Application of AHP in Development of Multi-Criteria Ergonomic Approach for Choosing the Optimal...

Application of AHP in Development of

Multi-Criteria Ergonomic Approach for Choosing

the Optimal Alternative for Material Handling- A

Case Study and Software Development to

Facilitate AHP Calculation

Md. Fashiar Rahman, Md. Bony Amin, Mahmud Parvez Department of Industrial Engineering and Management

Khulna University of Engineering & Technology

Khulna-9203, Bangladesh

Abstract— Manual materials handling is a known risk in

industry period. There should be no argument. Manual

handling task are responsible for a large proportion of work

related injuries and long term health problems amongst worker

in manufacturing industry. The ergonomic systems design

model requires an analysis of the key characteristics of a job and

its component tasks before potential solutions can be identified.

The Analytic Hierarchy Process (AHP) is a multi-criteria

decision making (MCDM) method that helps the decision-maker

facing a complex problem with multiple conflicting and

subjective criteria The objective of this paper is to demonstrate

the application of the Analytic Hierarchy Process (AHP), a

popular multi-criteria decision support tool, in development of

multi-criteria ergonomic approach for the selection of (best)

material handling way in an industry and also introduce a

software which is able to calculate local priorities and

consistency ratio. One of the major problems that modern

companies have a significant source of worker absence and high

costs due to compensation claims due to risks involved in

manual load handling. The examples of factors that influence

the choice of material handling procedure include i.e.:

anthropometry and biomechanics.

Keywords—AHP, MCDM, Ergonomic, Material Handling,

Software development.

I. INTRODUCTION

The Analytic Hierarchy Process (AHP) is a popular

decision support method developed in the 1970s by American

mathematician, Thomas L. Saaty. Since then it has been used

in real environment, including business, healthcare, politics

and education. There are many organizations that applied this

method in making their decisions. For example, IBM used

AHP to design the AS/400 computer as part of its quality

improvement strategy, and win the Baldridge Quality Award

[1]. The Nuclear Regulatory Commission (NRC) of the US

applied AHP to allocate money in information technology

projects with many competing priorities. The Xerox

Company also used this method for similar purpose. The

AHP was chosen as a decision support tool in many political

and military applications, i.e. whether to build or not to build

the National Missile Defense system in 2002 [2]. Over the

last three decades, a number of methods have been developed

which use pairwise comparisons of the alternatives and

criteria for solving multi-criteria decision-making (MCDM)

between finite alternatives. The analytic hierarchy process

(AHP) proposed by Saaty is a very popular approach to

multi-criteria decision-making (MCDM) that involves

qualitative data. In the pairwise comparison method, criteria

and alternatives are presented in pairs of one or more referees

(e.g. experts or decision makers). It is necessary to evaluate

individual alternatives, deriving weights for the criteria,

constructing the overall rating of the alternatives and

identifying the best one [3].

II. PROBLEM MODELING

Manual handling is any transporting or supporting of a

load by one or more workers. It includes the following

activities: lifting, holding, putting down, pushing, pulling,

carrying or moving of a load. The main risk factors or

conditions associated with the development of injuries in

MMH tasks include [4]:

Awkward postures (e.g. bending, twisting);

Repetitive motions (e.g. frequent reaching, lifting,

carrying);

Forceful exertions (e.g. carrying or lifting heavy

loads);

Pressure points (e.g. grasping [or contact from]

loads, leaning against parts or surfaces that are hard

or have sharp edges);

Static postures (e.g. maintaining fixed positions for

long periods of time).

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Repeated or continual exposure to one or more of these

factors may initially lead to fatigue and injuries. Injuries can

include damage to muscles, tendons, ligaments, nerves, and

blood vessels. Repetitive high-exertion lifting is a major

contributor to injuries of the low back [5]. MMH activities

are a significant source of worker absence and high costs due

to compensation claims. To reduce the risks involved in

manual load handling, engineers specify the use of material

handling devices (MHDs) to eliminate or reduce the lifting

requirements in MMH in many industrial facilities. Among

the major MHDs mentioned are chain blocks, cranes, hoists,

industrial manipulators, jib cranes and overhead cranes [4].

Fig. 1. Schematic representation of the hierarchy

The basic problem of decision-making is to choose the best

option from a set of competing alternatives that are evaluated

under conflicting criteria. The AHP is a multi-criteria

decision-making tool developed in the 1970s by Saaty (1980)

to solve a specific class of problems that involve

prioritization of potential alternative solutions that considers

both qualitative and quantitative criteria (Henderson and

Dutta, 1992). This technique consists of a systematic

approach based on breaking the decision problem into a

hierarchy of interrelated elements. Such a structure clarifies

the problem and presents the contribution of each of the

elements to the final decision. Two features of the AHP

differentiate it from other decision-making approaches. First,

it provides a comprehensive structure that combines the

intuitive rational and irrational values during the decision

making process. Second, the AHP has the ability to judge the

consistency in the decision-making process (Akarte et al.,

2001) [4]. The advantage of the AHP is its flexibility, ease of

use, and the ability to provide a measure of the consistency of

the decision maker’s judgment (Park and Lim, 1999). In

addition, this method allows the incorporation of tangible and

intangible factors that would otherwise be difficult to take

into account [6]. In this paper the Goal is to satisfy the

company objectives with consideration of ergonomic and

productive elements. The main sources of hierarchies relevant

to choose the optimal alternative for “manuable” material

handling were: Jung and Jung (2001), Chan et al. (2001), and

Henderson and Dutta (1992).Jung and Jung (2001)

decomposed the focus “Intensity of perceived workload” into

a hierarchy of 4 Criteria (physical job demand, environmental

factors, postural discomfort, mental job demand) and 13 Sub-

criteria: weight, frequency, duration, and distance for

physical job demand; working climate, lighting, noise,

vibration, and exposure to chemicals for environmental

factors; standing, stopping, squatting, and twisting for

postural discomfort. The elements concerned only ergonomic

and safety aspects. To evaluate these elements, their benefit

was not stated, the indicators were defined as sets of

linguistic values. Chan et al.(2001) developed a hierarchy for

“The best commercial AVG model selection” providing 4

Criteria and 15 Sub-criteria: performance measures with

speed, load capacity, accuracy, efficiency, and repeatability;

technical with maintenance, convenience, compatibility,

technological risk, and safety; economic with initial cost, and

operating cost; strategic with flexibility, manufacturer, and

future plan. The elements concern both production

performance and ergonomics and safety performance.

Satisfaction of company

objectives

Ergonomics and safety

performance Production performance

Cognitive

Ergonomics

Work

Management

Safety Productivity Adaptability Capability Flexibility Anthropometry

and

biomechanics

-Easy to

Understand

-Easy to use

-Competence

and Training

-Work

experience

-Training

procedure

-Mechanical

Hazards

-Work clothing

and PPE

-Production

capacity

-Investment

cost

-Generality

- Elasticity

-Efficiency

and

Effectiveness

-Customer

satisfaction

-Lighting

and

carrying

-Constraints

on the layout

-Lifting and

Carrying

-Pushing and

Pulling

-Posture

-Visual

requirement



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However, the latter is represented by one Sub-criterion

without further specifications. Henderson and Dutta (1992)

used the AHP in analysis of ergonomics guidelines. They

focused on manual lifting utilizing 9Criteria that are the main

risk factors (ISO 11228-1, 2003): frequency of lifting,

distance lifted, height lifted, size of load, design of load,

location of load, worker’s size, worker’s gender, worker ’ s

age. Other types of manual material handling (i.e. pushing,

pulling, carrying, and moving of a load) and Criteria of

production performance were not considered [4].

In this paper the Strategic Criteria are Ergonomics and safety

performance and Production performance. There are 4

Criteria related to Ergonomics and safety performance,

namely Anthropometry and biomechanics, Cognitive

ergonomics, Work management, and Safety. The criteria

associated with Production performance are Productivity,

Adaptability, Capability, and Flexibility. The last level of the

hierarchy is composed of 20 Sub-criteria. The schematic

representation of the hierarchy is showed in fig 1.

III. METHODOLOGY

A. Making Pairwise Comparisons and Obtaining the

Matrices of Element Evaluation

In this step, the elements of each level are compared

pairwise, weighting them as a function of their importance for

corresponding element of the higher level. The aim is to

construct a set of pairwise comparison matrices for each of

the lower levels of elements. An element in the higher level

governs the elements in the lower level [7].

Following each branch point in the hierarchy, the importance

of each element is compared, in turn, with every other

element immediately below that branch point. Evaluation,

denoted as A, will be formed using the comparisons. Each

entry aij of the matrix, in the position (i, j), is obtained

comparing the row element Ai with the column element Aj.

Where: aij is the relative importance of the element Ai respect

to the element Aj. The comparison of any two elements Ai

and Aj with respect to the higher level element is made using

questions of the type: “How much more is the element Ai

preferred over the element Aj under the higher level

element?” Saaty (1980) suggests the use of a 9-point

linguistic scale to convert the verbal responses into numerical

quantities representing the values of aij [4]. The scale is

explained in Table 1.

TABLE I. SCALE OF RELATIVE IMPORTANCE

ACCORDING TO SAATY (1980) AND SAATY (1987).

Intensity

of importance Definition

1 Equal importance between Ai and Aj

3 Weak or moderate importance of Ai over Aj

5 Essential or strong importance of Ai over Aj

7 Demonstrated or very strong importance of

Ai over Aj

9 Absolute or strong importance of Ai over Aj

2,4,6,8 Intermediate

B. Local Priorities / Eigen Vector Calculation

There are several methods for calculating the eigenvector.

The goal is to find a set of priorities p1… pn such that pi /pj

match the comparisons aij in a consistent matrix and when

slight inconsistencies are introduced, priorities should vary

only slightly. Different methods have been developed to

derive priorities.

1) Method 1:Psychologists using pair-wise matrices

before Saaty used the mean of the row. This old method is

based on three steps [8].

a) Sum of the elements of each column j :

b) Dividing each value by its column sum:

c) Mean of row i :

The table below gives a worked example in terms of five

attributes to be compared.

Production

performance PR AD CA FL

Local

priorities

Productivity (PR) 1 4 1/4 3 ……..

Adaptability (AD) 1/4 1 1/8 2 ……..

Capability (CA) 4 8 1 6 ……..

Flexibility (FL) 1/3 1/2 1/6 1 ……..



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The method ―mean of row derives the priorities as follow

a. Adding the elements of the columns: (5.58; 13.5;

1.542; 12)

b. Normalizing the columns

Production


Local

priorit

ies

Productivity

(PR) 0.179 0.29 0.162 0.25 ……..

Adaptability

(AD) 0.0448 0.0741 0.0812 0.1667 ……..

Capability

(CA) 0.7169 0.5926 0.6485 0.5 ……..

Flexibility

(FL) 0.059 0.0370 0.1081 0.0833 ……..

c. Calculating the mean of the rows: (0.22; 0.0917;

0.6145;0.07185)

TABLE II.

RANDOM INDEX VALUE (SAATY,

1980)

N

1

2

3

4

5

6

7

8

9

10

RI

0.00

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

Production


Local

priorities

Productivity (PR) 1 4 1/4 3 0.22

Adaptability (AD) 1/4 1 1/8 2 0.0917

Capability (CA) 4 8 1 6 0.6145

Flexibility (FL) 1/3 1/2 1/6 1 0.07185

2) Method 2: In the case of the introduction of small

inconsistency, we can decently think that it induces only a

small distortion. Based on this idea, Saaty (1977) uses the

perturbation theory to justify the use of the principal

eigenvector p as the desired priorities vector (3). He argues

that slight variations in a consistent matrix imply slight

variations of the eigenvector and the eigenvalue.

Erg

ono

mic

s

and

Saf

ety

Per

form

ance

AB

CE

WE

WM

SA

nth

roo

t o

f

pro

duct

of

val

ues

Eig

en v

ecto

r

Anthropometry

and

biomechanics

(AB)

1 5 3 4 1/3 1.821 0.3

Cognitive

ergonomics

(CE)

1/5 1 1/2 1/4 1/3 0.384 0.063

Work

environment

(WE)

1/3 4 1 1/2 1/4 0.699 0.116

Work

management

(WM)

1/4 2 2 1 1/3 0.803 0.133

Safety (SA) 3 3 4 2 1 2.352 0.388

Total 6.059 1

According to this idea, after the matrices of element

evaluation have been developed, the next step is to calculate a

vector of local priorities or weights of elements in the matrix

A.

In terms of matrix algebra, this consists of calculating the

principal eigenvector w of the matrix by multiplying together

the entries in each row of the matrix and then taking the nth

root of that product gives a very good approximation to the

correct answer. The nth roots are summed and that sum is

used to normalize the eigenvector elements to add to 1.00 [9].

The table below gives a worked example in terms of five

attributes to be compared. Where, the Eigen vector for



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“Anthropometry and biomechanics” has been calculated as

(1.821/6.059) =0.3; “Cognitive ergonomics” as (0.384/6.059)

=0.063 and so on.

Here, A ω = ω

Where: is the largest eigenvalue of the matrix A and

the corresponding eigenvector ω contains only positive

entries. When the vector ω is normalized, it becomes the

vector of local priorities of the elements with respect to the

element of the higher level.

As priorities make sense only if derived from consistent

or near consistent matrices, a consistency check must be

applied. Saaty (1977) has proposed a consistency index (CI),

which is related to the eigenvalue method:

Where, n = dimension of the matrix; 𝜆max = maximal

eigenvalue.

The consistency ratio (CR), the ratio of CI and RI, is given

by:

Where, RI is the random index (the average CI of 500

randomly filled matrices). If CR is less than 10%, then the

matrix can be considered as having an acceptable

consistency. Saaty (1977) calculated the random indices

shown in table 2. If the CR of the matrix is high, it means that

the input values are not consistent, and hence are not reliable.

In general, a CR of 0.10 or less is considered acceptable. If

the CR is higher, the comparisons need to be revised in order

to improve their consistency.

The next stage is to calculate λmax so as to lead to the

Consistency Index and the Consistency Ratio. We first

multiply on the right the matrix of judgments by the

eigenvector, obtaining a new vector.

The calculation for the first row in the matrix is:

1*0.3+5*0.063+3*0.116+4*0.133+ (1/3)*0.388 = 1.623

For the second row

(1/5)*0.3+1*0.063+ (1/2)*0.116+ (1/4)*0.133+ (1/3)*0.388

= 0.344

For the third row

(1/3)*0.3+4*0.063+ 1*0.116+ (1/2)*0.133+ (1/4)*0.388 =

0.6315

For the fourth row

(1/4)*0.3+2*0.063+ 2*0.116+ 1*0.133+ (1/3)*0.388 = 0.695

For the fifth row

3*0.3+3*0.063+ 4*0.116+ 2*0.133+ 1*0.388 = 2.207

This vector of five elements (1.623, 0.344, 0.6315, 0.695,

2.207) is, of course, the product A ω and the AHP theory

says that A ω = ω so we can now get five estimates of

by the simple expedient of dividing each component

of (1.623, 0.344, 0.6315, 0.695, 2.207) by the corresponding

eigenvector element. This gives 1.623/0.3 =5.41 together

with 5.46, 5.44, 5.23 and 5.69. The mean of these values is

5.446 and that is our estimate for . If any of the

estimates for turns out to be less than n or 4 in this

case, there has been an error in the calculation, which is a

useful sanity check.

The Consistency Index (CI) for a matrix is calculated

from ( -n)/ (n-1) and, since n=5 for this matrix, the CI is

0.11. The final step is to calculate the Consistency ratio (CR)

for this set of judgments using the CI for the corresponding

value from large samples of matrices of purely random

judgments (RI) using Table 2.

So,

= 0.098

Hence CR is less than 0.10, so it is acceptable.

C. Making Pairwise Comparisons, Obtaining the Matrices of

Alternative Evaluation, Determining Local Priorities

Alternatives and Verifying the Consistency of

Comparisons

In this step, using the similar procedure described earlier

the local priorities of alternatives with respect to each

element of the lowest level can be estimated. In particular, the

alternatives are compared pairwise, scoring them as a

function of their relative preference with respect to each

element of the lowest level. The comparison of two

alternatives Mi and Mj is made using questions of the type:

“How much does the alternative Mi benefit over the

alternative Mj under the element?” The matrices of alternative

evaluation are consequently developed; it is possible to

calculate a vector of local priorities or scores of alternatives

and to verify the consistency of comparisons [1].

D. Determining Global Priorities of Alternatives

In the last step, the local priorities (scores) of an

alternative with respect to each element of the lowest level

are multiplied by the corresponding local priorities (weights)

of element of the lowest level. The sum of these products is

the global priority or final score of the alternative.

Determining global priorities of all alternatives, it is possible

to obtain the rating of the alternatives in achieving the goal of

the decision problem [1] [2].



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IV. LOCAL PRIORITIES OF ELEMENTS

The local priorities obtained for all of the elements, i.e.

the components of the normalized eigenvector of the pairwise

comparison matrix (element evaluation), are shown in Table

3, computing the pairwise comparison matrices (element

evaluation) in Appendix A.

From the analysis of Table 3, it is observed that the most

important Strategic Criterion that affects the satisfaction of

company objectives is Production performance, with a weight

of 0.750.

The Ergonomics and safety performance follows

Production performance, with a local priority of 0.250.

Among the criteria referenced to Production performance, the

most important item is Capability with a local weight of

0.628. The most important criterion referenced to

Ergonomics and safety performance is Anthropometry and

biomechanics with a local weight of 0.253.

CHART 1 : GLOBAL PRIORITIES OF ELEMENTS

The local priorities represent the relative weights of the

elements in a group with respect to the element above. The

global priorities are obtained by multiplying the local

priorities of the elements by the global priority of their above

element are shown in Table 4. For example, the global

priority of the sub-criterion Lifting and carrying (0.0 015) is

obtained multiplying the local priority of the same Sub-

criterion (0.242) by the local priority of the criterion

Anthropometry and biomechanics (0.253) by the local

priority of the strategic criterion Ergonomics and safety

performance (0.250). From each set of pairwise comparisons,

the element weight and consistency ratio were calculated

using Saaty’s eigenvector approach (1980). From CHART 1

it can be said that the most important sub- criteria is

Effectiveness (0.731) .That means it include most to the main

goal.

TABLE III. LOCAL PRIORITIES OF THE ELEMENTS

Erg

on

omic

s an

d S

afet

y p

erfo

rman

ce (

0.2

50)

An

thro

pom

etry

an

d

bio

mec

han

ics

(0.2

53) Lifting and carrying 0.242

Pushing and pulling 0.136

Posture 0.545

Visual requirement 0.07

Co

gn

itiv

e

erg

ono

mi

cs (

0.1

14)

Easy to understand 0.75

Easy to use 0.25

Wo

rk

man

agem

ent

(0.1

45

)

Competence and Training 0.594

Working procedure 0.065

Training procedure 0.34

Saf

ety

(0.0

485

) Mechanical Hazard 0.8

Work clothing and PPE 0.2

Pro

duct

ion

and

Per

form

ance

(0

.75

0)

Pro

du

ctiv

ity

(0.2

2)

Production capacity 0.143

Investment cost 0.857

Ad

apta

bil

ity

(0.0

79) Elasticity 0.5

Generality 0.5

Cap

abil

ity

(0.6

28)

Efficiency 0.108

Effectiveness 0.344

Customer satisfaction 0.546

Fle

xib

ilit

y

(0.0

689

) Required space 0.2

Constraints on the layout 0.8



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TABLE IV. GLOBAL PRIORITIES OF THE ELEMETS

Lifting and carrying 0.01530

Pushing and pulling 0.0086

Posture 0.0345

Visual requirement 0.0044

Easy to understand 0.0210

Easy to use 0.0071

Competence and Training 0.0215

Working procedure 0.0024

Training procedure 0.0123

Mechanical Hazard 0.0097

Work clothing and PPE 0.0024

Production capacity 0.0236

Investment cost 0.01414

Elasticity 0.0338

Generality 0.0338

Efficiency 0.188

Effectiveness 0.731

Customer satisfaction 0.081

Required space 0.0103

Constraints on the layout 0.0413

V. CASE STUDY

In an industry they used to convey their final product

from the store house to shipment truck manually by worker.

The tasks performed included the lifting of low-lying objects.

Actually the whole task is carrying the carton up to 50 meter

from the store house and after that released on to ground by a

worker. Then another worker lifts that carton from the ground

and put it on to the shipment truck. A forklift could be used

for this purpose. To choose the best method for this

conveying work AHP can be used.

The two alternatives (manual and forklift) are compared

with respect to each Sub-criterion, according to the AHP

procedure described earlier in this paper. To execute the

comparisons, the question is “How much does Forklift

benefit over Manual under Element?” For example, Table 5

represents the pairwise comparison matrix of the alternatives

with respect to the Lifting and carrying Sub-criterion and the

question is “How much does Forklift require less physical

effort then Manual under lifting and carrying?” In Table 5

also the local priorities are stated.

In table 5 the Forklift option was preferred by subjects 7

times over that of manual, the value 7 is entered into the (1,

2) position. The reciprocal value 1/7 is automatically entered

in the transpose position (2, 1).

TABLE V. EXAMPLE OF A PAIRWISE COMPARISON MATRIX

(ALTERNATIVE EVALUATION).

Lifting and

carrying Forklift Manual

Local

priorities

Forklift 1 7 0.875

Manual 1/7 1 0.125

All matrices of pairwise comparison (alternative evaluation

matrices) are in Appendix A.

According to AHP procedure, the final step is to weight

the results to obtain the final scores of the two alternatives,

i.e. the global priorities achieved and summarized in Table 6.

The values were calculated computing Table 4 and the

pairwise comparison matrices (alternative evaluation) in

Appendix.

TABLE VI. FINAL SCORES OF THE ALTERNATIVES

Alternatives Score

Forklift 1.02

Manual 0.276

The application of the AHP showed greater satisfaction of

the company objectives (the Goal) replacing manual handling

with forklift here.

VI. SOFTWARE TO CALCULATE LOCAL

PRIORITIES AND CONSISTENCY RATIO

Calculation of local priorities and consistency ratios are

more time consuming. So, software has been developed using

visual basic to minimize the effort to calculate the local

priorities and consistency ratio.

1) Software Algorithm :

Step1: Structure has been used to declare the order of

the matrix.

Step2: Input the name of the focus criteria

Step3: Input the name of the criteria or sub criteria to

make comparison.

Step4: Input the relative importance of an element over

another element according to saaty’s scale.

Step5: Show the local priorities and consistency ratio

for the given criteria or sub criteria.

2) Software Input:

Enter the order of the matrix: 3

Enter the focus criteria name: Work environment

Enter the name of the criteria / Sub-criteria:

“”Thermal environment””

“”Lighting environment””



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“”Noise exposure””

“”Space demands””

Enter the relative importance of “Thermal

environment” over “Lighting environment”: 4


environment” over “Noise exposure”: 6


environment” over “Space demands”: 3

Enter the relative importance of “Lighting

environment” over “Noise exposure”: 3

Enter the relative importance of “Lighting

environment” over “Space demands”: ¼

Enter the relative importance of “Noise exposure” over

“Space demands”: 1/5

3) Software Output:

Focus criteria:

Work environment

Consistency ratio:

0.07759

Local

priorities/

Eigen

vector

TE LE NE SD

0.5294 Thermal

environment

(TE)

1 4 6 3

0.1196 Lighting

environment

(LE)

0.25 1 3 0.25

0.0590 Noise

exposure

(NE)

0.17 0.33 1 0.2

0.2920 Space

demands

(SD)

0.33 4 5 1

4) Software Representation:

After initialization the following interface will appear.

Fig. 2. Interface to input order of the matrix.

Fig. 3. Updating the order of the matrix

After updating the order of the matrix it will ask to input the

focus criteria and after that it will also ask to input criteria or

sub-criteria related to focus criteria (Fig 4).

Fig. 4. Interface to input the type of criteria or sub-criteria

After updating focus criteria and related criteria or sub

criteria “Comparison” button will appear on screen. After

clicking “Comparison” button it will ask to type the

importance of an element over another element (Fig 5).

Fig. 5. Interface to input importance of the elements.

Fig. 6.

After updating the importance of the elements “Local

priorities/ Eigen vectors” button will appear on the screen.



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After clicking “Local priorities/ Eigen vectors” button a

“Show” button will appear on the screen. By clicking the

“Show” button software will show the comparative

importance of the elements, local priorities of every criteria

or sub-criteria, consistency ratio (Fig 6).

Fig. 7. Interface of the final output

VII. CONCLUSION

The main advantage of the AHP is its ability to rank

choices in the order of their effectiveness in meeting

conflicting objectives. Decisions that need support methods

are difficult by definition and therefore complex to model. A

trade-off between prefect modeling and usability of the model

should be achieved. It is our belief that AHP has reached this

compromise and will be useful for many other cases as it has

been in the past. The AHP is a useful technique for

discriminating between competing options in the light of a

range of objectives to be met. This widespread use is

certainly due to its ease of applicability and the structure of

AHP which follows the intuitive way in which managers

solve problems. The hierarchical modeling of the problem,

the possibility to adopt verbal judgments and the verification

of the consistency are its major assets. Expert Choice, the

user-friendly supporting software, has certainly largely

contributed to the success of the method. The limitations of

the AHP are that it only works because the matrices are all of

the same mathematical form – known as a positive reciprocal

matrix. Although the Analytic Hierarchy Process has been the

subject of many research papers and the general consensus is

that the technique is both valid and useful, there are critics of

the method. Their criticisms have included: A) since there is

no theoretical basis for constructing hierarchies, AHP users

can construct different hierarchies for identical decision

situations, possibly producing different solutions, B) AHP

rankings are claimed to be arbitrary because they are based

on subjective opinions using a ratio scale, a situation which

can produce "rank reversal," C) there are said to be flaws in

the methods of combining individual weights into composite

weights, and D) the process has no sound underlying

statistical theory. Along with its traditional applications, a

new trend, as compiled by the work of Ho (2008), is to use

AHP in conjunction with others methods: mathematical

programming techniques like linear programming, Data

Envelopment Analysis (DEA), Fuzzy Sets, House of Quality,

Genetic Algorithms, Neural Networks, SWOT-analysis. This

paper presents an AHP-based methodology to support the

resolution of a real-world problem: to select the best material

handling solutions evaluating ergonomic criteria and

production performance measures.

REFERENCES

[1]. Saaty, T.L. Relative Measurement and Its Generalization in Decision

Making. Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors. The Analytic Hierarchy / Network

Process. Rev. R. 8 Acad. Cien. Serie A. Mat. 2008, 102(2), 251-318.

[2]. Saaty, T.L. Decision making with the analytic hierarchy process. Int. J. Services Sciences, 2008, 1(1), 83-98.

[3]. José antonio alonso, mª teresa lamata “consistency in the analytic

hierarchy process: a new approach”, international journal of uncertainty, fuzziness and knowledge-based systems, vol.14, no. 4

(2006) 445-449.

[4]. Diana Rossi, Enrico Bertoloni, Marco Fenaroli, Filippo Marciano, Marco Alberti, “A multi-criteria ergonomic and performance

methodology for evaluating alternatives in “ manuable” material

handling”, International Journal of Industrial Ergonomics , Vol. 43, pp. 314-327.2013.

[5]. Research and Education Unit, Division of Occupational Safety and

Health, California Department of Industrial Relations, Ergonomic Guidelines for Manual Material Handling, Cal/OSHA Consultation

Service, Publication No. 2007-131.

[6]. Alessio Ishizaka and Ashraf Labib, “Analytic Hierarchy Process and

Expert Choice: Benefits and Limitations”, ORInsight, Vol. 22(4), p.

201–220, 2009”

[7]. Alessio Ishizaka, Markus Lusti, “How to derive priorities in AHP: a comparative study”,

[8]. Alessio Ishizaka and Ashraf Labib, “Review of the main developments

in the Analytic Hierarchy Process”, Expert Systems with Applications, Vol. 38(11), 14336-14345, 2011.

[9]. Geoff Coyle, “the analytic hierarchy process (ahp)”, Practical Strategy.

Open Access Material. AHP, Pearson Education Limited 2004. [10]. McCaffrey, James (June 2005). "Test Run: The Analytic Hierarchy

Process". MSDN Magazine. Retrieved on 2007-08-21.

[11]. De Steiguer, J.E. (October 2003), The Analytic Hierarchy Process as a Means for Integrated Watershed Management, in Renard, Kenneth G.,

“First Interagency Conference on Research on the Watersheds”,

Benson, Arizona: U.S. Department of Agriculture, Agricultural Research Service, at 736–74.

APPENDIX A

Focus ES P&P Local

priorities

Ergonomics and Safety

performance(ES) 1 0.33 0.250

Production and

performance (P&P) 3 1 0.750



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Ergonomics and Safety

performance AB CE WM SA

Local

prioriti

es

Anthropometry and

biomechanics (AB) 1 2 3 0.33 0.253

Cognitive Ergonomics (CE) 0.5 1 0.5 0.33 0.114

Work Management (WM) 0.33 2 1 0.33 0.145

Safety (SA) 3 3 3 1 0.0485

CR=0.079

Production


Local

priorities

Productivity (PR) 1 4 0.25 3 0.22

Adaptability (AD) 0.2 1 0.13 2 0.079

Capability (CA) 4 8 1 6 0.628

Flexibility (FL) 0.33 0.5 0.17 1 0.069

CR=0.0559

Anthropometry and

biomechanics LC PP PO VR

Local

priorities

Lifting and carrying (LC) 1 2 0.25 5 0.242

Pushing and pulling (PP) 0.5 1 0.25 2 0.136

Postures (PO) 4 4 1 4 0.545

Visual requirement(VR) 0.2 0.5 0.25 1 0.07

CR= 0.08541

Cognitive Ergonomics EA EU Local

priorities

Easy to understand (EA) 1 3 0.750

Easy to Use (EU) 0.33 1 0.250

Work Management CT WE TP Local

priorities

Competence and Training (CT) 1 8 2 0.594

Work Experience (WE) 0.13 1 0.17 0.065

Training Procedures (TP) 0.5 6 1 0.34

CR=0.0157

Safety MH WP Local

priorities

Mechanical Hazards (MH ) 1 4 0.8

Work clothing and PPE (WP) 0.25 1 0.2

Productivity PC IC Local

priorities

Production Capacity (PC) 1 0.17 0.143

Investment Cost(IC) 6 1 0.857

Adaptability EL GE Local

priorities

Elasticity (EL) 1 1 0.5

Generality (GE) 1 1 0.5

Capability EF EE CS Local

priorities

Efficiency (EF) 1 0.2 3 0.188

Effectiveness (EE) 5 1 7 0.731

Customer Satisfaction (CS) 0.33 0.14 1 0.081

CR=0.05594

Flexibility ES EU Local

priorities

Required Space (RS) 1 0.25 0.2

Constraints on Layout (CL) 4 1 0.8

ALTERNATIVE EVALUATION MATRICES

Lifting and carrying Forklift Manual Local

priorities

Forklift 1 7 0.875

Manual 0.143 1 0.125

Pushing and pulling

Forklift

Manual

Local

priorities Forklift

1

5

0.833

Manual

0.2

1

0.167

Posture Forklift Manual Local

priorities

Forklift 1 3 0.750

Manual 0.33 1 0.250



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Visual requirement Forklift Manual Local

priorities

Forklift 1 0.2 0.167

Manual 5 1 0.833

Easy to understand Forklift Manual Local

priorities

Forklift 1 3 0.750

Manual 0.33 1 0.250

Easy to use Forklift Manual Local

priorities

Forklift 1 7 0.875

Manual 0.143 1 0.125

Competence and training Forklift Manual Local

priorities

Forklift 1 3 0.750

Manual 0.33 1 0.250

Work procedure Forklift Manual Local

priorities

Forklift 1 5 0.833

Manual 0.2 1 0.167

Training procedure Forklift Manual Local

priorities

Forklift 1 3 0.750

Manual 0.33 1 0.250

Mechanical hazard

Forklift Manual

Local

priorities


Manual 5 1 0.833

Work clothing and PPE Forklift Manual Local

priorities

Forklift 1 1 0.5

Manual 1 1 0.5

Production capacity Forklift Manual Local

priorities

Forklift 1 9 0.9

Manual 0.11 1 0.1

Investment cost Forklift Manual Local

priorities

Forklift 1 0.143 0.125

Manual 7 1 0.875

Elasticity Forklift Manual Local

priorities

Forklift 1 3 0.750

Manual 0.33 1 0.250

Generality Forklift Manual Local

priorities

Forklift 1 4 0.8

Manual 0.25 1 0.2

Efficiency Forklift Manual Local

priorities

Forklift 1 5 0.833

Manual 0.2 1 0.167

Effectiveness Forklift Manual Local

priorities

Forklift 1 7 0.875

Manual 0.143 1 0.125

Customer satisfaction Forklift Manual Local

priorities

Forklift 1 1 0.5

Manual 1 1 0.5

Required space Forklift Manual Local

priorities


Manual 5 1 0.833

Constraints on the

layout

Forklift Manual Local

priorities


Manual 5 1 0.833



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Application of AHP in Development of Multi-Criteria Ergonomic Approach for Choosing the Optimal...

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