In this paper, a backward approach is proposed for maximizing net present value (NPV) in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF). The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in backward mode. For this purpose, a Genetic Algorithm is applied to solve experimental cases with 50 variables and the results are compared with forward serial programming method. The remarkable results reveal that the backward approach is an effective way to maximize NPV in MRCPSP-DC while activity splitting is allowed. The algorithm is flexible enough to be used in real project.
In real industries, managers usually consider more than one objective in scheduling process. Minimizing completion time, operational costs and average of machine loads are amongst the main concerns of managers during production scheduling in practice. The purpose of this research is to develop a new scheduling method for job-shop systems in the presence of uncertain demands while optimizing completion time, operational costs and machine load average are taken into account simultaneously. In this research a new multi-objective nonlinear mixed integer programming method is developed for job-shop scheduling in the presence of product demand uncertainty. The objectives of the proposed method are minimizing cost, production time and average of machine loads index. To solve the model, a hybrid NSGA-II and Simulated Annealing algorithms is proposed where the core of the solving algorithm is set based on weighting method. In continue a Taguchi method is set for design of experiments and also estimate the best initial parameters for small, medium and large scale case studies. Then comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approaches in terms of the quality of the solutions and the solving times. The outcomes are then compared with a classic Genetic Algorithm. The outcomes indicated that the proposed algorithm could successfully solve large-scale experiments less than 2 minutes (123 seconds) that is noticeable. While performance of the solving algorithm are taken into consideration, the proposed algorithm could improve the outcomes in a range between 9.07% and 64.96% depending on the input data. The results also showed that considering multi-objective simultaneously more reasonable results would be reached in practice. The results showed that the market demand uncertainty can significantly affect to the process of job shop scheduling and impose harms in manufacturing systems both in terms of completion time and machine load variation. Operational costs, however, did not reflect significantly to market demand changes. The algorithm is then applied for a manufacturing firm. The outcomes showed that the proposed algorithm is flexible enough to be used easily in real industries.
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 pattern in a supply chain can be vital and direct it to the Industry 5.0 goals. In this research, a 3-phased learning-based 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 1 st 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 2 nd phase, a learning 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 (0419) 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 learning-based 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.
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