4. Experimental results and data analysis

To assess the computational tractability and efficiency of the developed model, we tested the operation of our model on a set of department of operating theater in "Habib Bourguiba" hospital in Tunisia. We report the results obtained on three departments of different sizes. The comparative Table 3 shows the relevant parameters of scale for the three departments.

The table shows the number of professionals for each discipline (D1: surgery, D2: anesthesia, D3: instruments) in the second column. The third column lists the possible size of team for each department which depends on the nature of the operation.

During 3 months, the team performance of the first support for building multidisciplinary teams is identified by seven tests. Respectively, two tests in orthopedics department, three tests in urology department, and two tests in the neurology department. Table 4 shows respectively the team size for each test and size of each discipline.

Then, we apply the second support for selection team. Within our framework of aid to the choice of the best team which satisfies the preferences of decision maker and operation need. Our case base is formed by 20 operations which satisfied this type of operation.

Our objective consists on searching the best team of a new case arising to the case base. This new case is described by the same attributes that those of the other cases in base, described in Table 5.

The objective of similarity measures is to look for the nearest case which satisfies the most preferences of the new operation in the case base. Indeed, by applying Eq. (1), we calculate all local similarities between attributes (Table 6).

The relative importance weighting attributes obtained by AHP method, Wi, are listed in Table 7.


Table 3. The experimental departments.

Step 3: Retrieving phase

An AHP structure for selection teams.

Industrial Engineering

Figure 5.

Table 2.

AHP comparison scale.

The objective of the retrieving phase is to find the most similar previous cases in case base, and retrieving them for analysis, in order to select one and reuse it in the next phase. The similar case retrieval depends on the case representation and their indexing in the case base. The objective is to measure the similarity between the

Verbal judgments Numerical rating

Equal importance 1 Moderate importance of one over another 3 Strong or essential importance of one over another 5 Very strong importance 7 Absolute importance 9 Intermediate values between two adjacent judgments 2, 4, 5, 8

The question in our model is which one of the previous teams is the most similar to the new operation (case) that must be treated. In order to evaluate the similarity, the similar attribute collection S = {sT1,…, sTn} should be determined first. Let us denote the new operation (case) to be considered by T′. By T, we denote operation (case) stored in the case base. We also denote by Sim the similarity degree between

In the first step, we calculate the local similarity sTi between attribute. We

sTi <sup>¼</sup> <sup>1</sup> � Ti � <sup>T</sup>′

� � � �

!

Tmax <sup>i</sup> � Tmin i

where Ti is the ith attribute of the case in memory, T′<sup>i</sup> is the ith attribute of the

In the second step, we calculate overall similarity by using the weights associated with each attribute. We thus introduce the importance of the attributes as a new variable. It measures the importance of the ith attribute, which we express as Ti. In our model, the weights Wi were calculated by using the AHP method. A general

i

min are the maximum and minimum values between

(1)

new case (operation) and the stored cases in the case base.

the new operation and the operation stored.

define this similarity in the following way:

max and Ti

form of similarity measure function is shown in Eq. (2).

current case, and Ti

84

all the cases for the ith attribute.


#### Table 4.

Tests of proposed model.


#### Table 5.

Case base construction for the team selection problems.

The attribute weights are then employed in Eq. (2) to measure the similarity between the cases in memory and the new case. Next, we obtain the resul in Table 8.

The computational study pretends to analyze if the model improves the effectiveness of the team in operating theater and how good is its contribution. For this study, the team performances are identified by 30 tests. Respectively, 10 tests in orthopedics department, 10 tests in urology department, and 10 tests in the neurol-

Attributes T CT Co R Weight (Wi) T 0.1 0.086 0.076 0.120 0.095 CT 0.3 0.260 0.307 0.240 0.276 Co 0.2 0.130 0.153 0.159 0.160 R 0.4 0.521 0.461 0.480 0.465

Case T CT CO R 0.9473 0.8 0.75 0.5 0.0210 1 0.5 0.75 0.8947 1 1 1 0.8947 0.8 1 0.75 0.5789 0.8 0.75 0.5 0.5157 0.6 0.75 0.75 0.9473 0.8 1 1 0.4210 1 0.75 0.5 0.6315 0.4 0.5 0.5 0.5263 0.8 1 1 0.7894 0.8 1 0.75 0.7052 0.8 0.75 1 0.9789 0.6 0.75 1 0.5789 1 0.75 0.5 0.9473 0.6 0.75 0.75 0.7894 0.2 0.5 0.5 0.5368 0.6 0.5 0.5 0.6315 0.4 0.5 0.5 0.8526 1 0.25 0.75 0.9578 0.6 1 0.75

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams…

DOI: http://dx.doi.org/10.5772/intechopen.80934

Finally, to assess the efficiency of our proposed model, we used model in the three departments of Habib Bourguiba hospital and we obtained the percentage of operation success in each department (see Figure 6). It analyzes the comparison of

results before and after the integration of our model.

ogy department.

Table 6.

Table 7. Attributes weight.

87

Similarities local calculation.


An Integrated Approach for the Building and the Selection of Multidisciplinary Teams… DOI: http://dx.doi.org/10.5772/intechopen.80934

Table 6. Similarities local calculation.



The computational study pretends to analyze if the model improves the effectiveness of the team in operating theater and how good is its contribution. For this study, the team performances are identified by 30 tests. Respectively, 10 tests in orthopedics department, 10 tests in urology department, and 10 tests in the neurology department.

Finally, to assess the efficiency of our proposed model, we used model in the three departments of Habib Bourguiba hospital and we obtained the percentage of operation success in each department (see Figure 6). It analyzes the comparison of results before and after the integration of our model.

The attribute weights are then employed in Eq. (2) to measure the similarity between the cases in memory and the new case. Next, we obtain the resul in

CNew 120 5 5 3 ?

Test Team size Size of each discipline

1 5 221 2 6 323 3 4 211 4 5 212 5 6 222 6 6 321 7 7 331

Case Criteria Team

 125 4 6 1 {C2, C3, A5, I2, I3} 122 5 3 2 {C4, C1, A2, I1, I4} 130 5 5 3 {C6, C3, A6, I2, I5} 110 4 5 2 {C10, C2, A5, I12, I3} 160 4 4 5 {C4, C2, A2, I3, I2} 74 3 6 2 {C1, C7, A10, I6, I14} 115 6 5 3 {C3, C6, A3, I9, I10} 65 5 4 1 {C5, C8, A1, I8, I7} 85 2 3 1 {C2, C1, A3, I12, I3} 75 4 5 3 {C7, C5, A8, I8, I11} 100 6 5 2 {C4, C3, A2, I10, I2} 92 4 6 3 {C9, C5, A7, I9, I5} 122 3 4 3 {C6, C10, A3, I5, I6} 160 5 4 5 {C6, C2, A10, I12, I5} 125 3 6 4 {C3, C10, A2, I14, I9} 140 1 3 1 {C2, C4, A6, I3, I6} 76 3 4 1 {C5, C9, A1, I9, I15} 85 2 3 1 {C3, C6, A9, I13, I2} 134 5 2 2 {C8, C1, A3, I15, I3} 124 3 5 2 {C2, C4, A2, I4, I10}

T CT CO R

D1 D2 D3

Table 8.

86

Table 5.

Case base construction for the team selection problems.

Table 4.

Tests of proposed model.

Industrial Engineering


The proposed model was tested on the real datasets collected from the "Habib Bourguiba" Hospital in Tunisia. However, because of the nature of the information and the difficulty of obtaining the data, the number of available data points was limited. The developed model is highly representative of the reality because it uses the last experience case that satisfies the most the decision maker preferences.

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams…

The next step in our work will be the use of our approach in other areas. We are also planning to imbed this model in a general project management system that we are currently developing. The model can be improved by adding other attributes

, Anderrahman El Mhamedi<sup>1</sup>

(experience, leadership, etc.) which can be studied in the future.

DOI: http://dx.doi.org/10.5772/intechopen.80934

Author details

89

and Younes Boujelbene<sup>3</sup>

Ikram Khatrouch1,2\*, Lyes Kermad1

1 IUT Montreuil, University of Paris 8, France

\*Address all correspondence to: Ikram.khatrouch@univ-st-etienne.fr

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

2 University of Lyon, Saint Etienne, France

3 FSEGS, University of Sfax, Tunisia

provided the original work is properly cited.

Table 8. Global similarities calculation.

Figure 6. Percentage of successful operations.
