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Modularity concepts attracted the attention of many researchers as it plays an important role in product design problems. Modularity requires dividing a product into a set of modules that are independent between each other and dependent within. The product is represented using Design Structure Matrix (DSM). DSM works as a system representation tool; it visualizes the interrelationship between product elements. In this research, a comparison is conducted between four optimization algorithms: Emperor Penguins Colony (EPC), First Modified Emperor Penguins Colony (MEPC1), Second Modified Emperor Penguins Colony (MEPC2) and Cuckoo Search (CS) optimization algorithms. These four algorithms aim at finding the optimal number of clusters and the optimal assignment of components to clusters, with the objective of minimizing the total coordination cost. Experimental results show that EPC outperforms the other three algorithms. [ABSTRACT FROM AUTHOR]

Sustainability is an important consideration in product design. The sustainable design should fully consider the environmental, social, and economic factors of the product. However, the three factors are often conflicting with each other. This paper aims to strike a balance between these factors and achieve sustainable product design through multi-objective optimization. The three influencing factors of sustainability, namely, the environmental factor, social factor and economic factor, were respectively defined as environmental impact, labor time and labor cost. Then, the product to be designed was represented as a design structure matrix (DSM), a list of all product components and the dependency patterns among these components. On this basis, the non-dominated sorting and cuckoo search were combined into a multi-objective optimization technique to optimize the product functionality. This technique looks for a set of Pareto optimal solutions, each of which represents the structure of modules and the number of modules. The effectiveness of the proposed technique was verified through a case study on a coffee maker. The results show that our technique outperformed the previous optimization methods. [ABSTRACT FROM AUTHOR]