, m;
Aj = (a1j, …, aij, …, amj)T be a cu ing pa e n, i = 1, 2, …, n, whe e aij is he numbe o i ems i in he
pa e n;
Xj be he equency ha he pa e n j is cu .
The di icul y in using his model is he la ge numbe o pa e ns o be enume a ed in p ac ical p oblems.
Since, he possible pa e ns numbe s inc eases exponen ially as he numbe o di e en i ems and he
demands inc eases.
2.2.3. The One-Cu Fo mula ions
In he one-cu models, independen ly p oposed by Rao (1976) and Dyckho (1981), he p inciple is o
de e mine how o apply a single cu (a one-cu ) on an o iginal o esidual piece o ma e ial. A one-cu
di ides he aw ma e ial in wo pieces: an o de ed i em and a esidual objec as shown in he igu e below.
Figu e 1. One-Cu
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The la e pa can be im loss, a po ion o be cu u he , o ano he o de ed wid h. The model
p oposed by Dyckho conside s he case in which di e en s ock leng hs a e a ailable. In he sequel, D
ep esen s he se o i em wid hs o cu om he s ock olls whose leng hs belong o S = {W1, ..., WK}.
The se o esidual objec s whose leng h is su icien ly la ge o cu an i em is deno ed by R. The decision
a iables yp,q indica e he numbe o imes a piece wi h leng h p is o be cu so as o p oduce an i em o
wid h q, and a esidual objec o wid h p−q. The zk a iables indica e he numbe o s ock olls wi h
leng hs Wk ha a e used. The Dyckho ’s model is as ollows:
(11)
Subjec o:
(12)
(13)
(14)
(15)
(16)
Whe e,
S is he se o he (sizes o he) o iginal objec s, p ∈ {W1, W2, …, WK}  N
R be he se o he (sizes o he) esidual pieces ob ained a e cu ing an i em om an objec o a
esidual piece i sel
D be he se o he (sizes o he) i ems q ∈{w1, w2, …, wm} ⊂N. We assume wi hou loss o gene ali y
ha S ∩ D = Φ
yp,q be he numbe o pieces o size p ha a e di ided in o a piece o size q, and a esidual piece o
size p – q
zk deno es he numbe o objec s o size Wk used
Nq s ands o he demand o i ems o size q
Bp is he numbe o objec s o size p in s ock, p = 1, 2, …, K.
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The majo disad an age o his model is he inc easing numbe o a iables as he numbe o i ems
inc eases. The possible sizes o esidual pieces can be la ge and hey need o be enume a ed o sol e he
p oblem. This model also canno be ex ended, a leas in a s aigh o wa d manne o bi-dimensional o
mul idimensional cu ing s ock p oblems.
2.2.4. The Flow Models
The a c low o mula ions a e based on low a iables. Le G = (V, A) be an acyclic di ec ed g aph wi h
e ices V = {0, 1, 2,…, W} whe e W is he size o he objec in s ock and A = {(i, j ): 0 ≤ i < j ≤ W} is
he se o a cs. The e exis s a di ec ed a c (i, j ) in his g aph G i he e is an i em d o size wd and j – i = wd.
Ca alho (1999) modelled he one-dimensional cu ing s ock p oblem as an a c low model. In his
model, a uni o low om node 0 o node W co esponds o a cu ing pa e n since i de ines a pa h
om node 0 o node W whe e he addi ion o he sizes o he i ems in co espondence o he a cs in his
pa h is smalle han he size o he objec . The ma hema ical o mula ion o he a c low model is gi en
below:
(17)
Subjec o:
(18)
(19)
(20)
(21)
(22)
Whe e,
Z is he low in a eedback a c, om e ex W o e ex 0
Xij is he low in a c (i, j )
bd is he demand o i em d, d = 1, 2, …, m.
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4. The op imal cu ing pa e n is now selec ed by sol ing he LP model (wi h ou applica ion) which
minimizes he o al was e.
5. Clicking on sol e bu on on he GB cu ing s ock p og am, he esul s displays on wo di e en
abs (Tex solu ion and Visual solu ion espec i ely).
6. The o al s ock ma e ials used (Summa ion o all Xj) is gi en as:
(30)
To al cos o s ock shee used is es ima ed using equa ion 31 s a ed below.
(31)
7. F om he ex solu ion o he LP model, he o al was e (o -cu s) can be e alua ed om he alue
o he objec i e unc ion o he model
8. A isual solu ion is p esen ed showing i ems and o -cu s (was e) on each op imal cu ing pa e n
wi h di e en colou on he second esul ab.
4. Model Applica ion
To demons a e he applicabili y o he model, wo p ac ical cases a e p esen ed in his pape . Case I
ela es o he cu ing p oblem encoun e ed a a Fu ni u e and Joine y Company while Case II he cu ing
p oblem a a gene al wood wo ks company in Nige ia. Bo h companies a e p i a ely owned.
4.1. Case I
Twel e (12) lush doo s a e o be p oduced om 55mm wid h wood o s anda d leng h 12 (3660mm).
Fi e 55mm wid h wood can be cu om a s anda d plank, since 10mm wid h is cu o o s aigh en he
s anda d plank edges and he blade wid h akes abou 4mm away a each cu .
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Pa Numbe Pa Leng h(mm) Numbe Requi ed o a Doo To al Numbe Requi ed
1 2000 2 24
2 710 4 48
3 540 2 24
4 620 2 24
5 625 5 60
Table 2. Requi emen s o he O de
F om he cu en p ac ice in he company I, hi een (13) planks we e used o ul il his ask. Since, a
plank o dina ily yields i e 55mm wid h wood, he e o e a o al o six y- i e (65) 55mm wid h wood is
equi ed. The cos pe uni o plank is one housand wo hund ed nai a (#1200). Fi e 55mm wid h wood
can be ob ained om a plank; his implies ha a uni o 55mm wid h wood echnically cos s wo hund ed
and o y nai a (#240).
4.2. Case II
Two p oduc s we e selec ed ou o he da a collec ed om Company II. The p oduc s a e as ollows:
4.2.1. P oduc I: Con e ence Chai s
Tables 3 and 4 summa ise he da a equi ed o p oduce 30 pieces o con e ence chai s as equi ed.
Wid h Numbe Used Planks Used
9cm 32 11
7cm 10 3
5cm 12 2
To al Planks Used 16
Table 3. Numbe o s ock leng h used by cu en p ac ice o p oduce he chai s
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Pa s Componen Leng h (cm) Numbe pe Chai Numbe Used
9cm wid h
1 A ms 64 2 60
2 F on legs 58 2 60
3 C osses (F on & Back) 54 2 60
7cm Wid h
1 Sides 58 2 60
5cm Wid h
1 Back legs 68 2 60
Table 4. De ails o Pa s lis o he con e ence chai
4.2.2. P oduc II: Panel Doo s
The s anda d size o a panel doo is 206cm X 84cm. Cu en p ac ice e ealed ha en (10) panel doo s
a e p oduced om hi y (30) planks based on he p esen cu ing echnique. A panel doo consis s o
h ee di e en ypes o he s ock ma e ial om which each equi ed leng h is ob ained. These a e o
wid hs 28cm, 15cm and 10cm. The de ails o each equi ed componen is gi en on Table 5 below.
Pa s Componen Leng h (cm) Numbe pe Doo Numbe Used
28cm wid h
1 Small panel 32 2 20
2 La ge panel 64 4 40
15cm Wid h
1 Up igh 206 2 20
2 C osses 76 3 30
10cm Wid h
1 C osses 76 3 30
2 V. small c oss 33 1 10
Table 5. De ails pa s lis o he Panel Doo s
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5. Resul s and Discussion
The So wa e p esen s esul s in wo o ms: The ex solu ion and he isual solu ion. The ex solu ion
gi es he desc ip ion o pa e ns o be cu , o al numbe o s ock o be used and i s cos ; while isual
solu ion shows he diag am o how he op imal pa e n is o be cu .
F om Table 6, he model ecommends he o al s ock (55mm wid h wood) o be used is 42. Since, i e
pieces 55mm wid h wood a e ob ained om a plank; o y- wo (42) 55mm wid h wood will gi e 8.4planks
(app oxima ely 9 s anda d size planks should be used). Nine planks yield o y- i e 55mm wid h wood,
he eby allowing wo 55mm wid h wood o be kep as ese e in case o mis akes while cu ing. Figu e 4
shows he op imal cu ing pa e ns o be used. The esul s ob ained show ha 9 planks should be used
ins ead o he cu en 13, which esul in a sa ing o 30.7%.
5.1. Tex Solu ion
The ex solu ion ob ained om he model can be summa ized below:
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng hs (mm) To al Was e(
WjXj
)
mm
2000 710 625 620 540
2 21.6 22 22 44 - - - 5280
8 2.4 2 2 - - - 8 120
9 0.96 1 - 5 - - - 110
35 15 15 - - 60 15 15 0
50 1.8 2 - - - 10 2 40
To al 41.76 42 24 49 60 25 25 5550
Table 6. Solu ion o Case I p oblem (Flush Doo )
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng h (cm) To al Was e (
WjXj
)
cm
64 58 54
3 15 15 60 - 30 30
21 15 15 - 60 30 390
To al 30 30 60 60 60 420
Table 7. Numbe o 9cm wid h o be used o p oduce P oduc I (Case II)
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Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Wid h (cm) To al Was e (
WjXj
)
cm
9 7 5
1 3.17 4 12 - - 12
2 10 10 20 10 10 -
7 0.5 1 1 - 4 1
To al 13.67 15 33 10 14 13
Table 8. To al numbe o planks o be used o Case II P oduc I (Con e ence Chai s)
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng h (cm) To al Was e (
WjXj
)
cm
64 32
2 8 8 40 8 112
7 1.09 2 - 22 28
To al 9.09 10 40 30 420
Table 9. Numbe o 28cm wid h o be used o p oduce P oduc II (Case II)
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng h (cm) To al Was e (
WjXj
)
cm
206 76
1 5 5 5 - 800
2 15 15 15 30 120
To al 20 20 20 30 920
Table 10. Numbe o 15cm wid h o be used o p oduce P oduc II (Case II)
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng h (cm) To al Was e (
WjXj
)
cm
76 33
2 6.92 7 28 7 203
3 0.77 1 3 4 6
To al 7.69 8 31 11 209
Table 11. Numbe o 10cm wid h o be used o p oduce P oduc II (Case II)
Pa e n No. (
j
) LP Solu ion (
Xj
) Numbe Cu Leng h (cm) To al Was e (
WjXj
)
cm
28 15 10
1 10 10 10 - - 20
2 10 10 - 20 - 0
4 2.67 3 - - 9 0
To al 22.67 23 10 20 9 420
Table 12. Solu ion o Case II P oduc II (Panel Doo )
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5.2. Visual Solu ion
Figu e 4 p esen an illus a ion o he isual solu ion ab o he applica ion. This shows he numbe o
each i em p oduced pe pa e n and he o – cu s (was e). Simila ly, GB Cu ing S ock P og am p o ides
isual solu ion o all o he cu ing plan solu ions.
In addi ion, implemen ing he cu ing pa e ns esul shown in Tables 7 and 8 espec i ely o he
con e ence chai s e eals ha i een (15) planks mus be consumed as agains he 16 planks used. This
gi es a sa ing o 6.25% o he o ganisa ion. In a la ge scale ope a ion, he sa ings could be subs an ial in
mone a y e ms.
Simila ly, implemen ing he cu ing pa e ns esul shown Tables 9-12 espec i ely o p oduce he panel
doo s shows ha wen y- h ee (23) planks a e equi ed ins ead o hi y (30) planks used. This leads o a
ne sa ing o 23.3% when compa ed o he numbe o planks used by he o me cu ing plan.
Figu e 4. Op imal Cu ing Plans o he lush doo p oblem.
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5.3. Compa ison S udy
To e alua e he e iciency o GB cu ing s ock p og am, wo comme cial so wa es ha ha e execu able
ial e sion a ailable on he in e ne was used o sol e he p oblem p esen ed in case I. The summa y
esul s ob ained a e as shown on Table 13.
So wa e Ma e ials Used Pa e n Used To al Was e (mm) Ma e ials
To al Cos (#)
GB Cu ing P og am 42 5 5,550 10,080.00
A 42 6 6,300 10,080.00
B 44 6 13,620 10,560.00
Table 13. Numbe o ma e ials, pa e n used, o al was e and cos by each p og am
The websi e o he comme cial so wa es a e lis ed on appendix I as A and B. Comme cial so wa e
package A p oduce he same op imal esul as ou cu ing s ock p og am in e ms o ma e ial used and
u ilized 6 pa e ns as shown in Figu e 5 o implemen he same esul . While comme cial so wa e
package B used he same numbe o pa e ns wi h so wa e A bu mo e ma e ials han bo h GB cu ing
p og am and A. The e o e, GB cu ing s ock is be e because i uses less numbe o pa e ns and educes
ope a ion ime han A and B. See appendix II o op imal cu ing pa e ns solu ion using so wa e B.
Figu e 5. Op imal Cu ing Plans gene a ed using package A
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Also, conduc ing p ice su ey on comme cial so wa e packages e eals ha i would cos wi hin
$100 - $400 (20,000 - 78,000 local cu ency) o pu chase a license. This is cos ly o local woodwo k
indus y in Nige ia which canno ansac in in e na ional cu ency. Thus, GB cu ing s ock p o ides easy
o use p og am wi h no p io compu e knowledge o local woodwo king indus y.
6. Conclusion
The cu ing s ock model and a pa e n gene a ion algo i hm we e de eloped. The algo i hm was coded
using VisualBasic.ne and a ee linea p og amming sol e called lpsol edll (dynamic link lib a y) was
in eg a ed o de elop a one dimensional cu ing s ock p og am named GB cu ing s ock p og am. The
cu ing s ock model signi ican ly educes ma e ial was e, elimina es calcula ing e o s, d as ically educes
ope a o mis akes and minimizes he o al s ock used, he eby imp o es p oduc i i y. Also, he esul s
ob ained we e compa ed agains he bes solu ion gene a ed by wo comme cial so wa es o he same
p oblem. I can be concluded ha GB cu ing s ock p og am yielded a e y good esul a easonable
compu a ional ime and pe o ms be e han he comme cial so wa e in e ms o ope a ional e iciency
ha is i equi es less ime o implemen esul s. Finally, due o i s a o dabili y; implemen ing ou esul
yielded a signi ican cos sa ings o abou 30.7% o company-I when he o al s ock ma e ials used is
compa ed wi h he o me cu ing plan.
Acknowledgemen s
We would like o hank he B i ish Council o suppo ing his wo k unde he AKTP (A ican
Knowledge T ans e Pa ne ship) ini ia i e wi h a G an . We also hank he anonymous e e ees o hei
help ul commen s and all hose who made sugges ions as o making he applica ion simple o use in
p ac ice.
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