scholarly journals Planning under uncertainty solving large-scale stochastic linear programs

1992 ◽  
Author(s):  
G. Infanger
Keyword(s):  
Large Scale ◽  
Linear Programs ◽  
Planning Under Uncertainty ◽  
Stochastic Linear Programs

scholarly journals A warm-start approach for large-scale stochastic linear programs

2009 ◽  
Vol 127 (2) ◽  
pp. 371-397 ◽  
Author(s):  
Marco Colombo ◽  
Jacek Gondzio ◽  
Andreas Grothey
Keyword(s):  
Large Scale ◽  
Linear Programs ◽  
Warm Start ◽  
Stochastic Linear Programs

Stochastic Decomposition for Two-Stage Stochastic Linear Programs with Random Cost Coefficients

2020 ◽  
Author(s):  
Harsha Gangammanavar ◽  
Yifan Liu ◽  
Suvrajeet Sen
Keyword(s):  
Large Scale ◽  
Computational Time ◽  
Stochastic Decomposition ◽  
Linear Programs ◽  
Sample Sizes ◽  
Practical Applications ◽  
Second Stage ◽  
Sample Average Approximations ◽  
Incremental Sampling ◽  
Stochastic Linear Programs

Stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or arbitrary sample sizes to create sample average approximations (SAA). When compared with the solutions obtained using the SAA procedure, SD provides solutions of similar quality in far less computational time using ordinarily available computational resources. However, previous versions of SD were not applicable to problems with randomness in second-stage cost coefficients. In this paper, we extend its capabilities by relaxing this assumption on cost coefficients in the second stage. In addition to the algorithmic enhancements necessary to achieve this, we also present the details of implementing these extensions, which preserve the computational edge of SD. Finally, we illustrate the computational results obtained from the latest implementation of SD on a variety of test instances generated for problems from the literature. We compare these results with those obtained from the regularized L-shaped method applied to the SAA function of these problems with different sample sizes.

Implementing post-trial access plans for HIV prevention research

2018 ◽  
Vol 44 (5) ◽  
pp. 354-358 ◽  
Author(s):  
Amy Paul ◽  
Maria W Merritt ◽  
Jeremy Sugarman
Keyword(s):  
Hiv Prevention ◽  
Large Scale ◽  
Healthcare Systems ◽  
System Level ◽  
Planning Under Uncertainty ◽  
Work Related ◽  
Prevention Trials ◽  
Practical Experiences ◽  
Post Trial ◽  
Local Healthcare

Ethics guidance increasingly recognises that researchers and sponsors have obligations to consider provisions for post-trial access (PTA) to interventions that are found to be beneficial in research. Yet, there is little information regarding whether and how such plans can actually be implemented. Understanding practical experiences of developing and implementing these plans is critical to both optimising their implementation and informing conceptual work related to PTA. This viewpoint is informed by experiences with developing and implementing PTA plans for six large-scale multicentre HIV prevention trials supported by the HIV Prevention Trials Network. These experiences suggest that planning and implementing PTA often involve challenges of planning under uncertainty and confronting practical barriers to accessing healthcare systems. Even in relatively favourable circumstances where a tested intervention medication is approved and available in the local healthcare system, system-level barriers can threaten the viability of PTA plans. The aggregate experience across these HIV prevention trials suggests that simply referring participants to local healthcare systems for PTA will not necessarily result in continued access to beneficial interventions for trial participants. Serious commitments to PTA will require additional efforts to learn from future approaches, measuring the success of PTA plans with dedicated follow-up and further developing normative guidance to help research stakeholders navigate the complex practical challenges of realising PTA.

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