Gupta, PankajMehlawat, Mukesh KumarAggarwal, UshaKhan, Ahmad Zaman...
29页
查看更多>>摘要：Embedding a culture of working with social benefits within an organization is at an all-time high these days. This paper assists in better understanding and articulating the economic, environmental, and social aspects of the sustainable four-stage supply chain. The proposed research incorporates input from all four phases of the supply chain (supplying raw materials, manufacturing, warehousing, and retailing of finished goods, and selling them to customers). Three conflicting objective functions have been developed: cost minimization, emission minimization, and social benefit maximization. A multi-objective optimization model has been formulated subject to the usual supply and demand constraints, balancing constraints, capacity constraints, and many more. The weighted sum approach has been used to solve the multi-objective optimization model. Finally, two real-life case studies to validate the proposed model have been presented: the lead-acid battery supply chain and the disposable syringe supply chain. (C) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Predictive models are increasingly being used to optimize decision-making and minimize costs. A conventional approach is predict-then-optimize: first, a predictive model is built; then, this model is used to optimize decision-making. A drawback of this approach, however, is that it only incorporates costs in the second stage. Conversely, the predict-and-optimize approach proposes learning a predictive model by directly minimizing the cost of the downstream decision-making task. This is achieved by using a task-specific loss function incorporating the costs of different outcomes in the first stage, with the eventual aim of obtaining more cost-effective decisions in the second stage. This work compares both approaches in the context of cost-sensitive classification. Conceptually, we use the two-stage framework to categorize existing cost-sensitive learning methodologies by differentiating between methodologies for cost-sensitive model training and decision-making. Empirically, we compare and evaluate both approaches using different cost-sensitive training and decision-making methodologies, as well as both class-dependent and instance-dependent cost-sensitive methods. This is achieved using real-world data from a range of application areas and a combination of cost-sensitive and cost-insensitive performance measures. The key finding is that the decision-making strategy is generally found to be more effective than training with a task-specific loss or their combination. (C) 2022 Elsevier Inc. All rights reserved.
NSTL
Elsevier