In this paper, the effectiveness In this paper, the effectiveness In this paper, the effectiveness In this paper, the effectiveness of the decision maker's risk attitude index for fuzzy cri of the decision maker's risk attitude index for fuzzy cri of the decision maker's risk attitude index for fuzzy cri of the decision maker's risk attitude index for fuzzy cri considered. considered. considered. considered. The fuzzy CPM algorithm waThe fuzzy CPM algorithm wa The fuzzy CPM algorithm wa The fuzzy CPM algorithm wa activities times as well activities times as well activities times as well activities times as well as the total float of project activities as the total float of project activities as the total float of project activities as the total float of project activities problem problem problem problem, the decision maker's risk attitude index ranking method produces unrealistic results when the decision , the decision maker's risk attitude index ranking method produces unrealistic results when the decision , the decision maker's risk attitude index ranking method produces unrealistic results when the decision , the decision maker's risk attitude index ranking method produces unrealistic results when the decision maker maker maker maker' ' ' 's attitude towards risk was neutral. s attitude towards risk was neutral. s attitude towards risk was neutral. s attitude towards risk was neutral. pr pr pr produced realistic results. The study shows that the utility of the ranking technique may be limited by the risk index oduced realistic results. The study shows that the utility of the ranking technique may be limited by the risk index oduced realistic results. The study shows that the utility of the ranking technique may be limited by the risk index oduced realistic results. The study shows that the utility of the ranking technique may be limited by the risk index associated with the problem. Therefore users of the technique for ranking fuzzy numbers have to proceed with associated with the problem. Therefore users of the technique for ranking fuzzy numbers have to proceed with associated with the problem. Therefore users of the technique for ranking fuzzy numbers have to proceed with associated with the problem. Therefore users of the technique for ranking fuzzy numbers have to proceed with caution. caution. caution. caution.

The study evaluated the queuing system in Blue Meadows restaurant with a view to determining its operating characteristics and to improve customers' satisfaction during waiting time using the lens of queuing theory. Data was obtained from a fast food restaurant in the University of Benin. The data collected was tested to show if it follows a Poisson and exponential distribution of arrival and service rate using chi square goodness of fit. A 95% confidence interval level was used to show the range of customers that come into the system within a time frame of one hour and the range of customers served within that time frame. Using the M/M/s model, the arrival rate, service rate, utilization rate, waiting time in the queue and the probability of customers likely balking from the restaurant was 442 INDEPENDENT JOURNAL OF MANAGEMENT & PRODUCTION (IJM&P)http://www. ijmp.jor.br v. 8, n. 2, April -June 2017 ISSN: 2236 The number of servers present in the system was two. The average number of customers in the system in an hour window was 40 customers with a utilization rate of 0.909. The paper concludes with a discussion on the benefits of performing queuing analysis to a restaurant.

Sand casting is reputed for the manufacture of engine components as a result of its ease of operation.An assemblage of process parameters at optimal conditions leads to enhanced mechanical properties of automobile engine components. The Response Surface Methodology Design of experiment created an experimental layout for the sand casting process parameters and the various levels as applied in the production of engine pistons at the foundry. The Box-Behnken Design provided a matrix of 27 experiments to be conducted. Multiple linear Regression technique was employed to develop a mathematical model for the hardness of the aluminium alloy. The developed model was inputted into the evolutionary Genetic Algorithm tool box as an objective model. The optimal levels determined from the Genetic algorithm were used to carry out actual experiment in the foundry and the result was similar to the predicted hardness value of the developed model. Statistical ANOVA test conducted showed that the mathematical model was adequate with a R 2 value of 81.02% and R 2 (adjusted) value of 60.07%. The developed model has a p-value of 0.016 which indicates that the model was significant. The optimal values obtained for pouring temperature, vibration frequency, vibration time and runner size are 700 o C, 31.52Hz, 59.998sec and 469.69mm 2 respectively.

This study investigates the effect of varying test conditions including ambient temperature (19, 22, and 25 C), mass charges of R600a refrigerant (40, 50, 60, and 70 g), and concentrations of TiO 2 nanolubricant (0, 0.2 and 0.4 g/L) on the performance of a slightly modified 100g R134a domestic refrigeration system. The investigated parameters include evaporator air temperature, energy consumption, coefficient of performance, and second law efficiency of the system. The results showed that the performance of the refrigeration system at 0.2 and 0.4 g/L concentrations of TiO 2 nanolubricant, improved at optimum ambient temperature and R600a mass charge conditions. At optimum conditions, the evaporator air temperature and energy consumption reduced within the range 5.26 to 26.32 %, and 0.13 to 14.09 % respectively, while the coefficient of performance and second law efficiency increased within the range 0.05 to 16.32 %, and 2.8 to 16 %, respectively. However, at other conditions (nonoptimum), the energy consumption and evaporator air temperature were higher and within the range 0.28 to 8.26 %, and 5 to 40 % respectively, while the coefficient of performance and second law efficiency reduced within the range 2.99 to 10.94 %, and 0.55 to 13.43 % respectively. In conclusion, we observed variations in the performance of the refrigerator with varying test conditions.

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