Assembly lines are productive systems, which are very efficient for homogeneous products. In the automotive industry, an assembly line is used in the production of several vehicle variants, including numerous configurations, options, and add-ins. As a result, assembly lines must be at the same time specialized to provide high efficiency, but also flexible to allow the mass customization of the vehicles. In this book, the planning of assembly lines for uncertain demand is tackled and optimization algorithms are offered for the balancing of such lines. Building an assembly line is a commitment of several months or even years, it is understandable that the demand will fluctuate during the lifetime of an assembly line. New products are developed, others are removed from the market, and the decision of the final customer plays a role on the immediate demand. Therefore, the variation and uncertainty of the demand must be accounted for in an assembly line. In this book, methods dealing with random demand or random production sequence are presented, so that the practitioners can plan more robust and efficient production systems.
Author(s): Celso Gustavo Stall Sikora
Series: Gabler Theses
Publisher: Springer Gabler
Year: 2022
Language: English
Pages: 196
City: Wiesbaden
Acknowledgements
Abstract
Contents
Acronyms
List of Figures
List of Tables
1 Introduction
1.1 Motivation and Overview
1.2 Objectives and Document Outline
2 Design and Operation of Assembly Lines
2.1 Production and Layout Configurations
2.2 Production and Layout Configuration in the Automotive Industry
2.3 Balancing of Assembly Lines
2.4 Influence of Multiple Products
2.5 Sequencing in the Automotive Industry
2.6 Buffers
3 Literature Review on Assembly Line Balancing Under Uncertainty
3.1 Scope and Structure
3.2 Existing Surveys in the Literature
3.2.1 Balancing
3.2.2 Master Scheduling
3.2.3 Rebalancing
3.2.4 Sequence Planning or Sequencing
3.2.5 Resequencing
3.2.6 Buffer Allocation
3.3 Classification Scheme
3.4 Uncertainties in Single-model Production Systems
3.4.1 Uncertainties in the Balancing
3.4.2 Uncertainties in the Buffer Allocation
3.5 Uncertainty in Multiple-model Production Systems
3.5.1 Uncertainty in the Processing Time
3.5.2 Uncertainty of the Production Sequence
3.5.3 Uncertainty of Demand
3.6 Uncertainties of the Disassembly Process
3.7 Gaps and Contributions to the Literature
4 Balancing Under Full Sequencing Control
4.1 Problem Definition
4.2 Solution Algorithm
4.2.1 Benders' Decomposition
4.2.2 Combinatorial Benders' Decomposition
4.2.3 Proposed Algorithm
4.3 Tests and Results
4.3.1 Dataset
4.3.2 Strength of the Partial Cuts
4.3.3 Effect of The Local Search
5 Balancing Under No Sequencing Control
5.1 Problem Definition
5.2 Evaluating the Expected Utility Work
5.3 Properties of the Total Cost Function
5.4 Solution Algorithm
5.4.1 Node Enumeration Scheme
5.4.2 Length Optimization
5.4.3 Iterative Computation of Nodes
5.5 Tests and Results
5.5.1 Dataset
5.5.2 Results
6 Controlling Production Sequences Using Buffers
6.1 Problem Description
6.1.1 Problem Classification and System Description
6.1.2 Due Date Definition
6.1.3 Problem-State Definition, Transition Function, and Solution Policy
6.2 Simulation Model
6.3 Heuristic Rules
6.4 Improvement Approaches
6.4.1 Expression for the Product Selection Policy
6.4.2 Improvement Procedures
6.5 Tests and Results
6.5.1 Simulation Length and Number of Repetitions
6.5.2 Implementation Details
6.5.3 Quality of the Heuristic Rules
6.5.4 Improving Expression-Based Rules Using Local Search
6.5.5 The Lookahead Procedure
6.5.6 Summary of the Results
7 Conclusion
7.1 Summary, Objectives, and Conclusions
7.2 Limitations and Future Works
A Bibliography
