Safety and Health Group Decision-Making with Multiple Criteria

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Safety and Health Group Decision-Making with Multiple Criteria

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Home Page > Law > Health and Safety > Safety and Health Group Decision-Making with Multiple Criteria

Safety and Health Group Decision-Making with Multiple Criteria

Posted: May 31, 2011 |Comments: 0
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Author: Samuel A. Oyewole, Ph.D.


1.0.      Introduction

Group decision-making with multiple criteria in safety and health could be described as a complex and complicated practice which is often used in situations involving multiple stakeholders or decision-makers. Each decision-maker possesses different skills, experiences and backgrounds and must come together to make a decision based on multiple criteria (Saaty, 1989). Members of the decision-making team in the field of environmental health and safety could include an industrial hygienist, environmental engineer, safety engineer, human factors/industrial engineer, as well as a mechanical engineer, etc. These individuals would often have different views which must all be aggregated to arrive at a tangible decision. Group decision-making with multiple criteria could be complicated further in situations where the appropriate decision-making tools and techniques are neglected (Baucells and Sarin, 2003).

For most problems involving group decision-making with multiple criteria, an acceptable method is the group coalition decision-making technique (GCDMT). This is a simple method which involves breaking down the group into small coalitions or sub-groups. In this case, each coalition makes the various sub-decisions within the group by incorporating relevant tools. A representative is then chosen from each coalition to meet and make the final decision. The utilities of each dis-joint coalition could then be aggregated to obtain the final decision. In a very large group, multiple representatives could be chosen for the general decision-making process. Efforts should be made not to include individuals having similar backgrounds or skills within the same sub-group. This is necessary so as to prevent the emergence of one-way or lopsided decisions.

The GCDMT is an effective method of overcoming challenges which may arise during the multi-criteria design decision-making process. These may include aggregation of individual decisions; changes in aggregation techniques due to changes in interdependence among the decision criteria; the integration and accommodation of different individual views, skills and attributes; as well as the integration of influences due to social dynamics-status, qualifications or portfolios of the different decision-makers. GCDMT could be argued as an effective methodology when taking the issues and challenges identified into consideration.

2.0.      Group Decision-Making Process

In order to address these issues, it is important that the following steps be followed.

Stating the design objective(s) or the goal(s) to be achieved: at this stage, the main focus of the decision-making effort should be stated. The objective(s) should be clear enough and must be easily understood by every decision–making team member.
Selection of decision-makers based on the problem: at this stage, the various decision-makers are selected from different backgrounds and expertise. Different methods and techniques for selecting decision-makers are proposed by Saaty (1980).
Break-down decision-makers into sub-groups: the establishment of coalition groups allows decision-makers within the sub-group to establish closer relationship. It also enables decision-makers to have a clearer picture of the ideas or attributes proposed within the sub-groups. At this stage, inter-personal trust could be easily established and a compromise or a decision agreement would be achievable. When a sub-group decision is made, the coalition could select representatives to present their decision to the general decision-makers.
Combination of coalitions: the combination of the sub-groups or their representatives to form the overall decision-making team is done after the sub-groups have made their respective unilateral decisions. It should be noted that the decisions made by each sub-group is mutually exclusive from one another.
Group decision-making: the combined sub-groups or their representatives utilize different tools and techniques to weigh and aggregate the individual decisions of each coalition, and a final decision is made based on this. At this stage, any other outstanding issues from the sub-groups or individuals are considered for possible integrated into the decision-making process. The utility of each individual coalition is performed during the group decision-making process.
Validation of final decision: the final decision is validated against the problem statement in order to determine whether the alternatives selected and the decision chosen meets the stipulated criteria as well as the requirements and objectives of the decision problem. Decision validation is necessary to detect any misapplication of tools used, uncertainties or biases in the decision-making process. Dy     er and Foreman (1992) recommended that tentative decisions be checked against the intuition of the decision-makers in order to eliminate non-feasible or non-realistic decisions. In some complex situations, the validation of final decision may lead to the generation of new requirements or additional objectives from design alternatives which may lead to the modification of the decision. Some decision modifications may include the addition of new criteria to the decision-making process.

3.0.      Sub-Group and General Coalition Decision-Making Process

In the sub-group, different decision-making tools such as the Analytic Hierarchy Process (AHP) proposed by Saaty (1980) could be utilized for the group decision support.In Saaty’s paper, the various methods of assembling the group as well as the processes involved in trying to reach a decision agreement are proposed. In some situations, the selected decision-makers may possess different hierarchy of power which may lead to some bias in the decision-making process.  Dyer and Forman (1992) proposed a methodology for group decision support using the AHP. The proposed method allows decision-making to be achieved based on choice and prioritization. AHP is a decision-making tool which provides an accurate and efficient methodology for finding the relative importance of each of the needs in the hierarchy. Using this methodology, the knowledge of the decision-maker could be identified by making use of their judgments in the hierarchy process (Saaty, 1986).

AHP could be used in situations where consensus agreement is needed or during voting or compromises. In some instances, decisions could be made based on the incorporation of the geometric means of individual judgments into AHP. Concession is very effective in situations where the decision teams have the same objectives or similar skills and background.  In situations when consensus may be difficult to achieve in multiple groups with multiple criteria, voting or compromise may be adopted. Moulin (1998) describes voting as a simple process where the alternative with the highest number of vote is ranked the best. A major problem with voting is the possibility of voting for an alternative or judgment which could lead to inferior design.

Chowlka and Raith (2000) developed two different methods of making group decisions using AHP. Contrary to the general concepts where individual preferences are combined, the AHP is used as a supporting tool to individual preferences. In order to effectively implement these methodologies, the geometric mean method (GMM) and the weighted arithmetic mean method (WAMM) are incorporated into their research. Saaty (1989) proposed the use of GMM to determine the group priority in situations where the individual judgments are to be obtained. A limitation of both methodologies is their inability to provide efficient solutions to very complex decision-making problems based on issue-wise compromise.

The sub-group may face some social dynamic challenges such as the years of experience of the decision-makers, past records, skills and qualifications of the decision-makers. For example, a situation could arise where a member of the team posses’ more voting power than the other member. Burdett (2000) described this as a “necessary evil” due to some inherent factors which may prevent the full participation of all group members. According to Burdett, in a large decision-making group, 20% of the decision-makers dominate the process 80% of the time. This limits the ability of others to fully participate in the decision-making process. In some instances, high ranking personnel might be resentful and unwilling to accept the ideas provided by subordinates. Sensitivity analysis is recommended in this situation in order to investigate the presence of varying importance among decision-makers. Other factors responsible for this might involve the inability of some group members to fully disclose their ideas or are pressured to make a concession leading to lopsidedness in the decision-making process.

A lopsided decision could reduce the efficiency or effectiveness of design operations. In order to avoid these limitations, Saari (1999) proposed the allocation of weights to attributes in order to achieve the best feasible decision when given a set of choices or alternatives. The methods proposed include the position method, dropping or adding alternatives, pair wise method, cancellation method, as well as Borda count technique. The position method utilizes weights by allocating them to each decision criterion. Based on the weight allocation, alternatives are generated and the alternatives are ranked based in reference to the summation of the allocated weights. Statistical limitations may arise as a result of this approach especially in situations where larger weights were allocated to more favored alternatives. The use of position method may become more complicated as more alternatives are added or considered.

The dropping or adding alternatives technique involves the dropping of non-feasible alternatives. A seemingly feasible alternative could also be added in order to achieve a successful decision outcome. Although an alternative could be bottom-ranked, such alternative may actually turn out to be the optimal alternative despite not being the top ranked alternative. It may however be difficult to achieve consistency with this type of method, especially in situations where alternatives are dropped in a nested set structure. The pairwise technique is a decision-making rule which is based on the voter’s preferences while the cancellation rules are based on rule cancellation or elimination. Moulin (1988) suggests that the pairwise technique consumes time and often applies ranks which are considered rigid and does not provide adequate information concerning the individual preferences of the decision-makers.

According to Saari (1985), the Borda count method was first implemented around 1782 – 1784 in a public voting system. The Borda count is a very quick decision-making tool since it can be completed in a single iteration process, without eliminating any information. A major limitation of this method is its inability to accommodate for reversal of the ranks allocated if necessary Borda Count provides a unique type of ranking determination which gives the maximum level of consistency when compared to other decision techniques such as the position method, dropping or adding alternatives, pairwise technique as well as the cancellation rules (Borda, 1782). When compared to the other types of decision-making rules, the Borda Count technique is also the only rule which recognizes symmetrical arrangements and other rules which tend to introduce bias and gives conflicting or distorting conclusions. Scott and Zivkovic (2003) argue that it may be difficult to reverse ranks allocated when a particular attribute is eliminated.

Another major challenge that could arise in the early stage of a design decision-making process is the issue of prioritization of quality requirements. Hsieh et al (2005) proposed the method of utilizing an updating urn-scheme for prioritizing quality requirements. In situations where targeted customers are unable to clearly present their preferences, as a result of this, the bulk of decision-making such as the determination of attributes now falls on the decision team. The updating urn-scheme method proposed accounts for this pitfall by incorporating social dynamic factors into the decision-making process. Interpersonal trust and trustworthiness are incorporated into the voting process during the utilization of the urn-scheme. In some situations, a decision-making group may utilize concession to select a group leader and allocate more voting powers to the individual.

A decision-making group leader could be selected based on past experience, skills or background. In another situation, a group leader could be selected based on interpersonal trust and trustworthiness and entrusted to prioritize the quality requirements. McAllister (1995) discussed the importance of trust as inevitable in situations where decision-makers need to work together in an effective manner. Although the urn-scheme could be used to provide a structured methodology for quantifying the relative importance of the quality requirements, entrusting decision-making on an individual or group of individuals based on trustworthiness may not effectively account for some important factors such as the long-term or strategic goals or priorities of an organization. The use of GCDMT based on the division of decision-makers into sub-groups accounts for this limitation since each individual within the sub-group may present diverse ideas.

At the sub-group level, any of the existing decision-making methods could be combined in order to reach a decision. Using a similar methodology proposed by Dodgos et al. (2001), the following steps are proposed.

Analyze the defined problem: the initial design objective is evaluated at this level in order to provide a clear view of the problem to the sub-group decision-makers.
Identify and define requirements: customer requirements are acquired in form of questionnaires, interviews, or other feedback methods. These requirements are defined and converted to attributes.
Establish design goals: based on the requirements, the design is developed.
Select weighting methods: the weighting technique to be adopted is selected.
Identify alternatives: at this stage, the ideas are generated and several design decision-making techniques such as AHP, QFD, brainstorming, and mind mapping could be used to select design concepts.
Select decision-making tool: as soon as the concepts have been selected, decision-making tools such as value or utility theory could be used to make decision.
Decision-making: the alternatives are weighted and aggregated and the best design could be selected based on the computation of the overall assessment measure for each decision alternative. This is achieved by combining the weights and priority scores.

When the sub-groups are combined, final decision could be made by the use of the utility theory. The utility theory or utility function is used in situations when the decision problem involves uncertainty such as uncertain information or uncertain occurrence probability. In this case, none of the design attributes are linearly changing with regards to their perceived value in terms of contribution towards the objective function (Thurston, 2006). The utility function would be used to come up with the most comprehensive solution. In the utility theory, attributes are assumed to be are preferentially independent and the multiplicative form of the multi-attribute utility function is used to evaluate the design alternatives (Thurston, 2006b). The utility theory utilizes a scaling constant which is the weighted sum of the individual multiplicative constants. Utility function takes into consideration uncertainties that could arise in the multiple criteria with multiple group decision-making.  A major limitation of the utility theory is that the multiplicative nature is only related to the elicitation and aggregation of individual preferences.

In order to aggregate multiple criteria, a weighting system should be established. A very common technique used in the aggregation stage is the simple multi-attribute rating technique (SMART) method. The SMART method is used to allocate weights for each of the designs required. The SMART method and another technique such as consensus could be used to agree to the allocation of weights to the attributes. SMART was first developed by Edwards in 1971 for the allocation of weights to represent each of the criteria in their order of relative importance (Edwards, 1971 and 1977; Lin et al. 2008). In SMART, the least important criterion or attribute oriented design is allocated 10 points and the next least important is allocated more points to reflect increasing relative importance.

In the group decision-making involving multiple criteria, SMART provides an effective method to assign weights to the criteria presented by each coalition. Unlike other weighting methods, SMART uses value function to determine how values should be assigned to each attribute or criterion. This is achieved by transforming points allocated to each attribute to a normalized scale. SMART is a flexible decision-making tool due to its simplicity and its transparency in as a weight allocation method. A major limitation of SMART is due to its inability to encompass all the information necessary to solve very complex problems. Other types of weighting methods are described by Hwang and Yoon (1981) and Weber and Borcherding (1993).

 

References

Baucells, M., and R.K. Sarin (2003). Group Decisions with Multiple Criteria. Management Science, 49 (8), pp. 1105-1118.

Borda J.C. (1782), “Mémoir sur les Élections Au Scrutin” Histoire de Académie Royale des Sciences, Paris.

Burdett, J. (2000). ‘Changing Channels: using the Electronic Meeting System to Increase Equity in Decision-making’, Technology, Learning and Performance Journal, 18(2), pp. 3-12.

Chowlka A. and Raith R. (2000), ‘Supporting group decisions with the AHP: harmonization vs. aggregation of preferences’, in Wanka G., Decision Theory and Optimization Theory and Practice, Saker Verlag, Aachen.

Dodgson, J.,  M. Spackman, A.D. Pearman and L.D Phillips, Multi-Criteria Analysis: a Manual, Department of the Environment, Transport and the Regions, London, pp.158, 2000. ISBN: 1 85112 454 3. Also at: http://www.environment.detr.gov.uk/multicriteria/index.htm

Dyer, R.F. and Forman, E.H. (1992) .Group decision support with the Analytic Hierarchy Process., Decision Support Systems, 8, 99-124.

Edwards, W. (1971) Social utilities. Engineering Economist, Summer Symposium Series 6, 119-129.

Edwards, W. (1977) .How to use multiattribute utility measurement for social decisionmaking., IEEE Transactions on Systems, Man, and Cybernetics, SMC-7, 326-340.

Hsieh, P., Ge, P, and Meier, S. (2007). Using an updating urn-scheme for prioritising quality requirements in the early stage of collaboration design, Journal of Design Research, Volume 6, Number 4, pages 444 – 461.

Hwang, C.L. and K. Yoon (1981). Multiple Attribute Decision-making: Methods and Applications.Berlin/Heidelberg/New York: Springer – Verlag

Lin, C., Gupta, S. and Okudan, G. E. (2008). “An Improved Concept Selection Approach for Design Decision-Making,” At the Proceedings of the Industrial Engineering Research Conference, Vancouver, Canada, pp 1706 – 1711.

McAllister, D. J. 1995. Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38, 24-59.

Moulin, H., (1988). Axioms of cooperative decision-making. Econometric Society Monograph 15. Cambridge, UK; New York: Cambridge University Press

Saari, D.G. (1999). Bad Decisions: Experimental Error or Faulty Decision Procedures?” Invited tutorial presented at the 1999 ASME meeting.

Saari D.G. (1985). The Optimal Ranking Method is the Borda Count, No 638, Discussion Papers, Northwestern University, Center for Mathematical Studies in Economics and Management Science, Retrieved December 16, 2010. http://econpapers.repec.org/RePEc:nwu:cmsems:638.

Saaty, T.L. (1989). Group Decision-making and the AHP in: B. Golden, E, Wasil and P.T. Harker, Eds., The Analytic Hierarchy Process: Applications and Studies Springer-Verlag, New York.

Saaty, T.L. (1980), The Analytic Hierarchy Process, McGraw-Hill, New York, NY.

Saaty, T.L. (1986), Axiomatic Foundation of the Analytical Hierarchy Process, Management Science, vol. 32, no. 7, pages 841-855.

Thurston, D. L. (2006a). “Utility Function Fundamentals.” Decision-making in Engineering Design. Ed. Kemper E. Lewis, et al. New York, New York: ASME Press, pages 15-19.

Thurston, D. L. (2006b). “Multi-attribute Utility Analysis of Conflicting Preferences.” Decision-making in Engineering Design. Ed. Kemper E. Lewis, et al. New York, New York: ASME Press, pages 125-133.

Weber, M. and K. Borcherding (1993). Behavioral influences on weight judgments in multiattribute decision-making. European Journal of Operations Research 67: 1—12.

Zivkovic, I. and M. J. Scott. (2003). “On rank reversals in the Borda Count” In 2003 ASME Design Engineering Technical Conferences, 15th International Conference on Design Theory and Methodology, Chicago, Illinois. ASME. Paper number DETC2003/DTM-48674.



About the Author:
Dr. Samuel A. Oyewole is an Assistant Professor of Environmental Health and Safety Engineering in the Dept. of Energy and Mineral Engineering, Pennsylvania State University. Dr. Samuel Oyewole obtained his Ph.D. in Industrial and Manufacturing Engineering from the Pennsylvania State University. He has over a decade of industrial and research experience, with published papers in several professional conferences and research journals. Dr. Oyewole is a member of the American Society of Safety Engineers (ASSE), and the Human Factors Engineering Society (HFES).
 
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