Building on the preceding studies, this paper aims to extend the latest capability maturity model integration (CMMI)-based organization assessment model to the fuzzy environment. Our approach has overcome the limitations of the preceding CMMI-based models. Our proposed model is based on the multi-attribute decision-making (MADM) approach incorporating the capability of group decision making. The rating of qualitative factors based on crisp values may be insufficient to model the real-world MADM industrial problem. For controlling human subjective vagueness, linguistic variables are translated using the triangular fuzzy number. The proposed model is generalized in order to be easy to adopt by other organizational assessment practitioner and researcher. Other researcher and practitioner can adopt the proposed model procedure and methodology in order to develop their own organizational assessment, capability improvement, and decision-making framework for companies, enterprises, or organization. The proposed model has two working parts. The ranking part of the framework model can be used for ranking the importance of influential factors, while the assessment part of the model can be used as an assessment tool in the SDO organization. Collectively, it might be utilized as a decision support system. A running numerical example of software outsourcing partnership (SOP) formation is presented to validate the proposed model. The ranking part is demonstrated with the help of empirical survey conducted with 35 experts, while the assessment part is demonstrated by conducting two case studies in SDO organization. SDO vendor organization can benefit from the model to gauge their capability toward SOP formation.

The imperative for swift and intelligent decision making in production scheduling has intensified in recent years. Deep reinforcement learning, akin to human cognitive processes, has heralded advancements in complex decision making and has found applicability in the production scheduling domain. Yet, its deployment in industrial settings is marred by large state spaces, protracted training times, and challenging convergence, necessitating a more efficacious approach. Addressing these concerns, this paper introduces an innovative, accelerated deep reinforcement learning framework—VSCS (Variational Autoencoder for State Compression in Soft Actor–Critic). The framework adeptly employs a variational autoencoder (VAE) to condense the expansive high-dimensional state space into a tractable low-dimensional feature space, subsequently leveraging these features to refine policy learning and augment the policy network’s performance and training efficacy. Furthermore, a novel methodology to ascertain the optimal dimensionality of these low-dimensional features is presented, integrating feature reconstruction similarity with visual analysis to facilitate informed dimensionality selection. This approach, rigorously validated within the realm of crude oil scheduling, demonstrates significant improvements over traditional methods. Notably, the convergence rate of the proposed VSCS method shows a remarkable increase of $77.5\%$, coupled with an $89.3\%$ enhancement in the reward and punishment values. Furthermore, this method substantiates the robustness and appropriateness of the chosen feature dimensions.