Energy-Aware Production Planning Using Fuzzy Goal Programming Techniques: A Review
Keywords:
Energy-Aware Production Planning, Fuzzy Goal Programming, Sustainability, Hybrid Optimization, Uncertainty ModellingAbstract
With industries grappling with escalating energy expenses, environmental control requirements, and global efforts for sustainability, energy-aware production planning is a critical priority in modern manufacturing.Energy-aware production planning has become a cornerstone in today’s manufacturing landscape, driven by the industries’ need to balance the rising costs of energy, environmental regulations, and global sustainability initiatives. The review aims to gather the latest developments of fuzzy goals programming (FGP) coupled with production planning models for energy efficiency. Although traditional deterministic and classical goal programming are well-established techniques, and are used as powerful tools for decision-making, they may fail to address situations with uncertainty and imprecision encountered in actual manufacturing systems with harmful renewables such as energy costs, random machine performance or fluctuations in renewable energy. Based on fuzzy set theory, flexible goals for energy consumption, cost, time, and quality are represented by the fuzzy goal programming, thus it helps the decision makers to handle the complex trade-off between various goals. The review showcases a wide array of applications covering different manufacturing and process industries and even smart factory settings, reflecting the versatility of FGP-based models. Adding genetic algorithms, simulation models, machine learning and multi-criteria decision-making techniques to the FGP approach, extends the scalability and robustness of the solution. However, there are still some obstacles in its way, such as the requirement of more real-time optimization frameworks, standardized assessment measures, easier integration of renewables, etc. Finally, the obtained findings highlight the crucial role of fuzzy techniques and Industry 4.0 tools based on AI in the future development of energy-aware production planning. Such future developments will lead to more intelligent, resilient, and sustainable manufacturing systems that will be able to adapt dynamically to changing demands for energy and process operations
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