Abstract. Analytic Hierarchy Process (AHP) is one of the techniques commonly used for prioritizing different alternatives, by using complex criteria. In real applications, conventional AHP assumes the expert judgment as it is exact and use crisp number leading to inconsideration of the uncertainty that came from linguistic variable. Fuzzy logic deals with situations which are vague or unwell defined and gives a quantify value. In this study a comparison is made between traditional AHP and fuzzy AHP by taking a case of selecting an effective oil refinery. The selection is conducted using system effectiveness as a criterion. The two approaches have been compared on the same hierarchy structure and criteria set and the result show that in both case dual drum scheme (DDS) has the highest priority but different value that is 0.51and 0.36 for AHP and FAHP respectively which shows that if the expert opinion is certain AHP should be used if not FAHP should be preferred

No abstract

No abstract

This paper presents a mathematical model to estimate the life cycle cost (LCC) of heat exchanger and pump. Maintenance cost, down time cost and acquisition costs are calculated. The main uncertainty in calculating these costs are prediction of number of failure and cumulative down time. Number of failure is determined using failure and repair time density function. According to the characteristic that the cumulative failure probability observed, a Weibull distribution model is used. The scale and shape parameters of the Weibull are extracted from the published data. The results of the study show that 71.3% loss in the reliability of heat exchanger and 34.2% reliability loss in pump could lead to 66.2 % increment of the total cost. The reliability of the system decreases because of number of failures will increase each year, and this failure leads to unavailability of the system.Therefore in order to achieve higher system effectiveness and reduce the total LCC, the reliability of the systems need to be increased through proper maintenance policies and strategies. The results of the study could assist the managers to make decision with high degree of accuracy.

In recent years, there has been a tremendous increase in environmental awareness, due to concerns about sustainability. Designing an efficient supply chain network that fulfills the expectation of both business owners and customers and, at the same time, pays attention to environmental protection is becoming a trend in the commercial world. This study proposes a theoretical model incorporating vehicle routing problems (VRPs) into the typical CLSC (closed-loop supply chain) network architecture. This combination assists all operators to act more efficiently in terms of environmental protection and profitability. A mixed-integer-linear-programming model for CLSC network design with fuzzy and random uncertain data is developed to achieve the goals. The parameters of the CLSC network are also programmed using hybrid fuzzy-stochastic mathematical programming. The model is for a single product and a single timeframe. Several numerical examples are provided to demonstrate the validity of the proposed mixed-integer-linear-programming (MILP) model. This study also investigated probabilistic possibilities for recourse variables with a trapezoidal fuzzy number using a problem size of four cases. The result indicates that the model performed well in the numerical test, suggesting it can help the operation to be more profitable if this model is implemented in their daily routines.