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

Data-Driven Approach to Grade Change Scheduling Optimization in a Paper Machine

This paper proposes an efficient decision support tool for the optimal production scheduling of a variety of paper grades in a paper machine. The tool is based on a continuous-time scheduling model and generalized disjunctive programming. As the full-space scheduling model corresponds to a large-scale mixed integer linear programming model, we apply data analytics techniques to reduce the size of the decision space, which has a profound impact on the computational efficiency of the model and enables us to support the solution of large-scale problems. The data-driven model is based on an automated method of identifying the forbidden and recommended paper grade sequences, as well as the changeover durations between two paper grades. The results from a real industrial case study show that the data-driven model leads to good results in terms of both solution quality and CPU time in comparison to the full-space model.Peer reviewe

Leveraging Electronic Health Records to improve Patient Appointment Scheduling: A design-oriented Approach

As demand for healthcare services continues to increase, hospitals are under constant economic pressure to better manage patient appointments. It is common practice in clinical routine to schedule appointments based on average service times, resulting in overtime and waiting times for clinicians and patients. To address this problem, we propose a data-driven decision support system for scheduling patient appointments that accounts for variable service times. We take advantage of the growing amount of patient- and treatment-specific data collected in hospitals. Using a simulation study, we evaluate the decision support system on the practical example of a Gastroenterology facility. Our results demonstrate improved appointment scheduling efficiency compared to the approach currently in use

PREFERENCE DRIVEN UNIVERSITY COURSE SCHEDULING SYSTEM

University course planning and scheduling is the process of determining what courses to offer, how many sections are needed, determining the best term to offer each section, assigning a faculty member to instruct each section, and scheduling each section to a timeslot to avoid conflicts. The result of this task has an impact on every student and faculty member in the department. The process is typically broken down into three major phases: course offering planning, faculty assignment to planned course sections, and course scheduling into timeslots. This thesis looks at each of these phases for the Industrial and Manufacturing department and brings them together into a decision support and scheduling system. A decision support tool is created to facilitate planning of course offerings. Operations research is applied to assign sections to faculty members using a faculty preference driven integer linear programming model in order to minimize dissatisfaction in the department. Next, the faculty-section pairs are scheduled into university timeslots using a complex integer linear programming model. This scheduling model takes into consideration the faculty member time availability and preferences and general student time slot preferences as it minimizes dissatisfaction while avoiding conflicts among labs, faculty members and courses offered for each class level

Renewal theory sleep time optimisation for scheduling events in Wireless Sensor Networks

This paper addresses the problem of optimised decision making in scheduling non deterministic events for WSN nodes. Scheduling events for highly constrained WSN nodes with finite resources can significantly increase the lifetime of the network. Optimising the scheduling of events ensures that under any given constraint the network lifetime is maximised. The presented technique uses Renewal theory to formulate a stochastic decision making process. By observing network events, optimised decisions are made regarding node sleep times. This technique links the time a node spends in the sleep state to the rate of traffic throughput in the network making the process able to adapt to changes. The proposed technique also has the added advantage of using data available locally to a node thus minimising control overheads. It can be employed in both static and ad hoc networks, as well as for autonomous decision making in nodes that have to self configure. Finally, this policy driven technique exploits the heterogeneous nature of a typical WSN architecture by using less constrained nodes for formulating policies which can then be implemented in more constrained nodes. Theoretical and empirical results are presented

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure