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Author Maravelias, Christos T
Title Mixed-integer and constraint programming methods for the scheduling of batch processes and the new product development
Descript 169 p
Note Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0886
Adviser: Ignacio E. Grossmann
Thesis (Ph.D.)--Carnegie Mellon University, 2004
This thesis deals with the development of optimization models in order to address: (a) the scheduling of batch plants, and (b) the simultaneous scheduling of tests and design of batch plants that arise in the New Product Development (NPD) of pharmaceutical and agrochemical industries
To address the issue of developing computationally effective methods for the scheduling of multipurpose batch plants, three optimization approaches are proposed
A novel continuous-time MILP formulation is proposed, that relies on the State Task Network (STN) and addresses the general problem of batch scheduling, accounting for resource (utility) constraints, variable batch sizes and processing times, various storage policies (UIS/FIS/NIS/ZW), batch mixing/splitting, and sequence-dependent changeover times. The proposed model is significantly faster than other continuous-time models and more general than event-driven formulations being equally fast
A hybrid MILP/CP algorithm is proposed, where the original scheduling problem is decomposed into a MILP master problem and a CP subproblem. The decisions about the type and number of tasks performed, as well as the assignment of units to tasks are made by the MILP master problem, while the CP subproblem checks the feasibility of a specific assignment and generates integer cuts. To enhance the performance of the algorithm, a graph-theoretic preprocessing that determines time windows for the tasks and equipment units and two new classes of integer cuts are proposed. The proposed framework can be used for various plant topologies and objective functions. Numerical results show that for some classes of problems, it is two to three orders of magnitude faster than standalone MILP and CP models
In the second part of the thesis we present optimization models for the solution of problems that arise in the New Product Development of agrochemical and pharmaceutical industries. (Abstract shortened by UMI.)
School code: 0041
DDC
Host Item Dissertation Abstracts International 65-02B
Subject Engineering, Chemical
Operations Research
Engineering, Industrial
0542
0796
0546
Alt Author Carnegie Mellon University
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