Redundancy allocation problem with multi-state component systems and reliable supplier selection

We propose a mathematical model for optimizing multiple redundancy-reliability systems known as mega-systems. The system components are multi-state and the universal generating function (UGF) has been simulated to evaluate the system availability. The components may have minor or major failure, which reduces the components performance rate. We assume the components can be sold to different vendors and a vendor selection component is included in the proposed model to accommodate this assumption. The proposed mathematical model is NP-hard and we use a parameter-tuned memetic algorithm (MA) to solve the problem. We further use a mechanism based on the response surface methodology (RSM) to calibrate the proposed MA. The performance of the proposed MA is compared with a commonly used genetic algorithm (GA)

Development of multi-objective simulated annealing based decision support system for course timetabling with consideration preferences of teachers and students

Course timetabling problem is a weekly assignment a set of course and teacher to the time and space with considering a lot of hard and soft constraints in universities. In each semester, heads of educational institutes take too much time and effort to prepare a timetable by using trial and error method or last semester's timetable, although the rapid changing needs, resources and rules of each semester causes this method are not the perfect solutions. In this study, we design and develop a novel multi objective mathematical model which taking into account the preferences of students and teachers, Due to the complexity, we have benefited the metaheuristic algorithm to solve nonlinear model. Simulated Annealing algorithm is used to solve the mathematical model in two stages. In the first stage, the system automatically generates feasible solutions that will meet all the hard constraints. Then, the solutions are improved with spotting different neighborhood's structures. This collection is in the form of computer software application which is implemented the C# language programing and SQL database. This system is tested the data gathered by Azad University data and the results compared to the manual process showed the great progress is achieved. The entire system is flexible and easy to test different scenario

State-based transition probabilities for systems subject to periodic inspection interval

In this paper, we present a general formula to calculate transition probabilities for six different types of systems based on their redundancy strategy and the status of the components. The systems are under periodic inspection policy and their components are repairable. The investigated systems include system I – active redundancy without any component to be replaced or repaired; system II – active with the component(s) to be replaced and repaired; system III – active with the component(s) to be repaired; system IV – standby without any component(s) to be replaced or repaired; system V – standby with the component(s) to be replaced and repaired; and system VI – standby with the component(s) to be repaired. In addition, all components in a system are considered non-identical. To calculate the transition probabilities for systems IV, V, and VI, we first consider a system with n non-identical components and cold standby configuration (NIC-CSC) and calculate the system state probabilities using Markov theory. Then, we present the general formula transition probabilities for systems IV, V, and VI using the results of the NIC-CSC system inspection interval. We demonstrate how to calculate the transition probabilities and matrixes for the six above-mentioned systems using the provided formulas. Moreover, we use the provided formulas to optimize the inspection interval of these systems using a modified full enumeration technique.</p

RELIABILITY OPTIMIZATION OF A SERIES-PARALLEL K-OUT-OF-N SYSTEM WITH FAILURE RATE DEPENDS ON WORKING COMPONENTS OF SYSTEM

This paper presents a mathematical model for a redundancy allocation problem (RAP) with k-out-of-n subsystems and failure rate depends on working components of system. It means that failure rate of components increases when a component fails. The subsystems may use either active or cold-standby redundancy strategies which considered as a decision variable. Thus, the proposed model and solution methods are to select the best redundancy strategy among active or cold-standby, component type, and levels of redundancy for each subsystem. The objective function is to maximize the system reliability under cost and weight constraints. Since RAP belongs to Np-hard problems, four meta-heuristic algorithms named genetic algorithm, Memetic algorithm, simulated annealing and particle swarm optimization are proposed. The results shown that the MA is better than other algorithms. Finally, in order to determine whether there is any significant difference between the results of four algorithms or not, a statistical test is applied