Linear optimization problem constrained by fuzzy max-min relation equations
Linear optimization problem constrained by fuzzy max-min relation equations
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DOI：
10.1016/j.ins.2011.04.042
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Cheung-Wen Chang;B. Shieh
中科院分区：
文献类型：
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作者：
Cheung-Wen Chang;B. Shieh
Fang 和 Li 引入了具有线性目标函数并受模糊最大最小关系方程约束的优化模型。他们将这个问题转化为0-1整数规划问题，并使用跳跃跟踪分支定界法来解决。随后，吴等人。通过提供最优目标值的上限改进了该方法，并提出了三个简化最优解计算的规则。这项工作提出了有关该优化问题的新理论结果。它们包括改进的最佳目标值上限、改进的简化问题的规则以及减少解决方案树的规则。因此，提出了一种用于寻找最佳目标值的加速方法，并且代表了对早期方法的改进。讨论了其潜在的应用。
Fang and Li introduced the optimization model with a linear objective function and constrained by fuzzy max–min relation equations. They converted this problem into a 0–1 integer programming problem and solved it using the jump-tracking branch-and-bound method. Subsequently, Wu et al. improved this method by providing an upper bound on the optimal objective value and presented three rules for simplifying the computation of an optimal solution. This work presents new theoretical results concerning this optimization problem. They include an improved upper bound on the optimal objective value, improved rules for simplifying the problem and a rule for reducing the solution tree. Accordingly, an accelerated approach for finding the optimal objective value is presented, and represents an improvement on earlier approaches. Its potential applications are discussed.