Optimal selection policies for a sequence of candidate drugs

Charalambous, Charis; Gittins, John

doi:10.1239/aap/1214950208

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…The RCPSP has also been extended to include ideas such as uncertain duration , partially renewable resources (Bottcher et al 1999), and maximizing net present value rather than minimizing makespan . As far as R&D planning is concerned, the problems of portfolio selection in the pre-clinical trials section (Charalambous and Gittins 2008), and the planning of R&D activities with technical failure without resource constraints have been addressed. Recently, an anticipatory algorithm for the stochastic RCPSP (Mercier and Van Hentenryck 2008) and a MIP model for portfolio optimization (Solak et al 2010) were proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Hazır

Haouari

Erel

2014

Handbook on Project Management and Scheduling Vol. 2

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This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher's location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) PrefaceThis handbook is devoted to scientific approaches to the management and scheduling of projects. Due to their practical relevance, project management and scheduling have been important subjects of inquiry since the early days of Management Science and Operations Research and remain an active and vibrant field of study. The handbook is meant to provide an overview of some of the most active current areas of research. Each chapter has been written by well-recognized scholars, who have made original contributions to their topic. The handbook covers both theoretical concepts and a wide range of applications. For our general readers, we give a brief introduction to elements of project management and scheduling in the first chapter, where we also survey the contents of this book. We believe that the handbook will be a valuable and comprehensive reference to researchers and practitioners in project management and scheduling and hope that it might stimulate further research in this exciting and practically important field.Short-listing and selecting the contributions to this handbook and working with more than one hundred authors have been a challenging and rewarding experience for us. We are grateful to Günter Sch...

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Section: Literature Reviewmentioning

confidence: 99%

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Hazır

Haouari

Erel

2014

Handbook on Project Management and Scheduling Vol. 2

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“…The derivation is given by Charalambous [4], who also investigated numerically the relationships between the optimal TEDR and k and the various parameters of our model.…”

Section: ))mentioning

confidence: 99%

Optimal selection policies for a sequence of candidate drugs

Charalambous

Gittins

2008

Adv. Appl. Probab.

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Pharmaceutical companies have to face huge risks and enormous costs of production before they can produce a drug. Efficient allocation of resources is essential to help in maximizing profits. Yu and Gittins (2007) described a model and associated software for determining efficient allocations for a preclinical research project. This is the starting point for this paper. We provide explicit optimal policies for the selection of successive candidate drugs for two restricted versions of the Yu and Gittins (2007) model. To some extent these policies are likely to be applicable to the unrestricted model.

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“…Problems of this form abound in the MDP literature, including the lot-sizing problem [13, pp. 151-159], the equipment replacement problem [18], the sequential search problem [10], and various resource allocation problems (see [3], [4], and [6]). Bounds on the perturbations in the state values for a given policy are computed in [12].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters

Tan

Hartman

2011

Journal of Applied Probability

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Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be performed directly for a Markov decision process with uncertain reward parameters using the Bellman equations. In particular, we consider problems involving (i) a single stationary parameter, (ii) multiple stationary parameters, and (iii) multiple nonstationary parameters. We illustrate the applicability of this work through a capacitated stochastic lot-sizing problem.

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Optimal selection policies for a sequence of candidate drugs

Cited by 9 publications

References 8 publications

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Robust Optimization for the Discrete Time-Cost Tradeoff Problem with Cost Uncertainty

Optimal selection policies for a sequence of candidate drugs

Sensitivity Analysis in Markov Decision Processes with Uncertain Reward Parameters

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