Resource Allocation Planning Helper (RALPH): Lessons learned

The current task of Resource Allocation Process includes the planning and apportionment of JPL's Ground Data System composed of the Deep Space Network and Mission Control and Computing Center facilities. The addition of the data driven, rule based planning system, RALPH, has expanded the planning horizon from 8 weeks to 10 years and has resulted in large labor savings. Use of the system has also resulted in important improvements in science return through enhanced resource utilization. In addition, RALPH has been instrumental in supporting rapid turn around for an increased volume of special what if studies. The status of RALPH is briefly reviewed and important lessons learned from the creation of an highly functional design team are focused on through an evolutionary design and implementation period in which an AI shell was selected, prototyped, and ultimately abandoned, and through the fundamental changes to the very process that spawned the tool kit. Principal topics include proper integration of software tools within the planning environment, transition from prototype to delivered to delivered software, changes in the planning methodology as a result of evolving software capabilities and creation of the ability to develop and process generic requirements to allow planning flexibility

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

IT & C Projects Duration Assessment Based on Audit and Software Reengineering

This paper analyses the effect of applying the core elements of software engineering and reengineering, probabilistic simulations and system development auditing to software development projects. Our main focus is reducing software development project duration. Due to the fast changing economy, the need for efficiency and productivity is greater than ever. Optimal allocation of resources has proved to be the main element contributing to an increase in efficiency.Reengineering, audit, project duration assessment, Monte Carlo simulation

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

Automated control of hierarchical systems using value-driven methods

An introduction is given to the Value-driven methodology, which has been successfully applied to solve a variety of difficult decision, control, and optimization problems. Many real-world decision processes (e.g., those encountered in scheduling, allocation, and command and control) involve a hierarchy of complex planning considerations. For such problems it is virtually impossible to define a fixed set of rules that will operate satisfactorily over the full range of probable contingencies. Decision Science Applications' value-driven methodology offers a systematic way of automating the intuitive, common-sense approach used by human planners. The inherent responsiveness of value-driven systems to user-controlled priorities makes them particularly suitable for semi-automated applications in which the user must remain in command of the systems operation. Three examples of the practical application of the approach in the automation of hierarchical decision processes are discussed: the TAC Brawler air-to-air combat simulation is a four-level computerized hierarchy; the autonomous underwater vehicle mission planning system is a three-level control system; and the Space Station Freedom electrical power control and scheduling system is designed as a two-level hierarchy. The methodology is compared with rule-based systems and with other more widely-known optimization techniques

SUSTAINABILITY STRATEGIES IN ENGINEERING INFRASTRUCTURE MAINTENANCE IN DEVELOPING COUNTRIES: SELECTED SOUTH WESTERN NIGERIA STATES CASE STUDY

It is an undeniable fact that production of maintenance- free infrastructure is not feasible. The reality is that all the elements and components that make up an engineering infrastructure unavoidably, deteriorates with time due to inherent defects in design and construction, and the effects of environmental agents and users activities. All engineering infrastructures are subject to aging, wear and tear in the performance of their functions and deterioration by exposure to outside operating environment. Hence, left to themselves, engineering infrastructures will eventually become inefficient, unreliable and fail. The issue then is how the existing infrastructure can be sustained to the extent that the functions they are designed to perform will not be compromised. To this end, this study will researched into sustainability strategies that can be adopted in engineering infrastructure maintenance. Data will be collected for purpose of extracting information on deployable strategies, including the use of Public engineering infrastructure in Southwestern part of Nigeria as case study. The study will later recommend strategies than can be adopted to aid this present generation provide solution to their environmental needs without compromise ability of future generation to meet their needs, which concept of sustainability has birthed

Intelligent perturbation algorithms to space scheduling optimization

The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed