251 research outputs found

    Scheduling theory since 1981: an annotated bibliography

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    Scheduling for Service Stability and Supply Chain Coordination

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    This dissertation studies scheduling for service stability and for supply chain coordination as well. The scheduling problems for service stability are studied from the single perspective of a firm itself, while the scheduling problems for supply chain coordination are investigated from the perspective of a supply chain. Both the studies have broad applications in real life. In the first study, several job scheduling problems are addressed, with the measure of performance being job completion time variance (CTV). CTV minimization is used to represent service stability, since it means that jobs are completed in a relative concentrated period of time. CTV minimization also conforms to the Just-in-time philosophy. Two scheduling problems are studied on multiple identical parallel machines. The one problem does not restrict the idle times of machines before their job processing, while the other does. For these two scheduling problems, desirable properties are explored and heuristic algorithms are proposed. Computational results show the excellent performances of the proposed algorithms. The third scheduling problem in the first study is considered on a single machine and from the users’ perspective rather than the system’s perspective. The performance measure is thus class-based completion time variance (CB-CTV). This problem is shown to be able to be transformed into multiple CTV problems. Therefore, the well-developed desirable properties of the CTV problem can be applied to solve the CB-CTV problem. The tradeoff between the CB-CTV problem and the CTV problem is also investigated. The second study deals with scheduling coordination in a supply chain, since supply chain coordination is increasingly critical in recent years. Usually, different standpoints prevent decision makers in a supply chain from having agreement on a certain scheduling decision. Therefore conflicts arise. In pursuit of excellent performance of the whole supply chain, coordination among decision makers is needed. In this study, the scheduling conflicts are measured and analyzed from different perspectives of decision makers, and cooperation mechanisms are proposed based on different scenarios of the relative bargaining power among decision makers. The cooperation savings are examined as well

    Order scheduling in dedicated and flexible machine environments

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    Order scheduling models are relatively new in the field of scheduling. Consider a facility with m parallel machines that can process k different products (job types). Each machine can process a given subset of different product types. There are n orders from n different clients. Each order requests specific quantities of the various different products that can be produced concurrently on their given subsets of machines; it may have a release date, a weight and a due date. Preemptions may be allowed. An order can not be shipped until the processing of all the products for the order has been completed. Thus, the finish time of an order is the time when the last job of the order has been completed. Even though the idea is somewhat new that order scheduling measures the overall completion time of a set of jobs (i.e., an order requesting different product types) instead of the individual completion time of each product type for any given order, many applications require that decision-makers consider orders rather than the individual product types in orders. Research into order scheduling models is motivated by their various real-life applications in manufacturing systems, equipment maintenance, computing systems, and other industrial contexts, where the components of each order can be processed concurrently on the parallel machines. In this research, two cases of order scheduling models are studied, namely, the fully dedicated environment in which each machine can produce one and only one product type, and the fully flexible machine environment in which each machine can produce all product types. With different side constraints and objective functions, the two cases include a lot of problems that are of interest. Special interest is focused on the minimization of the total weighted completion time, the number of late orders, the maximum lateness, and so on. On the one hand, polynomial time algorithms are proposed for some problems. One the other hand, for problems that are NP-hard, complexity proofs are shown and heuristics with their worst-case performance and empirical analyses are also presented

    Single-machine bicriteria scheduling

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    Resource assignment algorithms for vehicular clouds

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    In this thesis, we study the task scheduling problem in vehicular clouds. It falls in the category of unrelated parallel machine scheduling problems. Resource assignment in vehicular clouds must deal with the transient nature of the cloud resources and a relaxed definition of non-preemptive tasks. Despite a rich literature in machine scheduling and grid computing, the resource assignment problem in vehicular clouds has not been examined yet. We show that even the problem of finding a minimum cost schedule for a single task over unrelated machines is NP-hard. We then provide a fully polynomial time approximation scheme and a greedy approximation for scheduling a single task. We extend these algorithms to the case of scheduling n tasks. We validate our algorithms through extensive simulations that use synthetically generated data as well as real data extracted from vehicle mobility and grid computing workload traces. Our contributions are, to the best of our knowledge, the first quantitative analysis of the computational power of vehicular clouds

    A Stochastic Approach to Hierarchical Planning and Scheduling

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    This paper surveys recent results for stochastic discrete programming models of hierarchical planning problems. Practical problems of this nature typically involve a sequence of decision over time at an increasing level of detail and with increasingly accurate information. These may be modeled by multistage stochastic programs whose lower levels (later stages) are stochastic versions of similar NP-hard deterministic combinatorial optimization problems and hence require the use of approximations and heuristics for near-optimal solution. After a brief survey of distributional assumptions on processing times under which SEPT and LEPT policies remain optimal for m-machine scheduling problems, results are presented for various 2-level scheduling problems in which the first stage concerns the acquisition (or assignment) of machines. For example, heuristics which are asymptotically optimal in expectation as the number of jobs in the system increases are analyzed for problems whose second stages are either identical or uniform m-machine scheduling problems. A 3-level location, distribution and routing model in the plane is also discussed

    Grain-size optimization and scheduling for distributed memory architectures

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    The problem of scheduling parallel programs for execution on distributed memory parallel architectures has become the subject of intense research in recent, years. Because of the high inter-processor communication overhead in existing parallel machines, a crucial step in scheduling is task clustering, the process of coalescing heavily communicating fine grain tasks into coarser ones in order to reduce the communication overhead so that the overall execution time is minimized. The thesis of this research is that the task of exposing the parallelism in a given application should be left to the algorithm designer. On the other hand, the task of limiting the parallelism in a chosen parallel algorithm is best handled by the compiler or operating system for the target parallel machine. Toward this end, we have developed CASS (for Clustering And Scheduling System), a. task management system that provides facilities for automatic granularity optimization and task scheduling of parallel programs on distributed memory parallel architectures. In CASS, a task graph generated by a profiler is used by the clustering module to find the best granularity al which to execute the program so that the overall execution time is minimized. The scheduling module maps the clusters onto a. fixed number of processors and determines the order of execution of tasks in each processor. The output of scheduling module is then used by a code generator to generate machine instructions. CASS employs two efficient heuristic algorithms for clustering static task graphs: CASS-I for clustering with task duplication, and CASS-II for clustering without task duplication. It is shown that the clustering algorithms used by CASS outperform the best known algorithms reported in the literature. For the scheduling module in CASS, a heuristic algorithm based on load balancing is used to merge clusters such that the number of clusters matches the number of available physical processors. We also investigate task clustering algorithms for dynamic task graphs and show that it is inherently more difficult than the static case
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