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First, a multiple objective earthquake emergency shelter location allocation model is developed.
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To provide a multiple objective solution we used Pareto analysis.
The growing literature of STP focuses on multiple objective problems, that is, multiple objective solid transportation problems (MOSTPs).
Most recently, Xu et al. (2018) provided a hybrid bi-level model for earthquake emergency shelter location and allocation by considering the dynamic number of evacuees and its implementation, and the model results were also compared with the ones from multiple objective models.
Since most real-life problems include conflicting objectives, multiple objective optimization provides a means for obtaining more realistic models.
Since the solution algorithm of the FFP can transform multiple objective functions to equivalent constraints, it makes the FFP straightforward to deal with importance of multiple objectives.
To balance these conflicts, we treated each training set as a separate objective in a multiple objective optimization calculation.
The algorithm has also been used to solve earthquake disaster shelter location and allocation problems by adding multiple objectives, strict constraints, and discrete feasible domains (Hu et al. 2012; Zhao et al. 2015).
In acting today, the Fed appeared to have multiple objectives.
Supply chains are increasingly scrutinised and expected to meet multiple objectives.
Companies have to satisfy multiple objectives (such as quality, safety and overall environmental impact).
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