Submission Deadline: 31 July 2023 (closed) View: 188
The need for advanced computational models has manifested in the fact that a large number of complex nature and society systems include many conflict parameters. To make a balance among them, it is recommended to develop and apply such advanced computational models that can balance conflicting aims where there are many variant solutions available to decision-makers. Developing advanced computational models represents a significant challenge in uncertain environments, especially when available resources, time, and funds are limited. Advanced computational models have everyday applications for decision-making about the problems of complex systems in engineering. Complex systems in engineering seek the application of scientific principles for practical objectives such as the processes, manufacture, design, operation of products, costs rationalization, performance measurement, and improving service quality while accounting for constraints involving uncertainty parameters. Managers from various engineering fields are required to make right decisions. Consequently, managers need the support of these advanced computational models to engage in effective decision-making.
This special issue aims to collect papers that develop or apply advanced computational models: optimization, machine learning, simulation, probability and statistics, decision theory, systems dynamics, and multi-criteria decision-making in the field of engineering.
The list of potential topics includes, but are not limited to:
• Applications of advanced computational models as support in creating innovation in engineering
• Integration of novel advanced computational models in engineering
• Hybrid decision analysis for complex systems in engineering
• Innovative applications of advanced computational models
• Operations research tools for decision-making in engineering
• Mathematical programming in engineering
• Multi-criteria decision-making in engineering
• Sustainable decision-making under uncertainty
• Advanced computational models for risk analysis
• Advanced computational models for solving complex logistics systems
• Advanced computational models for solving complex routing and/or scheduling systems
• Advanced computational models for the efficiency analysis and/or performance measurement
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