Surprisingly Popular-Based Adaptive Memetic Algorithm for Energy-Efficient Distributed Flexible Job Shop Scheduling

With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood search. However, the local search (LS) operators are inefficient due to strong randomness; 2) the confidence-based adaptive operator selection model follows the experiences of the major crowds, which ignores the efficient operators with low weight, so it can not select the really efficient operator; 3) the previous works lack of efficient strategy to save energy; and 4) the mainstream memetic framework adopts LS to all solutions, which causes the population to converge too quickly and the diversity is extremely reduced. Thus, we propose a surprisingly popular-based adaptive MA (SPAMA) to overcome the above deficiencies. The contributions are as follows: 1) four problem-based LS operators are employed to improve the convergence; 2) a surprisingly popular degree (SPD) feedback-based self-modifying operators selection model is proposed to find the efficient operators with low weight and correct crowd decision making; 3) the full active scheduling decoding is presented to reduce the energy consumption; and 4) an elite strategy is designed to balance the resources between global and LS. In order to evaluate the effectiveness of SPAMA, it is compared with state-of-the-art algorithms on Mk and DP benchmarks. The results demonstrate the superiority of SPAMA to the state-of-art algorithms for solving EDFJSP.

Automated summary

With the development of the economy, distributed manufacturing has become the mainstream production mode. This study aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) by minimizing makespan and energy consumption. However, previous works have some gaps, such as inefficient local search operators, selection of inefficient operators, lack of efficient energy-saving strategy, and reduced diversity. To address these issues, a surprisingly popular-based adaptive MA (SPAMA) is proposed with problem-based LS operators, a self-modifying operators selection model, full active scheduling decoding, and an elite strategy. Comparison with state-of-the-art algorithms demonstrates the superiority of SPAMA in solving EDFJSP.