The Stochastic Discrete Time-Cost Tradeoff Problem with Decision-Dependent Uncertainty

Klerides, Evelina; Hadjiconstantinou, Eleni

doi:10.1007/978-3-319-05915-0_6

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Generally, all activities are considered with normal durations in traditional project planning (Klerides & Hadjiconstantinou, 2015). However, the decision makers sometimes have to complete the project earlier due to various reasons such as unforeseen delays, incentive contracts, imposed deadlines, contract commitments, overhead costs and pressure to move resources to other project.…”

Section: Introductionmentioning

confidence: 99%

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Albayrak

Özdemir

2018

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Completion of the activities within optimal time and cost plays a significant role in construction projects. Recently, project managers have to decrease the total durations and costs of the projects more than before due to the competitive environment. Mostly, decision makers usually seek different alternatives which reduce time and cost. As well as being one of the most major topics of construction management, this problem called time-cost trade-off (TCTO) which is extremely difficult to solve with traditional mathematical methods. In recent years, metaheuristic algorithms are outstanding methods in this field due to their flexible and adaptable structure. This paper presents a new algorithm called F-PSO which consists of hybridizing Firefly Algorithm (FA) with Particle Swarm Optimization (PSO). In this method, the problem is modelled with various execution modes to select the optimal one for each activity. The applicability and validity of the proposed method is confirmed by performing 18-activity project as a benchmark problem. Comparison of numerical results with different metaheuristic algorithms demonstrates the effectiveness and efficiency of F-PSO with regard to optimality of time and cost outcomes.

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Section: Introductionmentioning

confidence: 99%

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Albayrak

Özdemir

2018

rdlc

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“…In this stochastic literature, a number of papers have also used the approach of representing the time-cost trade-off on a single objective aimed at minimizing the project's total cost, which is assumed to be composed by activity costs and tardiness penalty costs (e.g., Gutjahr et al (2000); Zhu et al (2007); Tereso et al (2004); Godinho & Branco (2012); Said & Haouari (2015)). Within this context, Gutjahr et al (2000), Zhu et al (2007), and Said & Haouari (2015) have investigated two-stage stochastic problems, while Tereso et al (2004), Godinho & Branco (2012), and Klerides & Hadjiconstantinou (2015) have proposed adaptive policies for multi-stage decisions. In Chapter 5, we also represent the time-cost trade-off by a similar total cost minimization in the context of a portfolio of multiple interdependent projects.…”

Section: Multi-mode Project Schedulingmentioning

confidence: 99%

Effective Resource Allocation for Planning and Control Project Portfolios Under Uncertainty: A Robust Optimization Approach

MENDES¹

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To my advisor Marcus Poggi, for all the opportunities, teaching, support and advices throughout my academic and professional life. To my friend and co-advisor Bruno Flach, for giving me the chance to pursue my PhD while working at IBM, and for all the academic collaboration, inspiration and fun we've had during this journey. To the examination committee, for the various valuable contributions during the defense. To my IBM friends and co-workers, for all the support, fun and inspiration throughout these years. To my friends Hélio, Fábio, Cândida, Carlos, Stephanie, Felipe, Gisele, Valente and Antony, for being my carioca family, and for all the love, support and joy you share with me, Aurélia, Felipão and everyone close to us. To my friends Daniel and César, for their friendship in all key moments of my life, specially in the early years of our arrival in Rio. Thanks for everything my friends, you were really important for this moment to be possible. To my father Carlos, for all the support you dedicate to me and my brothers from childhood to adult life, and to be a model of honesty and integrity. To my family, for being the best family I can imagine, giving me the basis to be the person I am today. To my mom Armênia, to have dedicated her life to her sons, teaching us every good part of our personalities. Mom if this moment is a victory, it is yours more than mine, you gave everything to us, I hope we can return all you deserve to provide you with a life full of joy. Love you mom! To the love of my life, my wife Aurélia, for being the source of my happiness, for all the love you give to me and our son, for having supported me in all difficult moments and always pointed out the right decision for every problem, for giving me my most precious gift in life, our son Felipe, and for being this incredible, beautiful, dedicated and wise mom. My love, you are more than I could ever dreamed of, my admiration and love for you is infinite, I'm immensely lucky and happy to build a family by your side! To my son Felipe, for being such a great son and human being, for teaching me every day to be a better father and person, for all the love you give to me, and for simply be part of my world and the meaning of my life. My son, I love you in the way that I can't explain, I'm sorry for the lost moments while I was working in this PhD, your comprehension and affection were fundamental to me to have finished this journey successfully, now it is time to fulfill my promise and build a game with you!

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“…A few network design problems in other applications are similar to the problem in clinical trial supply chains and consider the tradeoff between time and cost. The tradeoff has been modeled in multi-objective problems of facility location (Arntzen et al 1995, Badri et al 1998, da Graça Costa et al 2008, Pasandideh et al 2013, project schedule Klerides 2010, Klerides andHadjiconstantinou 2015), JIT inventory allocation (Farahani and Elahipanah 2008), order allocation (Chan and Chung 2004), and transportation network design (Chen et al 2010). However, since a clinical trial is stopped when its patient accrual goal is reached, the clinical trial duration is different from those in above studies, where the duration to be minimized is the total time required to complete a predetermined number of jobs with limited resources and fixed processing times.…”

Section: Network Designmentioning

confidence: 99%

Optimal network design and inventory planning for clinical trial supply chains

Zhao¹

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The clinical trial plays a key role in the competitiveness of a pharmaceutical company. It is a critical step in new drug development and characterized by long duration, high cost, and high potential return. Due to the increasing complexity of new drug development, the profit is decreasing and the pharmaceutical companies strive to reduce the cost. In this thesis, we strive to improve the efficiency in three problems of clinical trial supply chains, including the inventory allocation, network design, and production planning problems. For each of these problems, we make a discussion of the relevant literature, modeling approaches, and algorithms. Due to the patient recruitment uncertainty in clinical Chapter 1 Introduction Background of Clinical TrialsNew drug development is characterized by high investment and low success rate (Laínez et al. 2012, Shah 2004. It follows a long sequence of steps, including the discovery, animal trials, Food and Drug Administration (FDA) application, product and process development, three phases of clinical trials, FDA filing and approval, and launch to the drug market of the United States (Colvin and Maravelias 2008). It is estimated that only around five of 5000 to 10000 discovered compounds perform well enough and can be selected to take the clinical trials (Profile 2010), and only one of the five can get the approval of FDA and enter the U.S. drug marketplace (DiMasi et al. 2003). If a candidate fails, all investment is wasted. About 67.4 billion dollars has been invested in Research and Development (R&D) in major pharmaceutical companies in 2010. It is about 17% of the total annual sales of these companies (Khanna 2012).Despite the high expense and low success rate, a successful drug is rewarding. It can not only help patients alleviate their pains or extend their lives, but also can generate large profits for the responsible pharmaceutical company.Under the FDA's regulations, each new drug is granted 20 years of patent protection. Once a new drug successfully enters the drug marketplace, a patentprotected drug can be highly profitable. A blockbuster can generate $1 billion or more in annual sales revenue, which not only compensates all previous R&D expenses but also generates a high profit for the company (Mullin 2011).Clinical trials play a key role in the process of developing new drugs. A new drug has to be tested on human beings in order to determine its safety, efficacy, the limited resources (e.g. time, cost, and labor) and may delay the schedule of other trials. Hence, probably the most important driver in the pharmaceutical industry is the time-to-market (Shah 2004). All pharmaceutical companies strive to commercialize their new drugs as soon as possible. The clinical trial duration should be minimized to gain the most profit. rates of the participants are decreasing in existing regions (Profile 2010, Thiers, et al. 2008. As a result, enrolling enough qualified patients can be timeconsuming at only one or a few locations. In addition, since clinical trials are usually ...

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The Stochastic Discrete Time-Cost Tradeoff Problem with Decision-Dependent Uncertainty

Cited by 4 publications

References 15 publications

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Multimodal optimization for time-cost trade-off in construction projects using a novel hybrid method based on FA and PSO

Effective Resource Allocation for Planning and Control Project Portfolios Under Uncertainty: A Robust Optimization Approach

Optimal network design and inventory planning for clinical trial supply chains

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