QFPM Thesis

8/12/2019 QFPM Thesis

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Quantitative Facilities Planning

Model Application and Improvement

In

Brgy. Angeles, San Antonio, ZambalesPaddy Rice Post-harvest Facilities
http://en.wikipedia.org/wiki/File:Oryza_sativa_of_Kadavoor.jpg


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Chapter 1 : Problem and Its Background

Rice Agriculture is the mainstay of San

Antonios basic industry in which highly

reflective on the well being of the whole

system

The need to develop paddy rice post-harvest

facilities to eliminate wastes and obtain food

security to San Antonio,Zamblaes.


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Chapter 1 : Problem and Its

Background

The need for paddy rice post-harvest facilities

to maintain efficiency with the utilization of

industrial engineering approach using facilties

planning and design.
http://en.wikipedia.org/wiki/File:Mirrool_Silos.jpg


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B.Statement of the Problem

What is the optimum facility location needed in paddyrice post-harvest farming facilities of San Antonio,Zambales? Is it advantageous to acquire this kind offacility?

What is the best distribution pattern needed to providethe least resources on rice product route dynamics? Isthe used of management science relevant in itscontinuous operations?

Is there a significant difference in the acceptability offacilities design recommendation and scientificmanagement when utilize in San Antonio, Zambalespaddy post-harvest rice farming?


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C. Objective of the Study

The main purpose of the study is to provide anoptimum facilities design and improving theperformance of rice farming agriculture in SanAntonio, Zambales. Specifically, it attempts to answer

the following research objectives: To determine the best location of facilities using

central gravity method and other techniques likequantitative facilities planning models which involvesrectilinear facility location problem, single location

minimax facility problem, quadratic assignmentproblem and location allocation models in San Antonio,Zambales rice farm


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C. Objective of the Study

To find out the optimized distance routing usingnetwork modeling technique and transportationproblem in San Antonio, Zambales municipal

agricultural lands. To find if there is a significant difference in

facilities installation, improvement andsystemization to the rice Farming performance

based on recommended industrial calculation ormeasurements.


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D. Theoretical/Conceptual Framework of

the Study

Independent VariablesRice FarmingFacilities, Routing,Machineries

Intervening VariablesWater supply, energy, pest,

weather, climate, arableland and fertilizer

Dependent VariablesEfficiency related

problems andperformance correlation


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Chapter 2: Review on Literature and

Study

This chapter will discuss the articles and past

studies that are relevant to the present

study. It will also elucidate the tools that

researchers utilized in the progress of the

study, and how it will be conducted properly.


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Study

A.Related Literature

1.Local Related Literature

LGU Ozamiz, DA, MISA to ink MOA for rice productionfacility

The Ozamiz city,Misamis OccidentalGovernment launched an on-farm mechanizationprogram with the mechanization of facilities and

equipment for the local farmers.This development hadbeen a great stride to paddy rice post harvest industry


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Study

Related Local Literature

Dr. Adolfo Necesito (February 1, 2011).

Philippines: Rice Research Needs New

Direction to Overcome Rice Crisis The current local production of rice is not

sufficient to meet the countrys consumption

demand, thus the Government has increased

the quantities of rice imports. The Philippines isamong the lowest producer of rice with only

four tons per hectare yield, according to the

Department of Agriculture
http://www.searca.org/index.php/knowledge-management/seminar-series/375-philippines-rice-research-needs-new-direction-to-overcome-rice-crisishttp://www.searca.org/index.php/knowledge-management/seminar-series/375-philippines-rice-research-needs-new-direction-to-overcome-rice-crisishttp://www.searca.org/index.php/knowledge-management/seminar-series/375-philippines-rice-research-needs-new-direction-to-overcome-rice-crisishttp://www.searca.org/index.php/knowledge-management/seminar-series/375-philippines-rice-research-needs-new-direction-to-overcome-rice-crisis


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Study

2.Foreign Related Literature

Hillikers(2011) article on the steep cost of

cheap food stated that facilities in agricultural

sector minimized post-harvest losses.(Related

literature by Joel Hilliker.2011.The Steep Cost

of Cheap Food.28(11):10(June 2011).


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Study

B.Related Studies

1. Related Local Studies

The study on the best possible rice post-harvest

facilities in the Philippines has been continuing for

this past year of 2013 because of loses that

reached one million tons of rice every year


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Study

2. Related Foreign Studies

In 2014, Daniel Benz and Samer Ijaz,

Junior and Senior in Supply ChainManagement at the University of

Illinois at Urbana-Champaign studied

on rice post-harvest facilities that willhelp minimize losses.


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Chapter 3:Research Methodology

This chapter describes the research design

and the research model. The research design

contains the focus of the study, as described in

the statement of objectives and analysis used

to address the objectives.


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Input: Product,routing, layout,support services,time andactivities

FlowAnalysis

RelationshipAnalysis

SpaceRequirement

Spaceavailability

LocationAnalysis

Space

RelationshipDiagram

Lay-outalternatives

Evaluation


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Facility Location Logistics Management

Factors that Affect Location Decisions

Distance Measures Classification of Planar Facility Location Problems

Planar Single-Facility Location Problems

Minisum Location Problem with Rectilinear Distances

Minisum Location Problem with Euclidean Distances

Minimax Location Problem with Rectilinear Distances

Minimax Location Problem with Euclidean Distances

Planar Multi-Facility Location Problems



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Logistics Managementcan be defined as the management of the

transportation and distribution of goods. The term goods includes raw

materials or subassemblies obtained from suppliers as well as finished

goods shipped from plants to warehouses or customers.

Logistics management problems can be classified into three categories:

Location Problems: involve determining the location of one or morenew facilities in one or more of several potential sites. The cost of

locating each new facility at each of the potential sites is assumed to

be known. It is the fixed cost of locating a new facility at a particular

site plus the operating and transportation cost of serving customers

from this facility-site combination.


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Allocation Problems: assume that the number and

location of facilities are known a priori and attempt to

determine how each customer is to be served. In other

words, given the demand for goods at each customer

center, the production or supply capacities at each facility,

and the cost of serving each customer from each facility,

the allocation problem determines how much each facility

is to supply to each customer center.

Location-Allocation Problems:involve determining not

only how much each customer is to receive from each

facility but also the number of facilities along with their

locations and capacities.


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Planar Single-Facility Location Formulations Minisum Formulation:

Min f(x) = wid(X, Pi)

where X = (x, y) : location of the new facility

Pi= (ai, bi) : location of the i-th exiting facility, i = 1, , m

wi: weight associated to the i-th exiting facility

For example, wi= ,

where ci: cost per hour of travel, ti: number of trips per month,vi: average velocity.

Minimax Formulation:

Min f(x) = Max {wid(X, Pi)} Min z

s. t. wi

d(X, Pi

) z, i = 1, , m


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Insights for the Minisum Problem with EuclideanDistance

Majority Theorem:

When one weight constitutes a majority of the total weight, an optimal new

facility location coincides with the existing facility which has the majority

weight.

w5w1

w4

w2

w3

P1P2

P3

P4

P5

Weight proportional to wi

String

Hole

Horizontal

pegboard


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Majority Theorem:

When one weight constitutes a majority of the

total weight, an optimal new facility location

coincides with the existing facility which hasthe majority weight.


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As follows


Min f(x, y) =

Note that f(x, y) = f1(x) + f2(y)

where f1(x) =

f2(y) =

The cost of movement in the x direction is independent of the cost ofmovement in the y direction, and viceversa.

Now, we look at the x direction.

f1(x) is convexa local min is a global min.

w [|x a | |y b |i i ii=1

m

]

w |x a |i ii=1

m

w |y b |i ii=1

m


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Minisum Location Problem with Rectilinear

Distances

As follows: The coordinates of the existing facilities are sorted so that

a1a2a3.

Now, we consider the case of m = 3.

Case x a1:

f1(x) = w1 |a1- x| + w2|a2- x| + w3|a3- x|

= - (w1 + w2+ w3)x + w1 a 1+ w2 a 2+ w3 a 3

= - W x + w1 a 1+ w2 a 2+ w3 a 3, where W = w1 + w2+ w3

Case a1x a2:

f1(x) = w1 |a1- x| + w2|a2- x| + w3|a3- x|

= (w1- w2- w3)x - w1 a 1+ w2 a 2+ w3 a 3

= (- W + 2 w1) x - w1 a 1+ w2 a 2+ w3 a 3


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Minisum Location Problem with

Rectilinear Distances

ExampleFind the optimal location of facility with he

respect to four (known) possible locations

which coordinates are P1=(6,11), P2=(12,5),

P3=(14,7), and P

4=(10,16). The objective is to

minimize the maximum distance from the

existing facility location to new facility

location and from the new facilty location to

its existing faciltity. The distances from the

locations to their closest existin facility are

h1=10, h2=16, h3=14, and h4=11. Assume

that distances are rectilinear. If multiple

optima exist, find all optimal solutions.

(5, 4) 6 8 10 12 14

(6, 11)

(10, 16)

(12, 5)

(10, 7)

(14, 7)

(12, 9)

h4= 11

h2= 16

h3= 14

h1= 10

16

14

12

10

8

6


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Notation

EF (existing facilities) locations : Pi= (ai, bi), i = 1, , m

NF (new facility) location : X = (x, y)

Travel distance from EF i to the nearest Facility = hi, i = 1, , m

Travel distance from NF to EF i = |x - ai| + |y - bi|

Formulation :

Min g(x, y)

where g(x, y) = max {|x - ai| + |y - b

i| + h

i}

Min z

s. t. |x - ai| + |y - bi| + hiz, i = 1, , m

Minimax Location Problem with Rectilinear

Distances (cont.)


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End

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QFPM Thesis