Minimization of the Number of Tool Magazine Setups on Automated Machines: A Lagrangean Decomposition Approach

This paper addresses the parts-grouping problem that arises in automated manufacturing environments where appropriate cutting tools must be loaded on Computer Numerical Control (CNC) machines to process a variety of parts. Since tool-loading times may be considerably long and reduce available machin...

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Vydané v:Operations research Ročník 51; číslo 2; s. 309 - 320
Hlavný autor: Denizel, Meltem
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Linthicum INFORMS 01.03.2003
Institute for Operations Research and the Management Sciences
Predmet:
Agile manufacturing
Analysis
Automation
Cutting tools
Datasets
Decomposition
Flexibility
Flexible manufacturing systems
Heuristics
Integer programming
Integers
Lagrangean decomposition
Machine tools
Machinery
Management science
Manufacturing
Manufacturing, automated systems: tool-loading decision
Numerical controls
Objective functions
Operations research
Optimal solutions
Packing problem
Problem sets
Production management
Production planning
Programming, integer, algorithms, relaxation: bin-packing problem with sharing
Studies
United States
ISSN:0030-364X, 1526-5463
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Shrnutí:This paper addresses the parts-grouping problem that arises in automated manufacturing environments where appropriate cutting tools must be loaded on Computer Numerical Control (CNC) machines to process a variety of parts. Since tool-loading times may be considerably long and reduce available machine processing times, it is important to find a mutually exclusive grouping of parts such that the total number of tools required by each group does not exceed the tool-magazine capacity and the number of groups is minimized. We present an integer programming formulation of the problem and develop a lower bounding procedure using Lagrangean decomposition. We then introduce valid inequalities to improve the quality of the lower bounds obtained from the relaxed problem. Our solution procedure for the lower bounding problem requires only one set of Lagrange multipliers, which reduces the required computational effort significantly. We also modify the lower bound solution heuristically to find an upper bound. The lower and upper bounds are then incorporated in a branch-and-bound scheme to search for an optimal solution. Our computational comparisons with the best existing solution procedures from the literature show that our procedure performs better on average, and for the majority of test problems within the scope of our experiments.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0030-364X
1526-5463
DOI:10.1287/opre.51.2.309.12784