Bioinspired computation is an umbrella term for different computational technologies that are based on principles or models of biological systems. This class of approaches, including evolutionary algorithm, swarm intelligence, and artificial immune system, complements traditional ones in the sense that the former can be applied to large and complex combination optimization problems, but the latter encounters difficulties. Therefore, bioinspired technologies are becoming important in the face of solving discrete and dynamic problems.

Recently, the bioinspired computation has attracted much attention of researchers and has been also widely applied to operation management fields ranging from production assembling, inventory control, project scheduling, human resource management, revenue management, and so forth. However, due to complexity and uncertainty in operation management problems, it is very difficult to find out the optimum solution under the limited resources, time and money in real-world applications, using the bioinspired technologies. Therefore, it is necessary to develop efficient or improved algorithms to solve operation management problems.

The aim of this special issue is to present the original research and review articles on the latest theoretical and practical achievements that will contribute to the field of bioinspired computation and its applications in operation management, in all branches of management science and computer science. Potential topics include, but are not limited to:

• Benchmarking and evaluation of new bioinspired algorithms
• Comparative theoretical and empirical studies on bioinspired algorithms, such as evolutionary algorithm, swarm intelligence, and artificial immune system
• New bioinspired methodology analysis tools, such as rough sets and stochastic process
• Bioinspired algorithms for discrete/dynamic operation management problems, such as production assembling, inventory control, project scheduling, human resource management, and revenue management
• Problems related to design, evaluation, and analysis of bioinspired algorithms in operation management

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