MARC 主機 00000nam  2200337   4500 
001    AAI3261626 
005    20080423130435.5 
008    080423s2007    ||||||||||||||||| ||eng d 
020    9780549002062 
035    (UMI)AAI3261626 
040    UMI|cUMI 
100 1  Charnprasitphon, Aphiwat 
245 10 Modeling and analysis of the batch production scheduling 
       problem for perishable products with setup times 
300    222 p 
500    Source: Dissertation Abstracts International, Volume: 68-
       05, Section: B, page: 3311 
500    Advisers: Faiz Al-Khayyal; Paul M. Griffin 
502    Thesis (Ph.D.)--Georgia Institute of Technology, 2007 
520    The focus of this dissertation is problem of batch 
       production scheduling for perishable products with setup 
       times, with the main applications in answering production 
       planning problems faced by manufacturers of perishable 
       products, such as beers, vaccines and yoghurts. The 
       benefits of effective production plans can help companies 
       reduce their total costs substantially to gain competitive
       advantages without reduction of service level in a 
       globalize economy 
520    We develop concepts and methodologies that are applied to 
       two fundamental problems: (i) the batch production 
       scheduling problem for perishable products with sequence-
       independent setup times (BPP-SI) and (ii) the batch 
       production scheduling problem for perishable products with
       sequence-dependent setup times (BPP-SD) 
520    The problem is that given a set of forecast demand for 
       perishable products to be produced by a set of parallel 
       machines in single stage batch production, with each 
       product having fixed shelf-life times and each machine 
       requiring setup times before producing a batch of product,
       find the master production schedule which minimizes total 
       cost over a specified time horizon. We present the new 
       models for both problems by formulating them as a Mixed 
       Integer Program (MIP) in discrete time. Computational 
       studies on BPP-SI and BPP-SD for industrial problems are 
       presented. In order to efficiently solve the large BPP-SI 
       problems in practice, we develop five efficient 
       heuristics. The extensive computational results show that 
       the developed heuristics can obtain good solutions for 
       very large problem sizes and require a very short amount 
       of computational time 
590    School code: 0078 
590    DDC 
650  4 Engineering, Industrial 
690    0546 
710 2  Georgia Institute of Technology 
773 0  |tDissertation Abstracts International|g68-05B 
856 40 |u