Variants to a knapsack decomposition heuristic for solving R&D pipeline management problems-Reference-Cited by-同舟云学术

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Variants to a knapsack decomposition heuristic for solving R&D pipeline management problems

Published:2017-01 Issue: Volume:96 Page:18-32
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Christian Brianna,Cremaschi Selen

Funder

US National Science Foundation through the Auburn University Integrative Graduate Research and Education Traineeship

NSF Career Grant

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference12 articles.

1. Heuristic solution approaches to the pharmaceutical R&D pipeline management problem;Christian;Comput. Chem. Eng.,2015

2. A stochastic programming approach for clinical trial planning in new drug development;Colvin;Comput. Chem. Eng.,2008

3. Scheduling of testing tasks and resource planning in new product development using stochastic programming;Colvin;Computers & Chemical Engineering,2009

4. A stochastic programming approach to planning of offshore gas field developments under uncertainty in reserves;Goel;Comput. Chem. Eng.,2004

5. A class of stochastic programs with decision dependent uncertainty;Goel;Math. Program.,2006

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1. A graph theoretic approach to non-anticipativity constraint generation in multistage stochastic programs with incomplete scenario sets;Computers & Operations Research;2022-10

2. Pharmaceutical R & D pipeline management under trial duration uncertainty;Computers & Chemical Engineering;2020-05

3. Decision-support challenges in the chemical-pharmaceutical industry: Findings and future research directions;Computers & Chemical Engineering;2020-03

4. A general primal bounding framework for large-scale multistage stochastic programs under endogenous uncertainties;Chemical Engineering Research and Design;2019-01

5. A primal bounding approach for multistage stochastic programs of resource-constrained planning and scheduling with stochastic task success;Computer Aided Chemical Engineering;2019

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