LEADER 00000nam  2200385   4500 
001    AAI3123778 
005    20050228064437.5 
008    050228s2004                        eng d 
035    (UnM)AAI3123778 
040    UnM|cUnM 
100 1  Maravelias, Christos T 
245 10 Mixed-integer and constraint programming methods for the 
       scheduling of batch processes and the new product 
       development 
300    169 p 
500    Source: Dissertation Abstracts International, Volume: 65-
       02, Section: B, page: 0886 
500    Adviser: Ignacio E. Grossmann 
502    Thesis (Ph.D.)--Carnegie Mellon University, 2004 
520    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 
520    To address the issue of developing computationally 
       effective methods for the scheduling of multipurpose batch
       plants, three optimization approaches are proposed 
520    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 
520    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 
520    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.) 
590    School code: 0041 
590    DDC 
650  4 Engineering, Chemical 
650  4 Operations Research 
650  4 Engineering, Industrial 
690    0542 
690    0796 
690    0546 
710 20 Carnegie Mellon University 
773 0  |tDissertation Abstracts International|g65-02B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
       advanced?query=3123778