Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142760
Title: Self-assembly for supply chains
Authors: Yee, Gabriel Qi Ming
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2019
Publisher: Nanyang Technological University
Source: Yee. G. Q. M. (2019). Self-assembly for supply chains. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Self-assembly is a natural construction process where components of a system spontaneously form into more complex aggregates when suitable environmental conditions are created. Self-assembly systems are remarkable in that the fabrica-tion of the complex structures are done with mechanisms that are self-reproducing and maintaining, distributed, and are not restricted to having be synchronous. From the perspective of strategy development, such bottom-up behaviours are like the real-world process of systematically identifying and studying the key issues and reasons for a problem before matching it with a strategy to solve it. In a similar fashion, the real-world processes of specifying objectives, tasks, and principles are like the specifying of environmental condi-tions when designing self-assembly systems. These two behaviours exist as two extreme ends of strategy development causing the typical academic publication on strategy development to dichotomously adopt one. As a science that can bridge both approaches, the ability to self-assembly a strategy would present a superior approach to strategy development. In this thesis, the conceptualization and implementation of an algorithm that self-assembles a strategy is presented. The algorithm is applied to a supplier se-lection problem and benchmarked as a symbolic regression solver against tradi-tional Genetic Programming across five representative problems. Finally, the thesis is concluded with statements for potential extension.
URI: https://hdl.handle.net/10356/142760
DOI: 10.32657/10356/142760
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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