In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in a dual resource constrained environment. The aim of this method is to find best production strategy of in-house manufacturing using worker assignment (both temporary and skilled workers) and outsourcing, while part demands are uncertain and can be varied periodically. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; a hybrid Tabu Search and Simulated Annealing algorithms and a hybrid Ant Colony Optimization and Simulated Annealing algorithms. A Taguchi method (L₂₇ orthogonal optimisation) is used to estimate parameters of the proposed method in order to solve experiments derived from the literature. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affects the part allocating which may induce workstation-load variations that yield to cell-load variation accordingly. To solve this problem, two methods are offered. The results show that promoting staff and using freezing technique are promising ways to reduce system imbalance while confronting with the mentioned condition. The outcomes also show the superiority of the proposed hybrid method in providing solutions with better quality.

Technological innovations play a crucial role in designing an effective green supply chain. However, it is crucial to know the factors influencing technological innovation in a green supply chain. Some preconceptions show that technological innovation in a business can be affected by internal and external factors, and therefore there must be correlations between such factors to flourish the technological innovation and, subsequently, the green supply chain. Besides, predicting the technological innovation level in a supply chain can be vital and direct it to the Industry 5.0 goals. In this research, a 3-phased framework will be proposed to predict the Technological Innovation Level of Green Supply Chains. The scope of this research includes Green Investment, Eco-friendly Design and Customer Collaborations. In the 1st phase of the framework, dependent and independent factors considering the scope of the Research will be determined; and then, using statistical data analysis, the weight of factors, which reflects their impact on technological innovation (dependent factor), will be determined. Then, in the 2nd phase, a comprehensive model will be developed and trained. Using the data of supply chains that were gathered in the first phase, the train and test data would be selected. In continuation, the model will be trained and its performance will be evaluated using some metrics. Then, in the last phase (phase 3), the developed model will be used to predict the technological level of supply chains. The outcomes of this research can help top managers of supply chains to predict the level of technological innovation by investing a certain budget in improving the dependent variables. The outcomes demonstrated that Customer Collaboration (0.481), Eco-friendly design (0.419) and Green Investment (0.41) have significant impacts on technological innovation improvement in the studied cases, respectively. Besides, the results showed the superiority of the K-nearest Neighbor algorithm while using the Minkowski distance method and considering 5 neighbors. The findings indicated that the proposed framework could predict Technological Innovation with 0.751 accuracies. The outcomes of this research can be helpful for industry owners to predict the expected technological innovation level of their system by investing a certain budget in green investment, eco-friendly design and customer collaborations in their enterprises.