Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

Abstract

Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.

Economic Operation of the Regional Integrated Energy System Based on Particle Swarm Optimization

“…(6) Update p best and g best according to the fitness obtained. (7) According to the obtained p best , whether it is the optimal value of the population is determined. If the conditions are met, the optimal value is output.…”

“…The coupling element increases the flexibility of balancing the fluctuation mode of renewable energy, which is mainly realized by optimizing the combination of gas-fired generator sets, the production, and energy consumption of compressors and electric gas equipment. As the coupling gradually increases, the interaction of energy and fuel efficiency increases and the combination of energy and fuel efficiency becomes more important [ 7 ].…”

An Optimization Method of Flexible Manufacturing System Reliability Allocation Based on Two Dimension-Reduction Strategies

“…The reliability constraint of each element can be written by MTBF ∈ [MTBF L , MTBF U ], wherein MTBF L and MTBF U are the lower limit value and the upper limit value of MTBF. For ease of modeling, the reliability-level factor of one element is defined as ς ∈ [0, 1]. Then, the value of MTBF of the kth element can be represented by a value related to the reliability-level factor ς, and can be written as follows.…”

“…Reliability allocation refers to the process of assigning the specified reliability indicator of the whole system to its basic units by balancing factors such as system quality, cost, and performance, which can be used to optimize the layout or design of the system [1]. In previous allocation principles, higher levels of reliability can always be assigned to units with the following conditions [2]: those with mature technology (since high levels of reliability can be obtained with fewer costs); those with simple structures that are easy to assemble or repair; those whose failure can lead to serious consequences; and those that work in poor condition.…”

Optimal allocation of reliability improvement target based on multiple correlation failures and risk uncertainty

“…Liu [23] et.al took the warranty servicing cost as an additional objective for the optimization of the repairable system. Kumar et.al [20] and Zhang et.al [34] solved the multi-objective optimal allocation model by the gray wolf optimizer algorithm and the Particle Swarm Optimization (PSO) algorithm, respectively. Taking the risk as the losses from failures, Todinov et.al [29] extended the concept of the total cost as the sum of the risk and the resources invested in reliability improvement.…”