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Title page for ETD etd-19991102-031914


Type of Document Master's Thesis
Author AKSAKALLI, VURAL ,
URN etd-19991102-031914
Title Heuristic Methods for Gang-Rip Saw Arbor Design and Scheduling
Degree Master of Science
Graduate Program Operations Research
Advisory Committee
Advisor Name Title
Yahya FATHI Chair
Matthias F. M. STALLMANN Member
Richard H. BERNHARD Member
Keywords
  • No Keywords Found
Date of Defense 1999-10-25
Availability unrestricted
Abstract

AKSAKALLI, VURAL. Heuristic Methods for Gang-Rip Saw Arbor Design and Scheduling. (Under the direction of Dr. Yahya Fathi).This research considers the problem of designing and scheduling arbors for gang-rip saw systems. Such systems are typically used within the furniture manufacturing industry for processing lumber, where lumber boards are first ripped lengthwise into strips of different widths, and then, cut to the required lengths to be used in manufacturing.A saw with multiple cutting channels is used to perform this operation. This saw has fixed blades at specific positions on a rotating shaft which rips incoming lumber boards into required finished widths. The pattern of cutting channels (i.e., the setting of the blades) along the saw shaft is referred to as an ''arbor''.A typical instance of the problem consists of (1) a set of required finished widths and their corresponding demands, (2) a frequency distribution of lumber boards in the uncut stock, (3) a shaft length, and (4) a blade width. The objective is to design a set of (one or more) arbors and the corresponding quantity of lumber to run through each arbor, such that the total amount of waste generated is minimized while the demand is satisfied.In the research, we focus on solving the problem using only one arbor. First, we discuss the computational complexity of the problem and propose a total enumeration procedure which can be used to solve relatively small instances. Then, we develop algorithms based on heuristic approaches such as local improvement procedures, simulated annealing, and genetic algorithms. Our computational experiments indicate that a local improvement procedure with two nested loops, performing local search with a different neighborhood structure within each loop, gives very high quality solutions to the problem within very short execution times.

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