Neutrosophic fuzzy goal programming approach in selective maintenance allocation of system reliability

Selective maintenance problem plays an essential role in reliability optimization decision-making problems. Systems are a configuration of several components, and there are situations the system needs small intervals or break for maintenance actions, during the intervals expert carried out the maintenance actions to replace or repair the deteriorated components of the systems. Because of the uncertainty associated with the component’s operational time, failure, and next mission duration create a new challenge in determining optimal components allocation and evaluating future missions successfully. In this paper, a multi-objective selective maintenance allocation problem is formulated with fuzzy parameters under neutrosophic environment. A new defuzzification technique is introduced based on beta distribution to convert fuzzy parameters into crisp values. The neutrosophic goal programming technique is used to determine the compromise allocation of replaceable and repairable components based on the system reliability optimization. A numerical illustration is used to validate the model and ascertain its effectiveness. The result is compared with two other approaches and found to be better. The method is flexible and straightforward and can be solved using any available commercial packages. The extension of the concept can be useful to other complex system reliability optimization.

Methods of Allocation of Task Teams to the Planned Works

Small construction objects are often built by standard task teams. The problem is, how to allocate these teams to individual works? To solve the problem of allocation three methods have been developed. The first method allows to designate optimal allocation of teams to the individual works in deterministic conditions of implementation. As a criterion of the optimal allocation can be applied: “the minimization of time” or “the minimization of costs” of works execution. The second method has been developed analogously for both criteria but for stochastic conditions and for the stochastic data. The third method allows to appoint a compromise allocation of teams. In this case, the criteria “the minimization of time” and “the minimization of costs” are considered simultaneously. The method can be applied in deterministic or stochastic conditions of works implementation. The solutions of the allocation problems which have been described allow to designate the optimal allocation of task teams and to determine the schedule and cost of works execution.

Minimum Cost Multiobjective Programming Model for Target Efficiency in Sample Selection

In this study, we developed a model which elaborates relationship among efficiency of an estimator and survey cost. This model is based on a multiobjective optimization programming structure. Survey cost and efficiency of related estimator(s) lie in different directions, i.e., if one increases, the other decreases. The model presented in this study computes cost for a desired level of efficiency on various characteristics (goals). The calibrated model minimizes the cost for the compromise optimal sample selection from different strata when characteristic j is subject to achieve at least 1−αj level of efficiency of its estimator. In the first step, the proposed model minimizes the variance for a fixed cost, and it then finds the rise in cost for an αj percent rise in efficiency of any characteristic j. The resultant model is a multiobjective compromise allocation goal programming model.

Interactive Fuzzy Goal Programming approach in multi-response stratified sample surveys

In this paper, we applied an Interactive Fuzzy Goal Programming (IFGP) approach with linear, exponential and hyperbolic membership functions, which focuses on maximizing the minimum membership values to determine the preferred compromise solution for the multi-response stratified surveys problem, formulated as a Multi- Objective Non Linear Programming Problem (MONLPP), and by linearizing the nonlinear objective functions at their individual optimum solution, the problem is approximated to an Integer Linear Programming Problem (ILPP). A numerical example based on real data is given, and comparison with some existing allocations viz. Cochran?s compromise allocation, Chatterjee?s compromise allocation and Khowaja?s compromise allocation is made to demonstrate the utility of the approach.