Joshua Dennie

Abstract

An important aspect of any manufacturing environment is efficient job scheduling. With an increase in manufacturing facilities focused on producing goods with a cellular manufacturing approach, the need arises to schedule jobs optimally into cells at a specific time. A mathematical model has been developed to represent a standard cellular manufacturing job scheduling problem. The model incorporates important parameters of the jobs and the cells along with other system constraints. With each job and each cell having its own distinguishing parameters, the task of scheduling jobs via integer linear programming quickly becomes very difficult and time-consuming. In fact, such a job scheduling problem is of the NP-Complete complexity class. In an attempt to solve the problem within an acceptable amount of time, several heuristics have been developed to be applied to the model and examined for problems of different sizes and difficulty levels, culminating in an ultimate heuristic that can be applied to most size problems. The ultimate heuristic uses a greedy multi-phase iterative process to first assign jobs to particular cells and then to schedule the jobs within the assigned cells. The heuristic relaxes several variables and constraints along the way, while taking into account the flexibility of the different jobs and the current load of the different cells. Testing and analysis shows that when the heuristic is applied to various size job scheduling problems, the solving time is significantly decreased, while still resulting in a near optimal solution. iii

Library of Congress Subject Headings

Production scheduling--Mathematical models; Manufacturing cells

Publication Date

2006

Document Type

Thesis

Advisor

Sudit, Moises - Chair

Advisor/Committee Member

Kuhl, Michael

Comments

Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in December 2013.

Campus

RIT – Main Campus

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