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This paper and its companion (Part 2) deal with modelling and optimization of the flexible job-shop problem (FJSP). The FJSP is a generalised form of the classical job-shop problem (JSP) which allows an operation to be processed on several alternatives machines. To solve this NP-hard combinatorial problem, this paper proposes a customised Genetic Algorithm (GA) which uses an array of real numbers as chromosome representation so the proposed GA is called a real-coded GA (RCGA). The novel chromosome representation is designed to produces only feasible solutions which can be used to effectively explore the feasible search space. This first part of the papers focuses on the modelling of the problems and discusses how the novel chromosome representation can be decoded into a feasible solution. The second part will discuss genetic operators and the effectiveness of the RCGA to solve various test bed problems from literature.

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Hybrid micro and nanoparticles have become a topic of intense research in recent years. This is due to the special properties of these materials that open new avenues in advanced applications. Herein, we report a novel method for the generation of hybrid particles utilising plasma polymerization. Poly (methyl methacrylate) (PMMA) beads were first coated with a thin allylamine based plasma polymer layer. Gold nanoparticles of engineered size and surface structure were then attached in a controlled manner to the plasma polymer coated beads. To generate uniform chemistry on the outermost surface and to preserve the nanotopography, we deposited a 5-10 nm thin layer of Acpp. We demonstrated that these particles can be utilized in in vivo models to interrogate important biological phenomena. Specifically, we used them in mice to study the inflammatory and foreign body responses to surface nanotopography. The data strongly indicates that surface nanotopography and chemistry can modulate collagen production and the number of adhering immune cells. The method for generating hybrid particles reported here is solvent free and can open new opportunities in fields such as tissue engineering, drug delivery, biosensors, and regenerative medicine.

This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

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