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Heuristics for improving the solution of p-median location problem with Erlenkotter approach

机译:用Erlenkotter方法改进p中位数位置问题解决方案的启发式方法

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This paper deals with the problem of designing the optimal structure of a public service system. The problem can be often formulated as a p-median location problem. Solving the p-median location problem in the public service system design is NP-hard problem. Real instances of the problem are characterized by big number of possible service center locations, which can take the value of several hundreds or thousands. The optimal solution can be obtained by the universal IP solvers only for smaller instances of the problem. The universal IP solvers are very time-consuming and often fail when a large instance is solved. Our approach to the problem is based on the Erlenkotter procedure and the Lagrangean relaxation. The Erlenkotter procedure solves the uncapacitated facility location problem. Using the Lagrangean relaxation allow to solve the p-median location problem. The suggested approach finds the optimal solution in the most studied instances. The feasibility and the quality of the resulting solutions of the suggested approach depends on the convenient setting of the Lagrangean multiplier. The convenient value of the multiplier can be obtained by a bisection algorithm. The resulting multiplier cannot guarantee the determination of the optimal solution, but it ensures the lower bound and the near-to-optimal solution. If our approach does not obtain the optimal solution, then it improves the near-to-optimal solution by some heuristic. We designed some heuristics for improving the obtained solution and choose the best improving heuristic. The improving heuristics are verified on comparison the resulting solution obtained by the improving heuristics and the optimal solution obtained by the universal IP solver XPRESS-IVE in the computational time and the quality of solutions.
机译:本文讨论了设计公共服务系统最佳结构的问题。该问题通常可以表述为p中位数位置问题。解决公共服务系统设计中的p中位数位置问题是NP难题。该问题的真实实例的特征是服务中心可能位于大量地点,这些地点可能价值数百或数千。通用IP解决方案只能针对问题的较小实例获得最佳解决方案。通用IP解算器非常耗时,并且在解决大型实例时通常会失败。我们针对该问题的方法基于Erlenkotter程序和Lagrangean松弛。 Erlenkotter程序解决了功能丧失的设备位置问题。使用拉格朗日松弛法可以解决p中值位置的问题。建议的方法可以在研究最多的实例中找到最佳解决方案。所提出方法的解决方案的可行性和质量取决于拉格朗日乘数的方便设置。乘数的方便值可以通过二等分算法获得。所得乘数不能保证确定最佳解,但是可以确保下界和接近最佳解。如果我们的方法没有获得最佳解,那么它会通过某种启发式方法来改善接近最佳的解。我们设计了一些启发式方法来改进获得的解决方案,并选择最佳的改进式启发式方法。通过比较改进的启发式方法得到的结果解决方案和通用IP求解器XPRESS-IVE的最佳解决方案在计算时间和解决方案质量方面的比较,验证了改进的启发式方法。

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