A two-stage approach for multi-objective decision making with applications to system reliability optimization

Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ 08854, USA
Reliability Engineering [?] System Safety (Impact Factor: 2.41). 10/2009; 94(10):1585-1592. DOI: 10.1016/j.ress.2009.02.022


This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.

Download full-text


Available from: Zhaojun Li, Oct 06, 2015
76 Reads
  • Source
    • "gap between the single solution and the Pareto optimal set by providing decision-makers with a medium-sized set of solutions (several representative solutions) from a holistic view (Li et al., 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Sustainability has been considered as a growing concern in supply chain network design (SCND) and in the order allocation problem (OAP). Accordingly, there still exists a gap in the quantitative modeling of sustainable SCND that consists of OAP. In this article, we cover this gap through simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment, and cross-docks, to satisfy stochastic demand received from a set of retailers. The problem has been mathematically formulated as a multi-objective optimization model that aims at minimizing the total costs and environmental effect of integrating SCND and SMP, simultaneously. To tackle the addressed problem, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism mechanism algorithm (AMOEMA) and adapted multi-objective variable neighborhood search (AMOVNS). According to achieved results, MOHEV achieves better solutions compared with the others, and also crowding distance method for BPSP outperforms minimum distance. Finally, a case study for an automobile industry is used to demonstrate the applicability of the approach.
    Computers & Operations Research 01/2015; DOI:10.1016/j.cor.2014.12.014 · 1.86 Impact Factor
  • Source
    • "Observe that in general, these problems are challenging to solve, especially for the large systems. Li et al. [23] developed a two-stage approach for solving MOSROP. In their approach, a Pareto optimal solution set is initially identified at the first stage via a multiobjective evolutionary algorithm. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., MOGA, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.
    Cybernetics, IEEE Transactions on 01/2015; DOI:10.1109/TCYB.2014.2382666 · 3.47 Impact Factor
  • Source
    • "This approach renders considering all of the Pareto-optimal solutions inefficient. To deal with this inefficiency, it is recommended that the size of the solution set be decreased (Li et al., 2009). Our proposed approach aims at reducing the size of the solution set as an extension of latest research in multi-objective problems (Taboada et al., 2007; Kulturel-Konak et al., 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Multi-objective optimization problems normally have not one but a set of solutions, which are called Pareto optimal solutions or non-dominated solutions. Once a Pareto-optimal set has been obtained, the decision maker faces the challenge of analyzing a potentially large set of solutions. Selecting one solution over others can be quite a challenging task because the Pareto set can contain an unmanageable number of solutions. This process is called post-Pareto optimality analysis. To deal with this difficulty, this study proposes the approach that promisingly prunes the Pareto optimal set. In this study, the newly developed approach uses Monte-Carlo simulation taking into account the decision maker’s prioritization to prune the Pareto optimal set. Then, the central weight vector, the optimal frequently appearance index and upper and lower bands of weights are enclosed to each solution to facilitate selecting a final solution. The well-known redundancy allocation problem is used to show the performance of the proposed method.
    International Journal of Applied Decision Sciences 01/2013; 6(1). DOI:10.1504/IJADS.2013.052632
Show more