Computationally efficient resource allocation for complex system reliability studies

Data collection planning is an important step in the experimental design process. The context in which we address data collection planning is that of collecting second-stage data in system reliability studies. This problem is often referred to as resource allocation. We motivate this problem by summarizing a Bayesian hierarchical model for assessing the reliability of a system and the current approach used to find an optimal resource allocation; this approach is computationally intensive, requiring repeated analyses via MCMC. We then introduce a computationally efficient approach for evaluating candidate resource allocations. Our approach can easily be combined with an optimization procedure to search for an optimal resource allocation. Specifically, we describe how genetic algorithms can be used to search for optimal resource allocations. We demonstrate the usefulness of our approach by employing genetic algorithms to search for an optimal resource allocation for collecting additional data to assess the reliability of an air-to-air heat-seeking missile and the reliability of a low-pressure coolant injection system, a safety feature in some nuclear-power boiling-water reactors

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

A design control structure for architectural firms in a highly complex and uncertain situation

A large architectural firm in a highly complex and uncertain production situation asked to improve its existing ?production control? system for design projects. To that account a research and design project of nine months at the spot was defined. The production control in the organization was based on a mix of project management tools, resource allocation to whole projects, and regular updating of the project portfolio. The results of the research analyses showed that the situation of the firm?s design projects cannot be controlled with only such tools, due to lack of coherence between the ?production control? and the design situation. To improve the coherence, a basic ?production control? structure is designed. The design of this structure is based on the match between the research findings and theoretical principles of how decisions should be made in multi-project situations that are highly complex and uncertain. The design consists of four hierarchical planning functions (strategic resource planning, rough cut capacity planning, resource constraint project scheduling and detailed planning). After finalizing and presenting the design, the design led to new insights into resource allocation for the client and has been approved by the client and office management. The implementation of the design is however still in design proposition due to other priorities

Human-Automation Collaboration in Complex Multivariate Resource Allocation Decision Support Systems

In resource allocation problems for systems with moving planning horizons and significant uncertainty, typical of supervisory control environments, it is critical that some balance of human-automation collaboration be achieved. These systems typically require leveraging the computational power of automation, as well as the experience and judgment of human decision makers. Human-automation collaboration can occur through degrees of collaboration from automation-centric to human-centric, and such collaboration is inherently distinct from previously-discussed levels of automation. In the context of a command and control mission planning task, we show that across a number of metrics, there is no clear dominant human-automation collaboration scheme for resource allocation problems using three distinct instantiations of human-automation collaboration. Rather, the ultimate selection for the best resource allocation decision support system will depend on a cost-benefit approach that could include mitigation of workload, conformance to intended design characteristics, as well as the need to maximize overall mission performance

Agent-Based Distributed Resource Allocation in Continuous Dynamic Systems

Intelligent agents and multiagent systems reveal new strategies to design highly flexible automation systems. There are first promising industrial applications of multiagent systems for the control of manufacturing, logistics, traffic or multi-robot systems. One reason for the success of most of these applications is their nature as some form of a distributed resource allocation problem which can be addressed very well by multiagent systems. Resource allocation problems solved by agents can be further categorized into static or dynamic problems. In static problems, the allocations do not depend on time and many resource allocation problem of practical interest can be solved using these static considerations, even in discrete-event systems like manufacturing or logistic systems. However, problems especially in highly dynamic environments cannot be addressed by this pure static approach since the allocations, i.e. the decision variables, depend on time and previous states of the considered system. These problems are hardly considered in the relevant agent literature and if, most often only discrete-event systems are considered. This work focuses on agent-based distributed dynamic resource allocation problems especially in continuous production systems or other continuous systems. Based on the current states of the distributed dynamic system, continuous-time allocation trajectories must be computed in real-time. Designing multiagent systems for distributed resource allocation mainly comprises the design of the local capabilities of the single agents and the interaction mechanisms that makes them find the best or at least a feasible allocation without any central control. In this work, the agents are designed as two-level entities: while the low-level functions are responsible for the real-time allocation of the resources in the form of closed-loop feedback control, the high-level functionalities realize the deliberative capabilities such as long-term planning and negotiation of the resource allocations. Herein, the resource allocation problem is considered as a distributed optimization problem under certain constraints. The agents play the role of local optimizers which then have to coordinate their local solutions to an overall consistent solution. It is shown in this contribution that the described approach can be interpreted as a market-based allocation scheme based on balancing of supply and demand of the resources using a virtual price. However, the agents calculate and negotiate complete supply and demand trajectories using model-based predictions which also leads to the calculation of a price trajectory. This novel approach does not only consider the dynamic behaviour of the distributed system but also combines control tasks and resource allocation in a very consistent way. The approach is demonstrated using two practical applications: a heating system and an industrial sugar extraction process

Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.Comment: 5 pages, 1 figur

Optimization and Challenges of Implementing Academic Service Management Information Systems in Muhammadiyah Higher Education: A Multiple-Case Study

Purpose – This research aims to analyze the implementation of the Academic Services Management Information System (MIS) at Muhammadiyah Higher Education Institutions (PTM) in Cirebon and Bandung, evaluate its effectiveness in improving the quality of learning, and identify and address challenges in its implementation. Design/methods/approach – The study employs a qualitative approach within an interpretive paradigm, with a multiple-case study design at PTM Cirebon and Bandung during 2022. Primary and secondary data were collected through interviews, observations, and documentation, followed by data analysis, including information reduction and data validation. Findings – The results indicate that the Academic Services MIS at PTM has not been optimally implemented, particularly in planning and information distribution. The main challenges include limited information access and lack of effective coordination. However, the universities have taken strategic steps to address these issues, including efficient budget allocation and utilizing MIS as a learning tool. Research implications – These findings provide important insights for higher education managers in optimizing the use of Academic Services MIS. Implications include enhanced resource allocation, human resources training, and improved coordination and communication systems. The study also suggests the need for more inclusive policy strategies to address resource disparities between institutions

Key success factors analysis for improving cost performance of green retrofit infrastructure on the jetty project

Several rating definitions must be met following the envision's system. The envisioned system aims to develop the green building concept in the existing jetty building. These definitions are quality of life, leadership, resource allocation, nature, climate, and resilience. This sustainability is needed to initiate changes in the planning, design, and provision of sustainable infrastructure together with the company. This also applies to implementing long-term infrastructure investments that are more cost-effective, resource-efficient, and adaptable. The study uses a qualitative and quantitative method, where data is obtained by distributing questionnaires and simulating using Statistical Products and Solution Services (SPSS). The application of Value Engineering (VE) and Life Cycle Cost Analysis (LCCA) has been chosen by researchers on existing jetty buildings with the green jetty concept, with investment costs in economic green jetty buildings and a return on investment costs of less than four years. In achieving the ten most influential factors in improving cost performance in sustainable dock construction, the results of the SPSS simulation processing obtained the ten most influential factors, namely: Planning, Energy, Siting, Materials, Ecology, Community, Economy, Operation, and Maintenance Cost, Follow-up Inspection, and Labor Experience

Key success factors analysis for improving cost performance of green retrofit infrastructure on the jetty project

Several rating definitions must be met following the envision's system. The envisioned system aims to develop the green building concept in the existing jetty building. These definitions are quality of life, leadership, resource allocation, nature, climate, and resilience. This sustainability is needed to initiate changes in the planning, design, and provision of sustainable infrastructure together with the company. This also applies to implementing long-term infrastructure investments that are more cost-effective, resource-efficient, and adaptable. The study uses a qualitative and quantitative method, where data is obtained by distributing questionnaires and simulating using Statistical Products and Solution Services (SPSS). The application of Value Engineering (VE) and Life Cycle Cost Analysis (LCCA) has been chosen by researchers on existing jetty buildings with the green jetty concept, with investment costs in economic green jetty buildings and a return on investment costs of less than four years. In achieving the ten most influential factors in improving cost performance in sustainable dock construction, the results of the SPSS simulation processing obtained the ten most influential factors, namely: Planning, Energy, Siting, Materials, Ecology, Community, Economy, Operation, and Maintenance Cost, Follow-up Inspection, and Labor Experience

AN INTELLIGENT DECISION SUPPORT SYSTEM FOR HORTICULTUE

 The objective of this research was to develop an intelligent decision support system for optimization of horticulture supply chain model using genetic algorithms. The case study was conducted at PT. Saung Mirwan, Megamendung-Bogor, a major producer of packed fresh vegetable and fresh-cut vegetable. The output of this research is an Intelligent Decision Support System of Supply Chain Management for Horticulture Agro industry (IDSS-SCM). IDSS-SCM consists of eight models: Products Demand Forecast, Vegetables Supply Forecast, Planting Schedule, Aggregate Planning, Material Requirements Planning I, Material Requirements Planning II, Inventory Management, and Transportation Route. Based on the most recent data collected, IDSS-SCM predicts that product demand will increase and it then gives optimum recommendations to the user such as plant schedule, material requirements planning, inventory, human resource allocation, and distribution route to fulfil the demand. The unique feature of this research was that a genetic algorithm (GA) with Partially Matched Crossover (PMX) operator was used to find the shortest distribution route as well as to optimize human resource allocation problem. The experiment results indicate that the GA developed in this research can solve a complex agroindustrial supply chain design problem faster and more efficiently. Keywords: intelligent decision support system, genetic algorithms, supply chain management, agroindustry, partially matched crossover