MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

Huang, Mengyu; Huang, Lingying; Zhong, Yuxing; Yang, Huiwen; Chen, Xiao-Meng; Huo, Wei; Wang, Jiazheng; Zhang, Fan; Bai, Bo; Shi, Ling

doi:10.1007/s40305-023-00493-1

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…So, we cannot prove the uniqueness of the solution for the MILP model constructed in this article. Just as Huang et al [47] proved in their paper that MILP is an NP hard problem with multiple solutions, the original problem needs to be divided into several subproblems for solving.…”

Section: Basic Modelmentioning

confidence: 99%

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

Xu,

Ma,

Liu

et al. 2024

Sustainability

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The uncertainty of post-earthquake disaster situations can affect the efficiency of rescue site selection, material, and personnel dispatching, as well as the sustainability of related resources. It is crucial for decision-makers to make decisions to mitigate risks. This paper first presents a dual-objective model for locating emergency logistics facilities, taking into account location costs, human resource scheduling costs, transportation time, and uncertainties in demand and road conditions. Then, stochastic programming and robust optimization methods are utilized to cater to decision-makers with varying risk preferences. A risk-preference-based stochastic programming model is introduced to handle the potential risks of extreme disasters. Additionally, robust models are constructed for two uncertain environments. Finally, the study uses the Wenchuan earthquake as a case study for the pre-locating of emergency logistics facilities and innovatively compares the differences in the effects of models constructed using different uncertainty methods. Experimental results indicate that changes in weight coefficients and unit transportation costs significantly impact the objective function. This paper suggests that decision-makers should balance cost and rescue efficiency by choosing appropriate weight coefficients according to the rescue stage. It also shows that risk level and robust conservatism can significantly alter the objective function. While stochastic programming models offer economic advantages, robust optimization provides better robustness.

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Section: Basic Modelmentioning

confidence: 99%

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

Xu,

Ma,

Liu

et al. 2024

Sustainability

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“…This approach markedly reduces the computational time typically consumed by numerous repeated search sequences after each iteration. For further exploration, refer to [12][13][14][15][16][17][18][19][20][21][22]. Notably, the structural underpinnings of most of these methods are rooted in negative variables.…”

mentioning

confidence: 99%

A Paradigm Shift in Linear Programming: An Algorithm without Artificial Variables

Edalatpanah

2023

Syst. Anal.

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| IntroductionEmpirical assessments affirm that Linear Programming (LP) occupies a paramount position within the expansive domain of operations research. It's a striking revelation that many real-world predicaments can be seamlessly transmuted into LP models, thereby establishing this framework as an ineluctable cornerstone in contemporary applications spanning diverse domains, including energy management, military strategy, transportation logistics, and precision manufacturing optimization.The seminal Dantzig's Simplex method, incubated within the crucible of the American Air Force during the tumultuous era of the Second World War [1], stands firmly at the forefront of productive solution techniques

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MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

Cited by 2 publications

References 54 publications

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

Emergency Logistics Facilities Location Dual-Objective Modeling in Uncertain Environments

A Paradigm Shift in Linear Programming: An Algorithm without Artificial Variables

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