ABSTRACT
Current manufacturing enterprise is facing the challenges of rapid fluctuations in demand, stringent environmental regulations, and on-time delivery of service. These challenges need to be addressed in a more effective and efficient way so that the associated environmental effects, manufacturer’s capability of producing required production, and customer’s satisfaction to receive the product in time can be fulfilled. To overcome the aforementioned challenges, research study conducted here on energy efficient Flexible Job-shop Scheduling Problem (FJSSP) is addressed. Thereafter, a multi-objective based mathematical model has been developed by considering the objectives such as minimization of total completion time of jobs, the processing cost of the operations, and energy consumption of machines. The considered problem is well-known NP-hard in nature, therefore, an evolutionary algorithm-based improvised Moth Flame Optimization (MFO) is adapted to search for the optimal/near-optimal solutions. Numerical experiments are conducted with ten different modified instances of FJSSP, and with the proposed MFO algorithm a comparison study with the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is done. Finally, from the obtained results it has been confirmed that the proposed MFO algorithm performed better in comparison to NSGA-II.
