We consider the problem (J, F) of allocating a single machine to the stochastic tasks in J in such a way that precedence constraints F are respected. If we have rewards which are discounted and additive then the problem of determining an optimal policy for scheduling in the class of fully preemptive policies can be formulated as a discounted Markov decision process (MDP). Policies are developed...

We propose a new decision-theoretic approach for solving execution-time deliberation scheduling problems using recent advances in Generalized Semi-Markov Decision Processes (GSMDPs). In particular, we use GSMDPs to more accurately model domains in which planning and execution occur concurrently, planimprovement actions have uncertain effects and duration, and events (such as threats) occur asyn...

Task scheduling is important for the proper functioning of parallel processor systems. The static scheduling of tasks onto networks of parallel processors is well-defined and documented in the literature. However, in many practical situations a priori information about the tasks that need to be scheduled is not available. In such situations, tasks usually arrive dynamically and the scheduling s...

Escalating stratums of breeze vigor, which is variable, difficult to predict accurately, and escalating connected via power electronic converters, are changing how electricity grids are planned, designed, and operated. . Systems with high breeze penetration are also experiencing dramatic changes to the operating regimes of conventional resources to areas with large loads. Optimal diminutive-ten...

Scheduling problems have the standard consideration in the field of manufacturing. Among the various types of scheduling problems, the job shop scheduling problem is one of the most interesting NP-hard problems. As the job shop scheduling is an optimization problem, Genetic algorithm was selected to solve it In this study. Selection scheme is one of the important operators of Genetic algorithm....

Aggregating various resources of different organizations, Grid provides excess computation power and storage capacity and is a trend to solve the computation-intensive engineering and mathematical problem. Because of the dynamic and heterogeneous characteristic of Grid system, scheduling algorithms make a vital difference to the efficiency of Grid. In this paper, a heuristic algorithm based on ...

In the stochastic online scheduling environment, jobs with unknown release times and weights arrive over time. Upon arrival, the information on the weight of the job is revealed but the processing requirement remains unknown until the job is finished. With the objective of minimizing the total weighted completion time, various asymptotically optimal algorithms have been proposed for the online ...

Abstract We consider the problem of scheduling under demand uncertainty a multiproduct batch plant represented through the State Task Network. Given a scheduling horizon consisting of several time-periods in which product demands are placed, the objective is to select a schedule that maximizes the expected profit. We present a multistage stochastic Mixed Integer Linear Programming (MILP) model,...

We show how a technique from signal processing known as zero-delay convolution can be used to develop more efficient dynamic programming algorithms for a broad class of stochastic optimization problems. This class includes several variants of discrete stochastic shortest path, scheduling, and knapsack problems, all of which involve making a series of decisions over time that have stochastic con...

We formulate and solve a new stochastic integer programming model for dynamic sequencing and scheduling of appointments to a single stochastic server. We assume that service durations and the number of customers to be served on a particular day are uncertain. Customers are sequenced and scheduled dynamically (on-line) one at a time as they request appointments. We present a two-stage stochastic...