While optimization can play a lead role in increasing the efficiency of production planning (necessary for automated manufacturing systems), it can also be used to optimize the products themselves.

Let us assume that a company sells a certain product, be it an iron work piece or an optical system. The features of this product that are relevant for production could comprise production or material costs as well as quality metrics, such as stability or an alignment error measures. Assume further that we are able to compute such features from the building plan or design of such a work piece, either directly by using equations, via simulations, or by using some dedicated design software. In this scenario, the design parameters of the work piece, e.g., the shape, form, location, and number of lenses of an optical system or the shape and components of the alloy used to make a metal work piece can be subject to optimization: An optimization procedure can start with an existing design and step-by-step modify its parameters in order to improve the desired metrics (quality, costs, etc). This process can even incorporate measures of production uncertainties or design robustness.

As a result, cheaper and better products can be designed.

The ability to optimize product features can be another integral component for on-demand and highly-customizable production, as required by new concepts such as Industry 4.0 and Made in China 2025 [中国制造2025] and may directly be integrated with production planning and logistics.

1. Publications

  • Zhen Liu, Wei-Feng Yang, Qing-Hao Ye, Jin Hong, Guan-Yuan Gong, and Xiao-Bing Zheng. Alignment Error Analysis of the Snapshot Imaging Polarimeter. Applied Optics 55(8):1934-1940, March 2016
    doi:10.1364/AO.55.001934
    Indexing: EI:20161502207644, WOS:000371779900019, SCI

  • Guan-Yuan Gong, Xiao-Bing Sun, Wei-Feng Yang, and Zhen Liu. Research on the Data Processing Method for the Spatially Modulated Imaging Polarimeter. In Chunhua Shen, Weiping Yang, and Honghai Liu, editors, Proceedings of the Applied Optics and Photonics China (AOPC'15), May 5-7, 2015, Beijing, China, appeared in the Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) 9675; 967529 (October 8, 2015)
    doi: 10.1117/12.2199896
    Indexing: EI:20161602266730, WOS:000363280200081

  • Bin Li, Yu Wang, Thomas Weise, and Long Long. Fixed-Point Digital IIR Filter Design using Two-Stage Ensemble Evolutionary Algorithm. Applied Soft Computing, 13(1):329-338, January 2013.
    doi:10.1016/j.asoc.2012.09.004
    Indexing: EI:20124815716038, WOS:000311506900029, SCI

  • Alexandre Devert, Thomas Weise, and Ke Tang. A Study on Scalable Representations for Evolutionary Optimization of Ground Structures. Evolutionary Computation, 20(3):453-472, Fall 2012.
    doi:10.1162/EVCO_a_00054 / pdf
    Indexing: EI:20130916069460, WOS:000306767200005, SCI

  • Raymond Chiong, Thomas Weise, and Zbigniew Michalewicz, editors. Variants of Evolutionary Algorithms for Real-World Applications. ISBN: 978-3-642-23423-1, Berlin/Heidelberg: Springer-Verlag, 2012.
    doi:10.1007/978-3-642-23424-8
    Indexing: EI:20172603850806

  • Yu Wang, Bin Li, Thomas Weise, Jianyu Wang, Bo Yuan, and Qiongjie Tian. Self-Adaptive Learning Based Particle Swarm Optimization. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal, 181(20):4515-4538, October 2011.
    doi:10.1016/j.ins.2010.07.013 / pdf
    Indexing: EI:20113014172389, WOS:000293548900010, SCI, Google Scholar

  • Yu Wang, Bin Li, and Thomas Weise. Estimation of Distribution and Differential Evolution Cooperation for Large Scale Economic Load Dispatch Optimization of Power Systems. Information Sciences – Informatics and Computer Science Intelligent Systems Applications: An International Journal, 180(12):2405-2420, June 2010.
    doi:10.1016/j.ins.2010.02.015
    Indexing: EI:20101412821019, WOS:000277471400005, SCI, Google Scholar

2. Lectures and Courses