A Self-Learning Discrete Artificial Bee Colony Algorithm for Energy-Efficient Distributed Heterogeneous L-R Fuzzy Welding Shop Scheduling Problem

With the tendency of decentralization into factories, production scheduling among heterogeneous factories has become a prominent concern in industrial demand response, spurring research on distributed heterogeneous welding shop scheduling problem. Moreover, owing to the inevitable occurrence of uncontrollable system disturbance in practical production environment, the processing time of jobs is uncertain rather than deterministic. Thus, an L-R fuzzy number is introduced to tackle the uncertainty of processing time. Furthermore, in the pursuit of sustainable development, energy efficiency has been a significant emphasis from countries. An effective production scheduling in distributed heterogeneous L-R fuzzy welding shop can optimize both production and energy efficiency, but no related research is reported. Thus, to address this research gap, this article investigates an energy-efficient distributed heterogeneous L-R fuzzy welding shop scheduling problem (EDHFWSP) with the objectives of minimizing makespan and total energy consumption (TEC). To solve this issue, a self-learning discrete artificial bee colony (SDABC) algorithm is proposed. First, a collaborative initialization is presented to yield excellent initial solutions. Then, a self-learning selection strategy is developed to help solutions select a superior neighborhood structure in employed bee phase. Then, a self-learning variable neighborhood search is designed to adaptively select a neighborhood structure for execution in onlooker bee phase. Furthermore, an energy-saving strategy is devised to further optimize TEC without affecting makespan. In addition, to verify the effectiveness of SDABC, extensive experiments are performed to compare SDABC with other five optimization algorithms. Experimental results validate that SDABC outperforms its competitors.