Iterative MILP algorithm to find alternate solutions in linear programming models

IF 2 3区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Optimization and Engineering Pub Date : 2024-04-26 DOI:10.1007/s11081-024-09887-3
Dev A. Kakkad, Ignacio E. Grossmann, Bianca Springub, Christos Galanopoulos, Leonardo Salsano de Assis, Nga Tran, John M. Wassick
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Abstract

We address in this paper linear programming (LP) models in which it is desired to find a finite set of alternate optima. An LP may have multiple alternate solutions with the same objective value or with increasing objective values. For many real life applications, it can be interesting to have a pool of solutions to compare what operations should be executed and what is the cost/benefit of doing it. To obtain a specified number of these alternate solutions in the increasing order of objective values, we propose an iterative MILP algorithm in which we successively add integer cuts on inactive constraints. We demonstrate the application and effectiveness of this algorithm on a 2 dimensional LP and on small and large supply chain problems. The proposed iterative MILP algorithm provides an effective approach for finding a specified number of alternate optima in LP models, which provides a useful tool in a variety of applications as for instance in supply chain optimization problems.

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在线性规划模型中寻找替代解决方案的迭代 MILP 算法
我们在本文中讨论了线性规划(LP)模型,在这些模型中,我们希望找到一组有限的备用最优解。一个 LP 可能有多个目标值相同或目标值递增的备用解决方案。在现实生活中的许多应用中,拥有一组解决方案来比较应该执行哪些操作以及这样做的成本/收益是非常有趣的。为了按照目标值递增的顺序获得一定数量的备用解决方案,我们提出了一种迭代 MILP 算法,在该算法中,我们连续添加了对非活动约束条件的整数切分。我们演示了该算法在二维 LP 以及小型和大型供应链问题上的应用和有效性。所提出的迭代 MILP 算法为在 LP 模型中寻找指定数量的备用最优值提供了一种有效的方法,为供应链优化问题等各种应用提供了有用的工具。
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来源期刊
Optimization and Engineering
Optimization and Engineering 工程技术-工程:综合
CiteScore
4.80
自引率
14.30%
发文量
73
审稿时长
>12 weeks
期刊介绍: Optimization and Engineering is a multidisciplinary journal; its primary goal is to promote the application of optimization methods in the general area of engineering sciences. We expect submissions to OPTE not only to make a significant optimization contribution but also to impact a specific engineering application. Topics of Interest: -Optimization: All methods and algorithms of mathematical optimization, including blackbox and derivative-free optimization, continuous optimization, discrete optimization, global optimization, linear and conic optimization, multiobjective optimization, PDE-constrained optimization & control, and stochastic optimization. Numerical and implementation issues, optimization software, benchmarking, and case studies. -Engineering Sciences: Aerospace engineering, biomedical engineering, chemical & process engineering, civil, environmental, & architectural engineering, electrical engineering, financial engineering, geosciences, healthcare engineering, industrial & systems engineering, mechanical engineering & MDO, and robotics.
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