A Decision Support System for Effective Scheduling in an F-16 Pilot Training Squadron

Scheduling of flights for a flight training squadron involves the coordination of time and resources in a dynamic environment. The generation of a daily flight schedule (DFS) requires the proper coordination of resources within established time windows. This research provides a decision support tool to assist in the generation of the DFS. Three different priority rules are investigated for determining an initial ordering of flights and a shifting bottleneck heuristic is used to establish a candidate DFS. A user interface allows a scheduler to interact with the decision support tool during the DFS generation process. Furthermore, the decision support tool provides the capability to produce a weekly schedule for short-term planning purposes as well as the entire flight training program schedule for long- term planning purposes

Decision support tool for dynamic workforce scheduling in manufacturing environments \

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 113-116).Scheduling for production in manufacturing environments requires an immense amount of planning. A large number of factors such as part availability, production cost, space constraints and labor supply must be taken into account. Considering these factors, tasks are scheduled into shifts and allocated the required human resources. However, when actual production begins, the original schedule must be updated regularly due to the dynamic nature of the environment. An enormous challenge in these rapidly changing environments is the reallocation of workers to tasks in real-time due to events such as worker absences, emergent tasks and unanticipated delays. The focus of this thesis is the development of a decision support tool that can assist shift supervisors to rapidly generate new worker-task assignments during a shift to ensure that production stays on track. This research discusses the systems engineering development process of the aforementioned decision support tool including the initial planning and analysis, the interface design, and the resource allocation algorithm. The development process was iterative, with evaluations and feedback at every step facilitating the refinement of the tool. Emphasis was laid on creating a collaborative framework between the human operator and the automated planning algorithm. While automated planning algorithms are a critical component of resource allocation systems since they can solve complex multivariate scheduling problems much faster than humans, they are inherently brittle and unable to respond to uncertainties in dynamic environments. Thus, in this system, the human operator is given high-level planning tasks and the ability to set goals, while the automation handles the creation of the detailed planning and scheduling assignments. Another factor that was stressed was the inclusion of ergonomic risk. Worker-task assignments that do not take into account ergonomic risk exposure can lead to repetitive stress injuries over time, causing manufacturing plants to incur substantial medical expenses. Any system that allocates (or re-allocates) workers to tasks must take into account the ergonomic risk that workers are subjected to due to the tasks they perform in the given shift. The system was evaluated through extensive interactions with individuals from an aircraft production line, including senior level management and representative users from the production floor. The evaluations yielded positive results. Both the management and the representative users were able to identify the applicability of the tool immediately, and all individuals agreed that the system could be very useful in real production environments. The shift supervisors from the shop floor affirmed that the tool captured all major pieces of information they consider while making re-planning decisions. To better assess the potential of the tool and to refine it further, future research should initiate pilot studies to compare the proposed tool with current methods used for decision-making, which are paper schedules and best judgment of human operators.by Radhika Malik.M. Eng

Optimization of traffic light control system of an intersection using fuzzy inference system

This paper considers an automated static road traffic control system of an intersection for the purpose of minimizing the effects of traffic jam and hence its attendant consequences such as prolonged waiting time, emission of toxic hydrocarbons from automobiles, etc. Using real-time road traffic data, a dynamic round-robin allocation of right-of-way to road users based on fuzzy inference system (FIS) was implemented as a decision support tool. The static phase scheduling algorithm for traffic light systems was used as a benchmark to measure the performance of our technique which is based on dynamic phase scheduling algorithm. The performance comparison records a significant improvement of about 65.35% in average waiting time. This clearly demonstrates the efficacy and potential of our solution strategy to address the traffic scheduling problem.Keywords: Fuzzy Logic; Traffic Control Systems; Dynamic Phase Scheduling; Static Phase Scheduling, Fuzzy Set

Strategic Planning Tool Development using Portfolio Decision Analysis

A portfolio decision analysis strategic planning tool was developed for the Facilities Management Office at the University of Arkansas. The tool provides information to support budget allocation decisions based on their Strategic Planning Project List, project attributes (e.g, seat utilization, scheduling preferences, and sustainability rank), and budget constraints. The projects are evaluated using multiobjective decision analysis. We introduce dynamic value functions, which vary the range of the value measures based on the planning horizon, to evaluate the projects). We determine facilities portfolios based on the project values and constraints using Linear Programming. In addition, insightful reports are generated, which provide the stakeholders and decision makers visibility in the data trends that might affect the budget allocation in the future (e.g., student enrollment, fees increase)

A dynamic multi-criteria decision-making model for the maintenance planning of reinforced concrete structures

Decision-making is essential in buildings management process playing a decisive role in the maintenance planning design. Multi-Criteria Decision Making (MCDM) methods can be applied as a support tool to fulfil a set of requirements that arise during the scheduling of maintenance activities of these structures. The Analytic Hierarchy Process (AHP) is a broadly recognised methodology applied to model subjectively decision problems based on multi attributes analysis. This paper applies the AHP method under an objective approach, where the weight assignments are stochastically calculated instead of defining it based on the judgement of experts. The main objective of this study is to propose a dynamic decision model based on AHP for the maintenance planning of reinforced concrete structures under corrosion risk. This methodology provides the best maintenance alternative (inspection/repair) to be performed in these structures for a given intervention time. The best solution for the intervention is chosen regarding the Global Priority Vector of the final pairwise comparison matrix. After an illustrative application, the new dynamic decision model developed proven be a helpful tool for decisions-making regarding the most suitable intervention alternative within the maintenance planning of these structures.publishe

Hybrid Fuzzy-Bayesian Dynamic Decision Support Tool for Resource-Based Scheduling of Construction Projects

Title from PDF of title page viewed September 7, 2017Dissertation advisor: Ceki HalmenVitaIncludes bibliographical references (pages 153-165)Thesis (Ph.D.)--School of Computing and Engineering and Bloch School of Management. University of Missouri--Kansas City, 2017This dissertation proposes a flexible and intelligent decision support tool for scheduling and resource allocation of construction projects. A hybrid Fuzzy-Bayesian scheduling network and a new optimization model and solution approach have been developed to assess the combinatory effect of different risk factors on scheduling and optimize the time-cost tradeoff. Developed decision support tool employs interval-valued fuzzy numbers and Bayesian networks to dynamically quantify uncertainty and predict project performance during its make span. Using interval-valued fuzzy numbers makes the model more flexible and intelligent comparing to conventional fuzzy risk assessment models through incorporating the decision makers` confidence degree. The linguistic assessments of experts regarding the likelihood and severity of increase or decrease in task duration and cost when influenced by different risk factors are used to generate a set of duration and cost prior-probability distributions. A learning dynamic Bayesian scheduling network is developed to probabilistically combine the prior-probability distributions with initial activity duration estimates and update them as new evidence in form of actual activity data feed into the network. This model also predicts project performance at any point of time during its execution. Optimization model explicitly considers variation of time-cost tradeoff relationship during project execution and complex payment terms to maximize the project net present value (NPV). A sequential solution approach is proposed to combine a procedure for updating time-cost tradeoff data, and mixed integer linear programming (MILP) methods to obtain optimal project crashing and scheduling solutions that is adaptive to the current project status and crew productivity. Capability of proposed model in quantifying uncertainty at initial phases of project where project performance data are scarce, learning from data and predicting project performance, considering financial aspects of scheduling through optimal resource allocation and providing useful and clear advice to managers are advantages of developed decision support tool over already existing approaches.Introduction -- Literature review -- Methodology -- Case study and model validation -- Conclusion and recommendations -- Appendix. Detailed Fuzzy Weighted Average Calculations for a-cut = 0 Based on the Max-Min Paired Elimination Algorit

A Comprehensive Simulator for Hydropower Investment Decisions

Due to a higher share of power production from renewable sources with high short-term variation, hydro systems must more often operate closer to their components' physical limits. To simulate system behaviour, a hydropower system simulator must therefore include most physical details. We present a simulator for hydropower investment analysis that combines a medium-term production planning model based on stochastic dual dynamic programming principles with a detailed and deterministic short-term hydro scheduling model. To reduce computation times, the system description for the short-term model may include only a snipped subset of the plants and reservoirs without deteriorating the results. The simulator is verified in a case study where an investment decision has been analysed for a Norwegian hydropower producer. The combination of medium-term optimization and short-term, detailed simulation is a useful decision support tool and provides both economic results and detailed physical information about the system behaviour.A Comprehensive Simulator for Hydropower Investment DecisionsacceptedVersio

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

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Dynamic scheduling: integrating schedule risk analysis with earned value management

The topic of this paper is dynamic project scheduling to illustrate that project scheduling is a dynamic process that involves a continuous stream of changes and is a never ending process to support decisions that need to be made along the life of the project. The focus of this paper lies on three crucial dimensions of dynamic scheduling which can be briefly outlined along the following lines: (i) Baseline scheduling to construct a timetable that provides a start and end date for each project activity, taking activity relations, resource constraints and other project characteristics into account, and aiming to reach a certain scheduling objective, (ii) risk analysis to analyze the strengths and weaknesses of your project schedule in order to obtain information about the schedule sensitivity and the possible changes that undoubtedly occur during project progress and (iii) project control to measure the (time and cost) performance of a project during its progress and use the information obtained during the scheduling and risk analysis steps to monitor and update the project and to take corrective actions in case of problems. The focus of the current paper is on the importance and crucial role of the baseline scheduling component for the two other components, and the integration of the schedule risk and project control component in order to support a better corrective action decision making when the project is in trouble