Decision support system for risk management a case study

It takes into consideration the design activities and risk activities to generate a design project planning. During the project design, if different strategies can be used only for solving design problems, different others can help deal with the project risks. All of them lead to different possible scenarios. We present a decision tree to show the decisions steps and possible project scenarios.


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A generic decision support system is proposed. A case study of a satellite design project is developed to demonstrate the effectiveness of the proposition.

Keywords : Decision trees Product design Design problem solving Design projects Generic decisions Project managers Satellite designs Strategic decisions Systems engineering Design Problem solving Alternative solutions Complex products Artificial intelligence Product development Project management Risk management Decision support systems. Document type : Conference papers. Domain : Engineering Sciences [physics]. Files produced by the author s. Identifiers HAL Id : hal, version 1. Due to the structure of the fuzzy sets, each x i can only have 1 or 2 membership degrees greater than cero and their sum is always equal to 1.

In all the cases, the standard product-sum gravity method is used for the defuzzyfication process [ 51 ].

Decision support system

The structure of the production fuzzy rules is standard. According to this rule, and will be displaced to the right. The fuzzy inference engine firstly calculates in a range defined by the experts. In this range, the prototype takes into consideration 5 fuzzy sets with the same semantic labels: very little, little, standard, much and very much.

Once the new value is calculated defuzzyfication process the new right limit for the variable value interpretation is calculated as follows:. In conclusion, the interpretation of each variable in R depends on D and O provision. In the latter, which is more realistic, this does not occur [ 52 ]. RTE can also be calculated from an input or output point of view input-oriented or output-oriented techniques [ 53 ]. In the first approach, the model tries to minimize the input consumption when maintaining the output production constant and is used when the situation is focused on input management.

In the second, it tries to maximize the output production when maintaining the input consumption constant and is used for output management purposes. In order to assess the RTE of each scenario, both the fuzzy inference engine to interpret the variable values according to the B-MHCCM and the operational model to calculate RTE scores are embedded in the Monte-Carlo simulation engine to integrate the inner uncertainty of the environment.

The process is iterative and RTE scores are saved in a solution pool. The proposed pseudocode is:. This pseudocode is published in: dx. For each SHA and scenario 15 , simulations were carried out.

Decision support system

In all the assessed cases, the calculated RTE is a probabilistic distribution Fig 2 that can be analysed by both a frequency analysis and statistical estimators:. The first interval at the left hand side is [0, 0. After an intervention or policy, the initial RTE statistical distribution can vary, significantly or not, depending on the resulting impact. The resulting RTE statistical distributions were additionally analysed using the stability and entropy indicators. A system can be considered stable when data changes that represent structural changes do not significantly modify RTE scores the efficiency of the system is not sensitive to structural changes.

In contrast, the system is unstable when small data changes vary the RTE scores dramatically the efficiency of the system is highly dependent on small structural changes. DMU final stability stab has two components. The interval stability of the DMU i intstab i is calculated in the following way 3 : 3 where nint i is the number of RTE intervals in the frequency analysis that have probability greater than zero and inttot is the total number of intervals defined in the frequency analysis.

The density stability of the DMU i denstab i is calculated in the following way 4 : 4 where acprob i is the first accumulated probability strictly greater than a predefined probability prob , nintprob i is the number of intervals needed to reach acprob i , minln is the minimum feasible value for the logarithm, and maxln is the maximum feasible value for the logarithm.

The final stability of the DMU i stab i results of a weighted sum 5 of interval stability and density stability is as follows: 5 where w int is the weight selected for the interval stability and w den is the corresponding weight for the density stability. Decision makers should be awarded. Entire System pre-management interventions.

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Fifteen Scenarios pre-management interventions. The oscillates within [0. The S4 shows the greatest probability 0. The maximum is reached by S11 , which combines residential and day care health related plus outpatient care. In contrast, S6 outpatient care has the lowest. Concerning the probability of having an RTE higher than 0. According to the input management, the interval stability intstab is very poor small changes in data values can change RTE scores dramatically.

Its density stability denstab is poor small changes in data values can change RTE scores a lot. The output management results show that the interval stability intstab is very poor, while the density stability denstab is intermediate small changes in data values do not change RTE too much. According to the input management, the entropy of the global system is very high, 3.

In the output management analysis, it is also high, 3.

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From an output management perspective, it varies within [2. Impact on the entire System post-management interventions. The three management interventions cause a small but positive RTE increase in the service performance oriented to the input management. However, the situation from an output management perspective remains constant.

Impact on the fifteen scenarios post-management interventions. The first one, placement capacity, shows the highest one 5. In S10 workforce capacity total, remains constant, while in S6 , S8 , S9 and S12 , it decreases, with S12 placement capacity for residential care in the hospital and community plus day and outpatient care having the worst one. Looking at the probability of having an RTE score higher than 0. S14 shows the highest percentage 3.

S9 shows the highest negative impact The probability of having an RTE score higher than 0. In the remaining scenarios, it increases, and S14 shows the highest percentage 0. In the input orientation and after the proposed interventions, the interval stability intstab remains constant, but the density stability denstab and stab have a slight increase 0. From an output management perspective, intstab , denstab and final stability stab remain constant Table 5.


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  • The input-oriented results show that the entropy decreases However, from an output management view, the entropy increases a bit 0. The results oriented to input management show that S11 places for hospital and community care and availability of outpatient care has the highest stab increase However, output management analysis evidences that S14 placement and workforce capacity for residential, day and outpatient care has the highest stab increase 0.

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    The input-oriented results show that the greatest negative variation it increases of the entropy corresponds to S8 with 1. However, in S7 placement capacity, it decreases positive impact by Form an output orientation perspective, S8 shows the highest increase 1. EDeS-MH allows decision makers to assess the RTE of the MH system for analysing the impact of potential management interventions and policies prior to their becoming real. This DSS is able to guide MH care managers and planners in designing evidenced-informed interventions as well as policies, reducing the risk associated with decision-making.

    From a hybrid methodological approach, the Monte-Carlo simulation engine allows for the incorporation of the uncertainty of the real system into the DSS [ 22 ]. The utilization of the DESDE-LTC codification system [ 48 ] allows for the standardization, comparison and evaluation of MH systems, considering their main types of care provided and avoiding the terminology variability [ 44 ]. Finally, the fuzzy inference engine prototype integrates expert knowledge in the operational model [ 22 ].

    In the initial situation of the entire Bizkaia MH system, the RTE indicators show that there is a relevant opportunity for improving the system performance from both input resources and output outcomes management points of view.