This article proposes a novel biogeography-based optimization (NBBO) algorithm to solve the distributed assembly permutation flow-shop scheduling problem with sequence-dependent set-up times (DAPFSP-SDST). The optimization objective of this problem is minimizing the maximum completion time (makespan). In the initialization phase, NBBO generates two kinds of feasible solutions. Secondly, the linear migration model is replaced with the sinusoidal migration model and a modified product insertion method is performed in the migration phase. Then, in the mutation phase, a job insertion method is used to adjust the processing order of jobs in each product. A local search method based on SDST is combined to jump out of local optima. Finally, simulation experiments based on 540 test instances and comparisons with seven existing algorithms as well as one simple biogeography-based optimization algorithm verify the superiority of NBBO.

He accurate forecasting of carbon dioxide (CO₂) emissions from fossil fuel energy consumption is a key requirement for making energy policy and environmental strategy. In this paper, a novel quantum harmony search (QHS) algorithm-based discounted mean square forecast error (DMSFE) combination model is proposed. In the DMSFE combination forecasting model, almost all investigations assign the discounting factor (β) arbitrarily since β varies between 0 and 1 and adopt one value for all individual models and forecasting periods. The original method doesn’t consider the influences of the individual model and the forecasting period. This work contributes by changing β from one value to a matrix taking the different model and the forecasting period into consideration and presenting a way of searching for the optimal β values by using the QHS algorithm through optimizing the mean absolute percent error (MAPE) objective function. The QHS algorithm-based optimization DMSFE combination forecasting model is established and tested by forecasting CO₂ emission of the World top‒5 CO₂ emitters. The evaluation indexes such as MAPE, root mean squared error (RMSE) and mean absolute error (MAE) are employed to test the performance of the presented approach. The empirical analyses confirm the validity of the presented method and the forecasting accuracy can be increased in a certain degree.

This article presents an enhanced iterated greedy EIG algorithm that searches both insert and swap neighbourhoods for the single-machine total weighted tardiness problem with sequence-dependent setup times. Novel elimination rules and speed-ups are proposed for the swap move to make the employment of swap neighbourhood worthwhile due to its reduced computational expense. Moreover, a perturbation operator is newly designed as a substitute for the existing destruction and construction procedures to prevent the search from being attracted to local optima. To validate the proposed algorithm, computational experiments are conducted on a benchmark set from the literature. The results show that the EIG outperforms the existing state-of-the-art algorithms for the considered problem.