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Optimal lot streaming and scheduling of multiple jobs in two-machine no-wait flow shops

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Optimal lot streaming and scheduling of multiple jobs in two-machine no-wait flow shops

Li, Ming (2002) Optimal lot streaming and scheduling of multiple jobs in two-machine no-wait flow shops. Masters thesis, Concordia University.

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Abstract

In this thesis, a heuristic method is developed to optimize the sublots sizes of a number of jobs and to sequence these jobs simultaneously in two-machine no-wait flow shops. The objective is to minimize the production cost. A no-wait manufacturing system is a production environment in which each job must be processed from start to finish, without any interruption either on or between machines. Such situations can be found in many manufacturing systems, as in iron and steel production as well as in the process of anodizing products and components. In a manufacturing flow shop, there normally are several jobs with multiple identical items to be processed by a set of machines. Lot streaming is to create sublots so that machine operations can be overlapped. This thesis work devotes to the development of a heuristic method to find optimal discrete-sized sublots for each job and sequence multiple jobs efficiently in a two-machine no-wait flow shop. Simulated annealing based heuristic search is used to search for a global optimal solution of the problem. The heuristic method is extended to solve three-machine flow shop problems. The efficiency and the effectiveness of this method are illustrated using several numerical examples.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Mechanical and Industrial Engineering
Item Type:Thesis (Masters)
Authors:Li, Ming
Pagination:xii, 114 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.A.Sc.)
Program:Mechanical and Industrial Engineering
Date:2002
Thesis Supervisor(s):Chen, Mingyuan
ID Code:1643
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:21
Last Modified:08 Dec 2010 10:21
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