Flexible Job Scheduling under Consideration of Time and Energy Consumption Using Enhanced Iterative Deferred Acceptance Algorithm

This paper highlights the shift in the industrial sector towards a decentralized structure, focusing the importance of energy efficiency for manufacturers and the need for quick job completion to satisfy customers. The study proposes a matching game approach using the Job Scheduling Problem (JSP) to address both manufacturer and customer concerns. It introduces the Deferred Acceptance (DA) algorithm to create stable and optimal matches between machines and operations, incorporating the W-value concept to represent willingness values between partners. The Enhanced Iterative DA (EIDA) algorithm, enhanced with the W-value, shows improved job completion time, reduced energy consumption, and faster runtime compared to the Genetic Algorithm. Through experiments, our enhanced iterative DA (EIDA) algorithm results in an average 6.40% increase in job completion time and a 16.60% reduction in manufacturers’ energy consumption compared to the Genetic Algorithm. Moreover, utilizing the W-value leads to a 19.03% average runtime improvement.