Corporate Social Responsibility (CSR) has been an articulated practice for over 7 decades. Still, most corporations lack an integrated framework to develop a strategic, balanced, and effective approach to achieving excellence in CSR. Considering the world’s critical situation during the COVID-19 pandemic, such a framework is even more crucial now. We suggest subsuming CRS categories under Sustainable Development Goals (SDGs) be used and that they subsume CSR categories since SDGs are a comprehensive agenda designed for the whole planet. This study presents a new CSR drivers model and a novel comprehensive CSR model. Then, it highlights the advantages of integrating CSR and SDGs in a new framework. The proposed framework benefits from both CSR and SDGs, addresses current and future needs, and offers a better roadmap with more measurable outcomes.

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost.

This paper offers a framework for analysis to benefit buying firms as they evaluate current and prospective suppliers, and to assist supplying organisations in becoming more competitive. It explores the notion of performance improvement frontiers for suppliers, in the context of developing suppliers rather than rationalising or pruning them. Dual-efficiency (strengths and weaknesses) frontiers are constructed using inverted efficiency techniques. Unilateral and bilateral approaches to the construction of these frontiers are examined. It is found that certain information content of bilaterally determined DEA assurance ranges can serve as a compromise between the buyer’s ideal performance priorities and a supplier’s capability-based priorities. For this reason, it represents a reasonable and jointly determined set of performance expectations for buyers to recommend to the supplier set. For the suppliers themselves, the bilateral ranges contribute a prioritised behavioural focus to develop or improve their capabilities on specific performance attributes.