• Effective Resource Management in Manufacturing Systems Optimization Algorithms for Production Planning

Effective Resource Management in Manufacturing Systems Optimization Algorithms for Production Planning

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Overview

Manufacturing Systems: Trends, Classification, and Behavior Patterns ... 1 1.1 Distributed Flexible Manufacturing Systems ... 2 1.1.1 DFMS Properties ... 4 1.1.2 DFMS Behaviour ... 7 1.1.3 Organizational Paradigms ... 8 1.1.4 Example of the Implementation of a Holonic Manufacturing System in Induction Motor Production . 10 1.1.5 A Layered Approach to DFMS Modeling ... 13 1.2 Manufacturing Control (MC) ... 14 1.2.1 Definition of Manufacturing Control ... 14 1.2.2 Manufacturing Control Functions ... 16 1.2.3 Classification of Production Scheduling ... 18 1.3 Scheduling and Resource Allocation ... 23 1.3.1 Definition ... 23 1.3.2 Mathematical Model for Job Shop Scheduling ... 24 1.4 On-line Manufacturing Control ... 27 1.4.1 Computational Complexity of Scheduling and Resource Allocation ... 28 1.5 Algorithmic Approach to Problem Solution ... 28 1.5.1 Greedy Heuristics ... 29 1.5.2 Local Search Heuristics ... 30 1.5.3 Off-line and On-line Algorithms ... 32 1.6 Conclusions ... 33 2 On-Line Load Balancing ... 35 2.1 Problem Definition . ... 35 2.2 Known Results and Existing Approaches ... 36 2.2.1 The Greedy Approach ... 37 2.2.2 The Robin-Hood Algorithm. ... 39 2.2.3 Tasks with Known Duration: the Assignl Algorithm ... 41 2.3 A Metaheuristic Approach ... 43 2.4 Example ... 48 2.5 Experimental Results ... 53 2.5.1 A Multi-objective Approach in the Case of Known Task Departure Dates ... 57 2.6 Conclusions ... 62 3 Resource Levelling ... 65 3.1 Background and Problem Definition ... 65 3.2 Resource Levelling and the Minimization of the Peak and the Makespan ... . 68 3.3 The Greedy Approach ... 75 3.4 The Metaheuristic Approach ... 78 3.4.1 Conceptual Comparison with Known Local Search Methods ... 79 3.4.2 How to Control the Effect of the Minimization of the Makespan and the Frequency Based Memory ... 82 3.5 Experimental Results . ... 84 3.5.1 Description of the Experiments. ... 84 3.5.2 Analysis of the Results ... 85 3.5.3 Lower Bounds Comparison ... 87 3.5.4 Comparison with Known Algorithms ... 90 3.6 The Extension to the Case with Arbitrary Integer Duration ... 93 3.7 Case Study 1 ... 95 3.8 Case Study 2 ... 100 3.9 Conclusions ... 103 4 Scheduling Jobs in Robotized Cells with Multiple Shared Resources ... 105 4.1 Background and Problem Definition ... 105 4.2 Problem Definition . ... 107 4.3 AP-Completeness Result ... 109 4.4 The Proposed Heuristic ... 110 4.5 Computational Results ... 114 4.6 Conclusions ... 120 5 Tool Management on Flexible Machines ... 121 5.1 Background ... 121 5.1.1 Definition of the Generic Instance ... 126 5.1.2 Assumptions ... 126 5.2 The Binary Clustering and the KTNS Approaches ... 127 5.3 The Proposed Algorithms ... 130 5.3.1 Algorithm 1 ... 130 5.3.2 Algorithm 2 ... 135 5.3.3 Algorithm 3 ... 141 5.3.4 Algorithm 4 ... 148 5.4 Computational Analysis ... 153 5.4.1 Comparison with Tang and Denardo ... 153 5.4.2 Comparison with Crama et al. ... 155 5.4.3 Comparison Among the Proposed Algorithms ... 163 5.5 Conclusions ... 168.

Product Details

ISBN-13: 9781846280054
ISBN-10: 1846280052
Publisher: Springer Science & Business Media
Publication date: 2006-01-09
Edition description: 2006
Pages: 216
Product dimensions: Height: 9.21 Inches, Length: 6.14 Inches, Weight: 1.13978989454 Pounds, Width: 0.56 Inches
Author: Massimiliano Caramia, Paolo Dell'Olmo
Language: en
Binding: Hardcover

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