Cloud manufacturing is a novel service-oriented networked manufacturing paradigm that aims to provide on-demand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted meta-heuristic algorithm-based approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRL-based framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online single-task scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.

Currently, systematic techniques for assessing macro mechanisms for transferring software engineering technologies are non-existent. This leads to inefficient allocation of research resources and increased risk to software technology intensive programs. Consequently, software technology transition today is an ill-defined and non-repeatable, inefficient process for bringing advanced software engineering technologies to market. This paper develops two dynamical models of a software technology transition from both macroscopic and microscopic views. The models are developed utilizing information theory, communication theory, chaos control theory, and learning curve principles. The combination of those scientifically sound mechanisms provides a basis for assessing, and/or prescribing a portfolio of technologies and the implementing macro infrastructure. This provides the theoretical framework for a practical method for a program manager to establish a high capacity transition channel, which accelerates technology maturation and insertion. Experiments based on very large number of data have been performed. Data samples assess the following technologies: software engineering, software technology transfer, Ada, Java, abstract data types, rate monotonic analysis, cost models, software standards, and software work breakdown structures.

This work presents a novel approach for development of complex real-time systems, called the documentation-driven development (DDD) approach. This approach can enhance integration of computer aided software development activities, which encompass the entire life cycle. DDD will provide a mechanism to monitor and quickly respond to changes in requirements and provide a friendly communication and collaboration environment to enable different stakeholders to be easily involved in development processes and, therefore, significantly improve the agility of software development for complex real-time systems. DDD will also support automated software generation based on a computational model and some relevant techniques. DDD includes two main parts: a documentation management system (DMS) and a process measurement system (PMS). DMS will create, organize, monitor, analyze, and transform all documentation associated with the software development process. PMS will monitor the frequent changes in requirements and assess the effort and success possibility of development. A case study was conducted by a tool set that realized part of the proposed approach.