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1. Production Planning using Genetic Algorithm (S. K. Kumar and M. K. Tiwari).
2. Process Planning through Ant Colony Optimization (Puneet Bhardwaj and M K. Tiwari).
3. Introducing a Hybrid Genetic Algorithm for Integration of Set Up and Process Planning (S. H. Chung and F. T. S. Chan).
4. Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimization (CPSO) Technique (Vikas Kumar and F. T. S. Chan).
5. Genetic Algorithms with Chromosome Differentiation (GACD) Based Approach for Process Plan Selection Problems (Nishikant Mishra and Vikas Kumar).
6. Operation Allocation in Flexible Manufacturing System Using Immune Algorithm (Mayank K. Pandey).
7. Tool Selection in FMS an Hybrid SA-TABU Algorithm Based Approach (Nitesh Khilwani, J. A. Harding and Nishikant Mishra).
8. Integrating AGVs and Production Planning with Memetic Particle Swarm Optimization (Sri Krishna, M. K. Tiwari and J. Harding).
9. Simulation-based Aircraft Assembly Planning Using a Self-Guided Ant Colony Algorithm (Sai Srinivas Nageshwaraniyer, Nurcin Celik, Young-Jun Son and Roberto Lu).
10. Applications of Evolutionary Computing to Additive Manufacturing (Candice Majewski).
11. Multiple Fault Diagnosis Using Psycho-Clonal Algorithms (Nagesh Shukla and Prakash).
12. Platform Formation Under Stochastic Demand (D. Ben-Arieh and A. M. Choubey).
13. A Hybrid Particle Swarm and Ant Colony Optimizer for Multi-attribute Partnership Selection in Virtual Enterprises (S. H. Niu, S. K. Ong and A. Y. C. Nee).
Posted August 14, 2011
Manufacturing is surely a most pragmatic of subjects. It by definition makes deliverables; often as quickly or as cheaply as possible. Tiwari and Harding's book is an eloquent testament to how evolutionary computing has moved from purely theoretical musings in computational biology to other sciences and now to this branch of engineering. The chapters also demonstrate how ever cheaper computing has made possible the intensive number crunching so necessary to implement the concepts.
What the chapters explain is that the complexity of the sheer number of variables and the equations of constraint present in contemporary manufacturing applications might necessitate a search through this space via the massive and repeated shufflings of test solutions that are regarded as genes. Some of you with backgrounds in computing might ask why not try the simplex algorithm? But that is for problems with linear equations, and linearity cannot always be assumed.
Chapter 2, 'Process Planning Through Ant Colony Optimisation', goes right into a problem involving the scheduling of different types of machines on a shop floor - a CNC lathe, a milling machine and forming and shaping machines. Where there are several instances of each. And each type can perform a given set of operations. This chapter is germane if you are new to evolutionary computing but have an extensive background in manufacturing because at the very least, the problem is quite understandable. The pseudocode of how the problem was tackled should also hopefully be straightforward to implement, as a good pedagogic way to get into this subject.
As an aside, the authors of chapter 2 hail from the Indian Institute of Technology. The chapter is a nice demonstration of the combination of the well known Indian expertise in computing with perhaps not as well acknowledged advanced research in manufacturing.
While chapter 2 was confined to the actual shop floor for manufacturing, chapter 4, 'Design for Supply Chain with Product Development Issues Using Cellular Particle Swarm Optimisation Technique', takes a broader look at a problem that threads through an entire supply chain. Here the issue is not how to make a single product but how to choose between 2 or maybe more product families. A merit of sticking with one product family is that the manufacturing of this involves common subsystem parts and process steps. This reuse or refactoring is highly desirable to reduce both design and manufacturing costs.
But if you really want to delve into the guts of evolutionary computing, check out chapter 5, 'Genetic Algorithms with Chromosome Differentiation Based Approach for Process Plan Selection Problems'. At a deep level, it goes into the tweaking of bits in a representation of a chromosome that encapsulates information and values of variables. The other chapters operate at a higher view, whereas this chapter makes you contemplate directly how or what to represent and how the data is mutated and shuffled down through the generations.
It should be said that not all the chapters are strictly about evolutionary computing. Chapter 7, 'Tool Selection in Flexible Manufacturing Systems: A Hybrid SA [Simulated Annealing]-Tabu Algorithm Based Approach', centres on the use of simulated annealing. The latter method arose out of condensed matter physics decades before the paradigms of evolutionary computing were applied. Yet the chapter reveals that simulated annealing is a closely allied idea and the c