In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.
A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem
