Energy-conscious Flexible Job Shop Scheduling Using Metaheuristic Algorithms

Within the landscape of manufacturing optimization, this research grapples with the intricate Flexible Job Shop Scheduling Problem (FJSP), particularly focusing on energy-conscious practices. In the production management context, FJSP is to specify the task allocation to machines and determine the relavant task sequences while energy-saving perspective is to handle the green-oriented concerns. Such a setting will result in an NP-hard problem. To this end, the study employs Genetic Algorithm (GA) and Simulated Annealing (SA) as metaheuristic tools to address the FJSP's challenges, emphasizing sustainability in industrial scheduling. Traditional models have often overlooked energy considerations, but in response to the growing need for environmentally friendly practices, this research explores avenues for achieving near-optimal solutions in the complex industrial scheduling domain. It contributes to the advancement of scheduling techniques in complex industrial settings. To capture the underlying uncertainty of the given domain, the energy consumption of machines is computed under a fuzzy modeling formulation.

Keywords:

Flexible Job Shop Problem (FJSP) , sequencing , Fuzzy Problems , meta-heuristic algorithms