Simplified swarm optimization for bi-objection active reliability redundancy allocation problems

Yeh, Wei-Chang; Su, Yi-Zhu; Gao, Xiao‐Zhi; Hu, Cheng-Feng; Wang, Jing; Huang, Chia‐Ling

doi:10.1016/j.asoc.2021.107321

Cited by 27 publications

(13 citation statements)

References 38 publications

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“…SSO is one of the simplest machine-learning methods (Wang et al [54][55][56] Yeh, et al [57]) in terms of its update mechanism. It was first proposed by Yeh [27], and has been tested to be a very useful and efficient algorithm for optimization problems, including network reliability (Yeh [58] Yeh, et al [59]), deep learning training (Yeh [60] Yeh, Lin, Liang and Lai [57]), disassembly sequencing problems (Yeh [61] Yeh [62]), energy problems (Lin et al [63]), and so on. Owing to its simplicity and efficiency, SSO is used here to find the best values in vehicle routing of the proposed HFVRP with Time-Varying Continuous Speed Function.…”

Section: Simplified Swarm Optimizationmentioning

confidence: 99%

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

Yeh

Tan

2021

Electronics

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Transportation planning has been established as a key topic in the literature and practices of social production, especially in urban contexts. To consider traffic environment factors, more and more researchers are taking time-varying factors into account when scheduling their logistic activities. The time-dependent vehicle routing problem (TDVRP) is an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) by determining a set of optimal routes serving a set of customers within specific time windows. However, few of them use the continuous speed function to express the time-varying. In practice, many vehicle routing problems are addressed by a fleet of heterogeneous vehicles with different capacities and travel costs including fix costs and variable costs. In this study, a Heterogeneous Fleet Vehicle Routing Problem (HFPRP) Time-Varying Continuous Speed Function has been proposed. The objective is to minimize distribution costs, which contained fixed costs of acquiring and variable fuel costs. To address this problem, our research developed a mathematical model and proposed a Simplified Swarm Optimization (SSO) heuristic for HFVRP with Time-Varying Continuous Speed Function.

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Section: Simplified Swarm Optimizationmentioning

confidence: 99%

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

Yeh

Tan

2021

Electronics

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“…With the objective of optimizing system reliability in various systems, the redundancy allocation problems (RAP), which has to only decide the redundancy of components, and the reliability redundancy allocation problems (RRAP), which has to simultaneously decide reliability and redundancy of components, are two well-known methods to improve the system reliability. RRAP that has been studied in this work is more complicated and difficult than RAP because it must determine the reliability and redundancy of components in the systems at the same time [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…The researches of RRAP over the years have mainly used four network systems as benchmarks including series system, the network with series and parallel elements, a complex (bridge) system, and the overspeed protection of a gas turbine system, which are thus adopted in this study, as shown in following Figures 1-4. Yeh et al researched the biobjective RRAP including optimizing the system reliability and cost [18]. The cold-standby strategy in RRAP was adopted by several researchers such Yeh, Ardakan and Hamadani, 2 of 14 and Mellal and Zio [19,21,22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

2021

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A novel constraints model of credibility-fuzzy for the reliability redundancy allocation problem (RRAP) is studied in this work. The RRAP that must simultaneously decide reliability and redundancy of components is an effective approach in improving the system reliability. In practice various systems, the uncertainty condition of components used in the systems, which few studies have noticed this state over the years, is a concrete fact due to several reasons such as production conditions, different batches of raw materials, time reasons, and climatic factors. Therefore, this study adopts the fuzzy theory and credibility theory to solve the components uncertainty in the constraints of RRAP including cost, weight, and volume. Moreover, the simplified swarm optimization (SSO) algorithm has been adopted to solve the fuzzy constraints of RRAP. The effectiveness and performance of SSO algorithm have been experimented by four famous benchmarks of RRAP.

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“…There has been much research on different solution algorithms for RRAP. For example, particle swarm optimization (PSO) [3,6], nondominated sorting genetic algorithm II (NSGA-II) [7][8][9], artificial bee colony algorithm (ABC) [10], genetic algorithms (GA) [1,2,4,11], simplified swarm optimization (SSO) [12,13], nest cuckoo optimization algorithm [14], a hybrid of PSO and SSO (PSSO) [15], and stochastic fractal search (SFS) [16] have been employed to study for RRAP.…”

Section: Introductionmentioning

confidence: 99%

“…Particle swarm optimization (PSO) that was first developed by Kennedy and Eberhard in 1995 [17] and simplified swarm optimization (SSO) that was originally exploited by Yeh in 2009 [18] are two of the most well-known algorithms in SI. In recent years, we have seen an increasing interest both in PSO [3,6,15,[19][20][21][22][23] and in SSO [12,13,15,[24][25][26][27][28] for solving larger problems in science and technology.…”

Section: Introductionmentioning

confidence: 99%

Solving Cold‐Standby Reliability‐Redundancy Allocation Problems with Particle‐Based Simplified Swarm Optimization

2021

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Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.

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Simplified swarm optimization for bi-objection active reliability redundancy allocation problems

Cited by 27 publications

References 38 publications

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

A Novel Constraints Model of Credibility-Fuzzy for Reliability Redundancy Allocation Problem by Simplified Swarm Optimization

Solving Cold‐Standby Reliability‐Redundancy Allocation Problems with Particle‐Based Simplified Swarm Optimization

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