Pipeline management

Subramanian D, Pekny JF, Reklaitis. A simulation-optimization framework for research and development pipeline management. AIChE J 2001 47(10) 2226. 

The basic stock/flow structure and the associated simulation model of pharmaceutical pipeline management provide a useful framework to analyze basic drug development... 

Using the elaborated theoretical and computing approaches a most effective pipeline management system has been conceptually elaborated with control modes at local level, station level and pipeline level. Local control is manually implemented from the device itself. The automatic or manual control of an installation is affected from its local micro-SCADA system. The supervisory pipeline control is effected from the active pipeline control centre. 

Subramanian D., Pekny J. and Reklaitis G.V. 2000. A simulation-optimization framework for addressing combinatorial and stochastic aspects on an R D pipeline management problem. Comp. Chem. Eng., 24, 1005-1011. 

R. E. Ricker, Analysis of Pipeline Steel Corrosion Data front NBS (NIST) Studies Conducted between 1922-1940 and Relevance to Pipeline Management, NISTER 7415, National Institute of Standards and Technology, U.S. Department of Commerce, May 2,2007, pp. 1-2. 

Farmer, D. van Amstel, R. P. (1991), Effective Pipeline Management, Aldershot ... 

A major component of this framework is the use of nondestructive evaluation technicjues to provide information about the condition of the pijjeline. All pip>es will eventually fail, but the rate of failure will dejjend on both the pipe material and the actual exposure to environmental and ojjerational conditions. The most important feature of the framework relates to the cyclical nature of pipeline management. Each pipe in the system must be examined periodically, and its condition reassessed in order to determine what action should be taken to maintain or upgrade its condition. The entry point to the cycle is the pipe selection area labeled as "Prioritize Pipes for Analysis" in Fig. 8.3. 

The key to the successful control of logistics lead times is pipeline management. Pipeline management is the process whereby manufacturing and procurement lead times are linked to the needs of the marketplace. At the same time, pipeline management seeks to meet the competitive challenge of increasing the speed of response to those market needs. 

From trucks and sheds to end-to-end pipeline management A wider definition of supply chain cost Understanding of the cost-to-serve and time-based performance indicators... 

The two enterprise-wide decision problems that have received lately most attention have been SCM and product development pipeline management (PDPM). SCM addresses the design and operational issues associated with the network with which an enterprise delivers its products to the marketplace, while PDPM addresses the set of decisions associated with turning a new discovery into a product and introducing it into the corporation s SC (Varma et al. 2007). Their successful solution has a direct impact on the viability of the enterprise. Ideally, all these decisions need to be coordinated. However, this is often not the case, leading to suboptimal portfolios and ineffective utilization of capital and R D resources (Varma et al. 2007). 

The Sim-Opt framework to deal with the R D pipeline management problem is developed by Subramanian et al. (2001). This work is extended to incorporate information from the simulation to improve solutions in terms of expected NPV and the probability of delivering a positive NPV (Subramanian et al. 2003). Finally, a multiphase version of the Sim-Opt architecture is presented by Zapata et al. (2008). 

The Optimal Condition Decomposition (OCD), which is a particular case of the Lagrangian relaxation procedure, is applied to overcome the computational cost of solving the monolithic problem, which integrates SC design-retrofitting and product development pipeline management formulations. Further details about this decomposition strategy can be found in Appendix A.7.1. 

Subramanian, D., Pekny, J. F, Reklaitis, G. V., Blau, G. E. (2003). Simulation-optimization framework for stochastic optimization of R D pipeline management. AIChE Journal, 49, 96-112. 

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