keputusan lokasi fasilitas dalam jaringan distribusi.ppt

1

Pertemuan #7Keputusan-keputusan Lokasi Fasilitas dalam Jaringan Distribusi

Dr. Eng. Ahmad RusdiansyahJurusan Teknik Industri FTI-ITS

Jurusan Teknik Industri FTI-ITS 2

Materi Pokok dan Sub Bahasan

Perencanaan Jaringan Distribusi dalam Segitiga Strategi Logistik. Kedudukan Sourcing Points, Intermediate Points , Sink Points dalam Jaringan

Distribusi Konsep Analisis Lokasi :

Manufacturing (Cost driven Based), Retailers (Revenue driven Based), Service Facilities (Service factor driven based)

Model Single Facility Location :

Center of Gravity (COG): Heuristics Vs Exact Solutions : Contoh-contoh aplikasi dan Penyelesaian Menggunakan Modul Logware

Model Multiple Facilities Location Multiple Center of Gravity (MCOG): Heuristics Vs Exact Solutions A Multiple Product Network Design Problem : Mixed Integer Programming Guided Linear Programming untuk Merancang Jaringan Distribusi Dynamic Location Problem Model Retailer Location Problem : Set Covering Problem Contoh-contoh tiap model dan Penyelesaiannya Menggunakan Modul Logware


Tujuan Instruksional Mahasiswa memahami Perencanaan Jaringan

Distribusi dalam Segitiga Strategi Logistik Mahasiswa memahami Kedudukan Sourcing Points,

Intermediate Points , Sink Points dalam Jaringan Distribusi

Mahasiswa memahami Konsep Analisis Lokasi Mahasiswa memahami Model-model Single Facility

Location dan menyelesaikan permasalahan secara manual ataupun menggunakan software Logware

Mahasiswa memahami Model-model Multiple Facility Location dan menyelesaikan permasalahan secara manual ataupun menggunakan software Logware


Referensi

Buku Teks : Ballou, Ronald. H. (2004) Business

Logistics Management, Prentice Hall International, Inc., USA


Facility Location Decisions

Experience teaches that men are so much governed by what they are accustomed to see and practice, that the simplest and most obvious improvements in the most ordinary occupations are adopted with hesitation, reluctance, and by slow graduations.

Alexander Hamilton, 1791


Facility Location in Location Strategy

PL

AN

NIN

G

OR

GA

NIZ

ING

CO

NT

RO

LL

ING

Transport Strategy• Transport fundamentals• Transport decisions

Customer service goals

• The product• Logistics service• Ord . proc. & info. sys.

Inventory Strategy• Forecasting• Inventory decisions• Purchasing and supply

scheduling decisions• Storage fundamentals• Storage decisions

Location Strategy• Location decisions• The network planning process

PL

AN

NIN

G

OR

GA

NIZ

ING

CO

NT

RO

LL

ING

Transport Strategy• Transport fundamentals• Transport decisions

Customer service goals

• The product• Logistics service• Ord . proc. & info. sys.

Inventory Strategy• Forecasting• Inventory decisions• Purchasing and supply

scheduling decisions• Storage fundamentals• Storage decisions

Location Strategy•Location decisions• The network planning process


Location OverviewWhat's located?· Sourcing points- Plants- Vendors- Ports· Intermediate points- Warehouses- Terminals- Public facilities (fire, police, and ambulance

stations)- Service centers· Sink points- Retail outlets- Customers/Users


Location Overview (Cont’d)

Key Questions

· How many facilities should there be?

· Where should they be located?

· What size should they be?

Why Location is Important· Gives structure to the network· Significantly affects inventory and

transportation costs· Impacts on the level of customer service to

be achieved


Methods of Solution· Single warehouse location– Graphic– Grid, or center-of-gravity, approach· Multiple warehouse location– Simulation– Optimization– Heuristics

Location Overview (Cont’d)


Nature of Location AnalysisManufacturing (plants & warehouses)

Decisions are driven by economics. Relevant costs such as transportation, inventory carrying, labor, and taxes are traded off against each other to find good locations.

Retail

Decisions are driven by revenue. Traffic flow and resulting revenue are primary location factors, cost is considered after revenue.

Service

Decisions are driven by service factors. Response time, accessibility, and availability are key dimensions for locating in the service industry.


Weber’s Classification of Industries


Agglomeration

Based on the observation that the output of one industry is the input of another. Customers for an industry’s products are the workers of those industries. Hence, suppliers, manufacturers, and customers group together, especially where transportation costs are high. Historically, the growth of the auto industry showed this pattern. Today, the electronics industry (silicon valley) has a similar pattern although it is less obvious since the product value is high and transportation costs are a small portion of total product price.


Method appraisal· A continuous location method· Locates on the basis of transportation costs alone

The COG method involves· Determining the volumes by source and destination

point· Determining the transportation costs based on

$/unit/mi.· Overlaying a grid to determine the coordinates of

source and/or destination points· Finding the weighted center of gravity for the graph

COG Method


COG Method (Cont’d)

i ii

i iii

i ii

i iii

RV

YRVY,

RV

XRVX

where

Vi = volume flowing from (to) point I

Ri = transportation rate to ship Vi from (to) point i

Xi,Yi = coordinate points for point i

= coordinate points for facility to be locatedY,X


COG Method (Cont’d)Example Suppose a regional medical warehouse is to be established to serve several Veterans Administration hospitals throughout the country. The supplies originate at S1 and S2 and are destined for hospitals at H1 through H4. The relative locations are shown on the map grid. Other data are: Note rate is a

per mile costPointi

Prod-ucts Location

Annualvolume,

cwt.

Rate,$/cwt/

mi. Xi Yi1 S1 A Seattle 8,000 0.02 0.6 7.32 S2 B Atlanta 10,000 0.02 8.6 3.03 H1 A & B Los

Angeles5,000 0.05 2.0 3.0

4 H2 A & B Dallas 3,000 0.05 5.5 2.45 H3 A & B Chicago 4,000 0.05 7.9 5.56 H4 A & B New York 6,000 0.05 10.6 5.2


COG Method (Cont’d)Map scaling factor, K



Solve the COG equations in table form

i Xi Yi Vi Ri ViRi ViRiXi ViRiYi1 0.6 7.3 8,000 0.02 160 96 1,1682 8.6 3.0 10,000 0.02 200 1,720 6003 2.0 3.0 5,000 0.05 250 500 7504 5.5 2.4 3,000 0.05 150 825 3605 7.9 5.5 4,000 0.05 200 1,580 1,1006 10.6 5.2 6,000 0.05 300 3,180 1,560

1,260 7,901 5,538


COG Method (Cont’d)Now,

X = 7,901/1,260 = 6.27

Y = 5,538/1,260 = 4.40

This is approximately Columbia, MO.

The total cost for this location is found by:

where K is the map scaling factor to convert

coordinates into miles.

i iiii YYXXKRVTC 22 )()(


COGCOG Method (Cont’d)



2,360,882Total

660,4920.056,0005.210.66

196,6440.054,0005.57.95

160,7330.053,0002.45.54

561,7060.055,0003.02.03

271,8250.0210,0003.08.62

509,4820.028,0007.30.61

TCRiViYiXiiCalculate total cost at COG

221

4.40)(7.36.27)(0.6)(500)8,000(0.02TC


Note The center-of-gravity method does not necessarilygive optimal answers, but will give good answers if there area large numbers of points in the problem (>30) and thevolume for any one point is not a high proportion of the totalvolume. However, optimal locations can be found by theexact center of gravity method.

åå

åå ==

i iii

i iiiin

i iii

i iiiin

/dRV

/dYRVY,

/dRV

/dXRVX

where

22 )Y(Y)X(Xdn

i

n

ii

and n is the iteration number.



Solution procedure for exact COG


1) Solve for COG2) Using find di

3) Re-solve for using exact formulation4) Use revised to find revised di

5) Repeat steps 3 through 5 until there is no change in

6) Calculate total costs using final coordinates

Y,XY,X

Y,X

Y,X


· A more complex problem that most firms have. · It involves trading off the following costs:

Transportation inbound to and outbound from the facilities Storage and handling costs Inventory carrying costs Production/purchase costs Facility fixed costs

· Subject to: Customer service constraints Facility capacity restrictions

· Mathematical methods are popular for this type of problemthat:

Search for the best combination of facilities to minimizecosts

Do so within a reasonable computational time Do not require enormous amounts of data for the analysis

Multiple Location Methods


Location Cost Trade-Offs

Number of warehouses

Cos

t

Production/purchaseand order processing

Inventory carryingand warehousing

Warehousefixed

Inbound andoutboundtransportation

Total cost

00


Multiple COG· Formulated as basic COG model· Can search for the best locations for a selected number of

sites.· Fixed costs and inventory consolidation effects are handled

outside of the model.

A multiple COG procedure· Rank demand points from highest to lowest volume· Use the M largest as initial facility locations and assign

remaining demand centers to these locations· Compute the COG of the M locations· Reassign all demand centers to the M COGs on the basis

of proximity· Recompute the COGs and repeat the demand center

assignments, stopping this iterative process when there is no further change in the assignments or COGs


· Location of truck maintenance terminals

· Location of public facilities such as offices, and police and fire stations

· Location of medical facilities

· Location of most any facility where transportation cost (rather than inventory carrying cost and

facility fixed cost) is the driving factor in location

· As a suggestor of sites for further evaluation

Examples of Practical COG Model Use


· A method used commercially- Has good problem scope- Can be implemented on a PC- Running times may be long and memory requirements substantial

- Handles fixed costs well- Nonlinear inventory costs are not well

handled

· A linear programming-like solution procedure can be used (MIPROG in LOGWARE)

Mixed Integer Programming


Example

The database for the illustrated problem is prepared as fileILP02.DAT in MIPROG. The form is dictated by the problemformulation in the technical supplement of the location chapter.

The solution is to open only one warehouse (W2) with resultingcosts of:Category CostProduction $1,020,000Transportation 1,220,000Warehouse handling 310,000Warehouse fixed 500,000 Total $3,050,000

MIP (Cont’d)


Plant P1

Production = $4/cwt.Capacity =60,000 cwt.

Plant P2

Production = $4/cwt.Capacity =Unrestricted

$0/cwt.Customer C1

50,000 cwt.

Customer C2

100,000 cwt.

Customer C3

50,000 cwt.

Warehouse W1

Warehouse W2

Handling = $2/cwt.

Handling = $1/cwt.

Plant P1


Plant P2


$0/cwt.Customer C1

20,000 cwt.

Customer C2

30,000 cwt.

Customer C3

60,000 cwt.

Warehouse W1

Warehouse W2

Handling = $2/cwt.

Handling = $1/cwt.

Product 1

Product 2

Fixed = $100,000Capacity = 110,000 cwt.

Fixed = $500,000Capacity = Unrestricted

MIP (Cont’d)

A Multiple Product Network Design Problem

The situation


Mixed Integer programming


Guided Linear ProgrammingSearches for the best locations. A typical problem is shown in the next figure and can be configured as a transportation problem of linear programming in terms of its variable costs. · The inventory and warehouse fixed costs are computed as per-unit

costs, but this depends on the warehouse throughput. They are placed along with the variable costs in the linear programming problem.

· The linear programming problem is solved. This gives revised

demand assignments to warehouses. Some may have no throughput and are closed.

· Fixed costs and inventory carrying costs are recomputed based on

revised warehouse throughputs. · When there is no change in the solution from one iteration to the

next, STOP.


Plant P1


Plant P2


$0/cwt.Customer C1

50,000 cwt.

Customer C2

100,000 cwt.

Customer C3

50,000 cwt.

Warehouse W1

Warehouse W2

Handling = $2/cwt.Capacity = 60,000 cwt.Fixed = $100,000

Handling = $1/cwt.Capacity = UnrestrictedFixed = $400,000

Inventory carrying cost =100(Throughput)0.7

Guided Linear Programming (Cont’d)The situation

Guided Linear Programming (Cont’d)Example· Use data from the network figure.· Allocate the fixed costs. Assume initially that each warehouse handles the

entire volume. Hence,

Warehouse 1100,000/200,000 = $0.50/cwt.

Warehouse 2400,000/200,000 = $2.00/cwt.

· Allocate inventory carrying costs. Assume that throughput for each product isequally divided between warehouses.

Each warehouse

100{[200,000/2)0.7]/(200,000/2)} = $3.2

· Build the cost matrix for the transportation problem of LP. Note its specialstructure.

Total customer demand

No. of whses

Matrix of Cell Costs and Solution Values for the First Iteration in the Example Problem Warehouses Customers

W1 W2 C1 C2 C3

Plant &warehousecapacities

P1

4a

60,0009 99

b99 99

60,000

P2

8 6140,000

99 99 99999,999

c

W1

0 99 9.7d

8.760,000

10.760,000

W2

99b

0 8.2e

50,0007.2

40,0008.2

50,000 999,999c

Warehousecapacity &customerdemand 60,000 999,999

c50,000 100,000 50,000

Plants

Warehouses

a Production plus inbound transport rates, that is, 4 + 0 = 4.b Used to represent an infinitely high cost.c Used to represent unlimited capacity.d Inventory carrying, warehousing, outbound transportation, and fixed rates, that is, 3.2 + 2 + 4 + 0.5 = 9.7.e 3.2 + 1 + 2 + 2.0 = 8.2.

Guided Linear Programming (Cont’d)· Solve as an LP problem, transportation type. Note the results in the

solution matrix.· Recompute the per-unit fixed and inventory costs from the first solution

results as follows.

1 + 2 + 3.57 + 2.86 = 9.43

Ware-house

Per-unit Fixed Cost,$/cwt.

Per-Unit Inventory Carrying Cost,$/cwt.

W1 $100,000/60,000 cwt.= 1.67

$100(60,000 cwt.)0.7/60,000 cwt.= 3.69

W2 $400,000/140,000 cwt.= 2.86

$100(140,000 cwt.)0.7/140,000 cwt.= 2.86

· Place these per-unit costs along with transportation costs in thetransportation matrix. The portion of the matrix that is revised is:

C1 C2 C3

W1 11.36a 10.36 12.36W2 8.72b 7.72 8.72

a 2 + 4 + 1.67 + 3.69 = 11.36b


The revised solution shows that all volume should be produced at plant 2and all customers served from warehouse 2. Once the throughputs are known, they are used to compute the cost

summaries shown in the solution table. Actual costs are used, not thecell costs of the LP problem. The solution is:

Guided Linear Programming (Cont’d)

The iterative process is repeated until there are no changes. The answeris to have only warehouse 2 open.

Cost typeWarehouse 1

0 cwt.Warehouse 2200,000 cwt.

Production $0 200,000 ´4 = $800,000Inbound transportation 0 200,000 ´2 = 400,000Outbound transportation

0

50,000 ´2 = 100,000100,000 ´1 =

100,000 50,000 ´2 = 100,000Fixed 0 400,000Inventory carrying 0 100(200,000)0.7 = 513,714Handling 0 200,000 ´1 = 200,000 Subtotal $0 $2,613,714

Total $2,613,714


Location by Simulation

•Can include more variables than typical algorithmic methods

•Cost representations can be precise so problem can be more accurately described than with most algorithmic methods

•Mathematical optimization usually is not guaranteed, although heuristics can be included to guide solution process toward satisfactory

solutions

•Data requirements can be extensive

•Has limited use in practice


Sh

yco

n/M

affe

i Sim

ula

tio

nStart

Preprocessingprogram

Read inall customerorder data

and locations

Volumeshipment

orders

Orders filledthrough ware-

housing system

Testprogram

Read inwarehouse

locationconfiguration

to be evaluated

Read infreight rates,

warehousing costs,taxes, etc.

Cost of warehouselocation configuration

Is anotherrun desired?

Stop

Yes

No


Commercial Models for Location

Features

• Includes most relevant location costs

•Constrains to specified capacity and customer service levels

•Replicates the cost of specified designs

•Handles multiple locations over multiple echelons

•Handles multiple product categories

•Searches for the best network design

Co

mm

erci

al M

od

els

(Con

t’d)

Plants/vendors/

ports/sources

Level 2warehousesor stocking

points

Level 1warehousesor stocking

points

Customers/demandcenters/

sinks

Supply

Demand

Production/purchase costs

Inventory &warehousing

costsInventory &

warehousingcosts

Transportation costs

Commercial Models (Cont’d)


Dynamic Location

Retail Location

Methods

· The general long-range nature of the location problem- Network configurations are not implemented immediately- There are fixed charges associated with moving to a new

configuration

· We seek to find a set of network configurations that minimizes the presentvalue over the planning horizon

· Contrasts with plant and warehouse location.- Revenue rather than cost driven- Factors other than costs such as parking, nearness to competitive

outlets, and nearness to customers are dominant

· Weighted checklist - Good where many subjective factors are involved - Quantifies the comparison among alternate locations

A Hypothetical Weighted Factor Checklist for a Retail Location Example

a Weights approaching 10 indicate great importance.b Scores approaching 10 refer to a favored location status.

(1)FactorWeight

(1 to 10)a Location Factors

(2)

Factor Score(1 to 10)b

(3)=(1)´ (2)

WeightedScore

8 Proximity to competing stores 5 405 Space rent/lease

considerations 3 15

8 Parking space 10 807 Proximity to complementary

stores 8 56

6 Modernity of store space 9 549 Customer accessibility 8 723 Local taxes 2 63 Community service 4 128 Proximity to major

transportation arteries 7 56 Total index 391

keputusan lokasi fasilitas dalam jaringan distribusi.ppt

Documents