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Algorithms 2018,11, 54
methodbyRosen. Thus,a three-foldcomprehensivecomparisonismadefor themulti-marketnetwork
design. Thecanonical settingat eachmarket includesanewsvendor setupwhohas toobtainhow
muchofthecommonalitytoorder. Everyitemcostscbutcanbesoldatpricep,wherep>c. Foragiven
demand, theprofit isgivenas theproductofpriceandminimumofdemandandorderingquantity
minus theprocurementcost,which iscostper itemmultipliedbyorderingquantity. Supposewehave
arandomdemand,Dhavingdensity f andcumulativedistributionF.Theexpectedprofit inanyof the
sellingpoints in thenetworkundernewsvendormodelwithdemanduncertainty isgivenby:
π(x)= pE(min(D,x))−cx (1)
where,
x:Numberof itemsordered
p: Sellingpriceof the item
c: Costof item,where c<p
D:Demand
E(y): Expectedvalueofy
Consideringthat thedemandD isuniformlydistributedbetween [a,b]. Thentheprofit function
inEquation(1)canbeexpressedas [13]:
π(x)=Ax2+Bx+C (2)
where,
A= −p
2(b−a),B= pb
b−a−c,C= −pa2
2(b−a)
Now,we apply the abovemodel to themulti-market scenario. Each of themarkets can be
consideredasanewsvendormodel.Consider themulti-marketmodelasdepicted inFigure2.
Figure2.Generalizedmulti-marketdesign.
Withreference toFigure2,weuse the followingnotations:
132
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Algorithms for Scheduling Problems
- Title
- Algorithms for Scheduling Problems
- Authors
- Frank Werner
- Larysa Burtseva
- Yuri Sotskov
- Editor
- MDPI
- Location
- Basel
- Date
- 2018
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-120-7
- Size
- 17.0 x 24.4 cm
- Pages
- 212
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Categories
- Informatik
- Technik