An exact method for scheduling of the alternative technologies in R&D projects Mohammad Ranjbar a,n , Morteza Davari b a Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, P.O. Box: 91775-1111, Iran b Faculty of Business and Economics, KULeuven, Belgium article info Available online 16 July 2012 Keywords: Project scheduling Technology Research and development Innovation abstract A fundamental challenge associated with research or new product development projects is identifying that innovative activity that will deliver success. In such projects, it is typically the case that innovative breakthroughs can be achieved by any of several possible alternative technologies, some of which may fail due to the technological risks involved. In some cases, the project payoff is obtained as soon as any single technology is completed successfully. We refer to such a project as alternative-technologies project and in this paper we consider the alternative-technologies project scheduling problem. We examine how to schedule alternative R&D activities in order to maximize the expected net present value, when each technology has a cost and a probability of failure. Although a branch-and-bound algorithm has been presented for this problem in the literature, we reformulate the problem and develop a new and improved branch-and-bound algorithm. We show using computational results that the new algorithm is much more efficient and outperforms the previous one. & 2012 Elsevier Ltd. All rights reserved. 1. Introduction The development of complex and innovative products is characterized by much uncertainty. In order to deal with this uncertainty, it has been suggested that research and development (R&D) projects should pursue multiple alternative solutions for developing the new products (see, for instance, [1] and [2]). The scheduling of these attempts, hereafter referred to as alternatives, is crucial for increasing the likelihood of successfully developing a product, minimizing development time and obtaining revenues as early as possible. Consider, for instance, a software development firm that has the option to develop their web services using either a traditional Java SPRING framework or the pioneering Ruby-on- Rails framework. While both might achieve a similar function- ality, the traditional Java SPRING framework will take longer to develop, but is more likely to handle the expected volume of users. A similar situation happens in the formulation, delivery and packaging development phase of the pharmaceutical drug-devel- opment process in which drug developers must devise a formula- tion that ensures the proper drug delivery parameters. It is critical to begin looking ahead to clinical trials at this phase of the drug development process. Drug formulation and delivery may be refined continuously until, and even after, the drug’s final approval. Trials have different costs, durations and probability of success, and optimal scheduling of these trials saves a notice- able amount of money for the drug developer firm (see [3]). In this paper, we focus on a single firm engaged in a single R&D or new product development (NPD) project. The project can be achieved by any one of several given alternatives. Each alternative is characterized by a cost, a duration and a probability of technical success (PTS). The successful completion of an alternative corre- sponds to the completion of the project and obtaining the project payoff. In other words, depending on the schedule and the realized successes of alternatives, some alternatives of the project will not be performed. Also, if in the time at which the success of an alternative is realized, there are some other alternatives in progress, they will be ignored. Since it is assumed that the cost of each alternative is incurred at the beginning of alternative while the project payoff will be obtained at the end of a successful alternative, there is the downside risk of disregarding some in progress alternatives. A serial schedule, in which alternatives are not attempted simultaneously, is conservative in terms of costs and minimizing the downside risk, but might result in the maximum project duration. On the other extreme, simultaneously developing all the potential alternative technologies, which could lead to the minimum project duration and an earlier launch date, carries a large downside risk and higher upfront costs (see, [4] and [5]). Our goal is to analyze such trade-offs and to solve the underlying optimization problem, which will be referred to as the Contents lists available at SciVerse ScienceDirect journal homepage: www.elsevier.com/locate/caor Computers & Operations Research 0305-0548/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cor.2012.07.005 n Corresponding author. Tel.: þ98 511 8805092. E-mail addresses: m_ranjbar@um.ac.ir (M. Ranjbar), morteza.davari@student.kuleuven.be (M. Davari). Computers & Operations Research 40 (2013) 395–405