1,373 research outputs found

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

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    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

    The Integration of Maintenance Decisions and Flow Shop Scheduling

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    In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects

    Scheduling Problems

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    Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

    Performance Modeling and Resource Management for Mapreduce Applications

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    Big Data analytics is increasingly performed using the MapReduce paradigm and its open-source implementation Hadoop as a platform choice. Many applications associated with live business intelligence are written as complex data analysis programs defined by directed acyclic graphs of MapReduce jobs. An increasing number of these applications have additional requirements for completion time guarantees. The advent of cloud computing brings a competitive alternative solution for data analytic problems while it also introduces new challenges in provisioning clusters that provide best cost-performance trade-offs. In this dissertation, we aim to develop a performance evaluation framework that enables automatic resource management for MapReduce applications in achieving different optimization goals. It consists of the following components: (1) a performance modeling framework that estimates the completion time of a given MapReduce application when executed on a Hadoop cluster according to its input data sets, the job settings and the amount of allocated resources for processing it; (2) a resource allocation strategy for deadline-driven MapReduce applications that automatically tailors and controls the resource allocation on a shared Hadoop cluster to different applications to achieve their (soft) deadlines; (3) a simulator-based solution to the resource provision problem in public cloud environment that guides the users to determine the types and amount of resources that should lease from the service provider for achieving different goals; (4) an optimization strategy to automatically determine the optimal job settings within a MapReduce application for efficient execution and resource usage. We validate the accuracy, efficiency, and performance benefits of the proposed framework using a set of realistic MapReduce applications on both private cluster and public cloud environment

    Unified Concept of Bottleneck

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    The term `bottleneck` has been extensively used in operations management literature. Management paradigms like the Theory of Constraints focus on the identification and exploitation of bottlenecks. Yet, we show that the term has not been rigorously defined. We provide a classification of bottleneck definitions available in literature and discuss several myths associated with the concept of bottleneck. The apparent diversity of definitions raises the question whether it is possible to have a single bottleneck definition which has as much applicability in high variety job shops as in mass production environments. The key to the formulation of an unified concept of bottleneck lies in relating the concept of bottleneck to the concept of shadow price of resources. We propose an universally applicable bottleneck definition based on the concept of average shadow price. We discuss the procedure for determination of bottleneck values for diverse production environments. The Law of Diminishing Returns is shown to be a sufficient but not necessary condition for the equivalence of the average and the marginal shadow price. The equivalence of these two prices is proved for several environments. Bottleneck identification is the first step in resource acquisition decisions faced by managers. The definition of bottleneck presented in the paper has the potential to not only reduce ambiguity regarding the meaning of the term but also open a new window to the formulation and analysis of a rich set of problems faced by managers.

    Optimal Map Reduce Job Capacity Allocation in Cloud Systems.

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    We are entering a Big Data world. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to provide performance for MapReduce jobs and minimize cloud resource costs. The contribution of this paper is twofold: (i) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters, (ii) we formulate a linear programming model able to minimize cloud resources costs and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees. Simulation results show how the execution time of MapReduce jobs falls within 14% of our upper bound on average. Moreover, numerical analyses demonstrate that our method is able to determine the global optimal solution of the linear problem for systems including up to 1,000 user classes in less than 0.5 seconds

    Theoretical and Computational Research in Various Scheduling Models

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    Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

    TRADE-OFF BALANCING FOR STABLE AND SUSTAINABLE OPERATING ROOM SCHEDULING

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    The implementation of the mandatory alternative payment model (APM) guarantees savings for Medicare regardless of participant hospitals ability for reducing spending that shifts the cost minimization burden from insurers onto the hospital administrators. Surgical interventions account for more than 30% and 40% of hospitals total cost and total revenue, respectively, with a cost structure consisting of nearly 56% direct cost, thus, large cost reduction is possible through efficient operation management. However, optimizing operating rooms (ORs) schedules is extraordinarily challenging due to the complexities involved in the process. We present new algorithms and managerial guidelines to address the problem of OR planning and scheduling with disturbances in demand and case times, and inconsistencies among the performance measures. We also present an extension of these algorithms that addresses production scheduling for sustainability. We demonstrate the effectiveness and efficiency of these algorithms via simulation and statistical analyses
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