On Solving Fully Rough Multi-Objective Integer Linear Programming Problems

E. Ammar, A. Emsimir
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引用次数: 1

Abstract

In this paper a suggested algorithm to solve fully rough multi-objectiveinteger linear programming problem [FRMOILP] is described. In orderto solve this problem and find rough value efficient solutions anddecision rough integer variables by the slice-sum method with thebranch and bound technique, we will use two methods, the first one isthe method of weights and the second is e- Constraint method. The basicidea of the computational phase of the algorithm is based onconstructing two LP problems with interval coefficients, and then to fourcrisp LPs. In addition to determining the weights and the values of e-constraint. Also, we reviewed some of the advantages and disadvantagesfor them. We used integer programming because many linearprogramming problems require that the decision variables are integers.Also, rough intervals (RIs) are very important to tackle the uncertaintyand imprecise data in decision making problems. In addition, theproposed algorithm enables us to search for the efficient solution in thelargest range of possible solutions range. Also, we obtain N suggestedsolutions and which enables the decision maker to choose the bestdecisions. Finally, two numerical examples are given to clarify theobtained results in the paper.
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求解完全粗糙多目标整数线性规划问题
本文提出了一种求解全粗糙多目标整数线性规划问题的算法。为了解决这一问题,利用分支定界技术的切片和方法寻找粗糙值有效解和确定粗糙整数变量,我们将使用两种方法,一种是权值法,另一种是e-约束法。该算法计算阶段的基本思想是先构造两个带区间系数的LP问题,再构造四个清晰的LP问题。除了确定e约束的权重和值之外。此外,我们还回顾了它们的一些优点和缺点。我们使用整数规划是因为许多线性规划问题要求决策变量是整数。粗糙区间(RIs)对于解决决策问题中数据的不确定性和不精确性非常重要。此外,该算法使我们能够在可能的解范围的最大范围内搜索有效的解。同时,我们得到了N个建议解决方案,使决策者能够选择最佳决策。最后,给出了两个数值算例来说明本文的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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