Solving a multistage partial inspection problem using genetic algorithms

Traditionally, the multistage inspection problem has been formulated as consisting of a decision schedule where some manufacturing stages receive full inspection and the rest none. Dynamic programming and heuristic methods (like local search) are the most commonly used solution techniques. A highly constrained multistage inspection problem is presented where all stages must receive partial rectifying inspection and it is solved using a real-valued genetic algorithm. This solution technique can handle multiple objectives and quality constraints effectively.

[1]  Mika Vainio,et al.  Optimizing the Performance of a Robot Society in Structured Environment Through Genetic Algorithms , 1995, ECAL.

[2]  Jen Tang,et al.  Design of Screening Procedures: A Review , 1994 .

[3]  Sheng-Tsaing Tseng,et al.  Optimal sequence of partial inspections subject to errors , 1994 .

[4]  Ashok Samal,et al.  A Synthesizable VHDL Coding of a Genetic Algorithm , 1998, Practical Handbook of Genetic Algorithms.

[5]  Christopher L. Barrett,et al.  Elements of a Theory of Simulation , 1995, ECAL.

[6]  John R. Koza Hierarchical Automatic Function Definition in Genetic Programming , 1992, FOGA.

[7]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.

[8]  R. H. Myers,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[9]  S. Unnikrishnan,et al.  Planning quality inspection operations in multistage manufacturing systems with inspection errors , 1998 .

[10]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[11]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[12]  Miryam Barad,et al.  A break-even quality level approach to location of inspection stations in a multi-stage production process , 1990 .

[13]  Nirwan Ansari,et al.  Computational Intelligence for Optimization , 1996, Springer US.

[14]  Tzvi Raz,et al.  A Method for Sequencing Inspection Activities Subject to Errors , 1983 .

[15]  Christian Bierwirth,et al.  Production Scheduling and Rescheduling with Genetic Algorithms , 1999, Evolutionary Computation.

[16]  J. Alander Population Size, Building Blocks, Fitness Landscape and Genetic Algorithm Search Efficiency in Combinatorial Optimization: An Empirical Study , 2019, Practical Handbook of Genetic Algorithms.

[17]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[18]  Keith Wiley Chapter 4 Pattern Evolver An Evolutionary Algorithm that Solves the Nonintuitive Problem of Black and White Pixel Distribution to Produce Tiled Patterns that Appear Gray , 1999 .