The static and analytic scheduling approach is very difficult to follow and is not always applicable in real-time. Most of the scheduling algorithms are designed to be established in offline environment. However, we are challenged with three characteristics in real cases: First, problem data of jobs are not known in advance. Second, most of the shop’s parameters tend to be stochastic. Third, th...

Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and ...

In stochastic scheduling, we want to allocate a limited amount of resources to a set of jobs that need to be serviced. Unlike in deterministic scheduling, however, the parameters of the system may be stochastic. For example, the time it takes to process a job may be subject to random fluctuations. Stochastic scheduling problems occur in a variety of practical situations, such as manufacturing, ...

The purpose of this paper is to present examples for the sometimes surprisingly di/erent behavior of deterministic and stochastic scheduling problems. In particular, it demonstrates some seemingly counterintuitive properties of optimal scheduling policies for stochastic machine scheduling problems. c © 2003 Elsevier B.V. All rights reserved.

We present a scheduling model which is tailored to the dynamics of software projects. In the model, activity times are stochastic, resources are constrained and non-identical, and scheduling is preemptive. As opposed to other stochastic project scheduling models, the completion time of an activity explicitly depends on the amount of feedback received from concurrent activities. As a consequence...

The field of stochastic scheduling is motivated by the design and operational problems arising in systems where scarce service resources must be allocated over time to jobs with random features vying for their attention. Important examples include manufacturing and computer-communication systems. Consider, e.g., the case of a manufacturing workstation processing different part types, where part...

Resource constrained project scheduling (RCPSP) is one of the most crucial problems in project problem. The aim of RCPSP, which is NP-hard, is to minimize the project duration. Sometimes the activity durations are not known in advance and are random variables. These problems are called stochastic resource constrained project scheduling problems or stochastic RCPSP. Various algorithms such as ge...