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

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.

KeywordsAHP, MCDM, Ergonomic, Material Handling, Software development.

1. 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].

2. 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).

   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].

   Satisfaction of company objectives

   Flexibility

   Adaptability

   Productivity

   Safety

   Work Management

   Cognitive Ergonomics

   Anthropometry and biomechanics

   Production performance

   Ergonomics and safety performance

   Capability

   -Lifting and Carrying

   -Pushing and Pulling

   -Posture

   -Visual

   -Easy to Understand

   -Easy to use

   -Competence and Training

   -Work experience

   -Training procedure

   -Generality

   – Elasticity

   -Efficiency and Effectiveness

   -Customer satisfaction

   -Lighting and carrying

   -Constraints on the layout

   -Production capacity

   -Investment cost

   -Mechanical Hazards

   -Work clothing and PPE

   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 makers 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, suatting, 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.

   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, workers size, workers 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.

3. METHODOLOGY

   1. 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

   2. 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].

         1. Sum of the elements of each column j :

         2. Dividing each value by its column sum:

         3. 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	..

         The method mean of row derives the priorities as follow

         1. Adding the elements of the columns: (5.58; 13.5; 1.542; 12)

         2. Normalizing the columns

            Production performance	PR	AD	CA	FL	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	..

         3. Calculating the mean of the rows: (0.22; 0.0917; 0.6145;0.07185)

         Ergonomics and Safety Performance	AB	CE	WE	WM	SA	nth root of product of values	Eigen vector
         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

         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

         Prouction performance	PR	AD	CA	FL	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.

         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

         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 =

         Where: is the largest eigenvalue of the matrix A and the corresponding eigenvector contains only positive entries. When the vector 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; max = 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 max 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 and the AHP theory says that A = 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 . 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.

   3. 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].

   4. 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].

4. 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

   (0.0689)

   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 Saatys 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

   Ergonomics and Safety performance (0.250)	Anthropometry and biomechanics (0.253)	Lifting and carrying 0.242
   		Pushing and pulling 0.136
   		Posture 0.545
   		Visual requirement 0.07
   	Cognitive ergonomi cs (0.114)	Easy to understand 0.75
   		Easy to use 0.25
   	Work management (0.145)	Competence and Training 0.594
   		Working procedure 0.065
   		Training procedure 0.34
   	Safety (0.0485)	Mechanical Hazard 0.8
   		Work clothing and PPE 0.2
   Production and Performance (0.750)	Productivity (0.22)	Production capacity 0.143
   		Investment cost 0.857
   	Adapta bility (0.079)	Elasticity 0.5
   		Generality 0.5
   	Capability (0.628)	Efficiency 0.108
   		Effectiveness 0.344
   		Customer satisfaction 0.546
   	Flexibility	Required space 0.2
   		Constraints on the layout 0.8

   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

5. 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,

   1. 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.

6. 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 saatys 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

      Noise exposure Space demands

      Enter the relative importance of Thermal environment over Lighting environment: 4

      Enter the relative importance of Thermal environment over Noise exposure: 6

      Enter the relative importance of Thermal 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

      Consistency ratio: 0.07759

   3. Software Output:

      Focus criteria: Work environment

      Local priorities/ Eigen		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. Interfac 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.

   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

7. 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.

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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

Flexibility	ES	EU	Local priorities
Required Space (RS)	1	0.25	0.2
Constraints on Layout (CL)	4	1	0.8

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

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

Production performance	PR	AD	CA	FL	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

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

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

Lifting and carrying	Forklift	Manual	Local priorities
Forklift	1	7	0.875
Manual	0.143	1	0.125

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

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

ALTERNATIVE EVALUATION MATRICES

Work clothing and PPE	Forklift	Manual	Local priorities
Forklift	1	1	0.5
Manual	1	1	0.5

Constraints on the layout	Forklift	Manual	Local priorities
Forklift	1	0.2	0.167
Manual	5	1	0.833

Mechanical hazard	Forklift	Manual	Local priorities
Forklift	1	0.2	0.167
Manual	5	1	0.833

Required space	Forklift	Manual	Local priorities
Forklift	1	0.2	0.167
Manual	5	1	0.833

Training procedure	Forklift	Manual	Local priorities
Forklift	1	3	0.750
Manual	0.33	1	0.250

Customer satisfaction	Forklift	Manual	Local priorities
Forklift	1	1	0.5
Manual	1	1	0.5

Work procedure	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

Competence and training	Forklift	Manual	Local priorities
Forklift	1	3	0.750
Manual	0.33	1	0.250

Efficiency	Forklift	Manual	Local priorities
Forklift	1	5	0.833
Manual	0.2	1	0.167

Easy to use	Forklift	Manual	Local priorities
Forklift	1	7	0.875
Manual	0.143	1	0.125

Generality	Forklift	Manual	Local priorities
Forklift	1	4	0.8
Manual	0.25	1	0.2

Easy to understand	Forklift	Manual	Local priorities
Forklift	1	3	0.750
Manual	0.33	1	0.250

Visual requirement	Forklift	Manual	Local priorities
Forklift	1	0.2	0.167
Manual	5	1	0.833

Elasticity	Forklift	Manual	Local priorities
Forklift	1	3	0.750
Manual	0.33	1	0.250

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