Journal of Computer Science

Application of Optimization Algorithms in Engineering Manufacturing

Description

Optimization algorithms, especially metaheuristics, are one of the most important emerging technologies of recent times for optimization in engineering manufacturing. Over the last years, there has been exponential growth of research activity in this field. Despite the fact that metaheuristics itself has not been precisely defined, it has become a standard term that encompasses several stochastic, population-based, and system-inspired approaches.

Metaheuristic methods use as inspiration our scientific understanding of biological, natural, or social systems, which at some level of abstraction can be represented as optimization processes. They intend to serve as general-purpose easy-to-use optimization techniques capable of reaching globally optimal or at least nearly optimal solutions. In recent years, one of the most successful approximation methods has been "nature-inspired metaheuristic algorithms" through studying the social or natural behavior of creatures. Their applications are based on continuous inventive techniques to yield appropriate and near-optimal results.

For example, Genetic Algorithm (GA) is inspired by the reproduction and evolution of human being. Particle Swarm Optimization (PSO) algorithm is inspired by the collective movement of birds as well as Ant Colony Optimization (ACO) algorithm which was designed according to the collective behavior of ants. Among the recently proposed metaheuristics, Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Runner-Root (RR) algorithms can be mentioned.

In this special issue, we plan to investigate the applicability of the novel nature-inspired metaheuristic algorithms to generate high-quality solutions for optimization problems.

Topics of interest include, but are not limited to:

1. Novel models in the field of engineering manufacturing
2. Application of optimization algorithms in supply chain design
3. Neural networks and deep learning
4. Internet of things and sustainability issues in engineering problems
5. Single-objective and multi-objective nature-based heuristics/metaheuristics
6. Exact solution methods such as Benders decomposition and Lagrangian relaxation
7. Goal Programming, ε-constraint and lexicographic methods
8. Multiple Criteria Decision Aiding (MCDA) algorithms
9. Uncertainty approaches including fuzzy theory, robust optimization, grey systems, etc.

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