Regression Based Allowance Policy Determination Procedures in a General Job Shop: An Evaluation in Terms of Completion Inaccuracy Penalties

This dissertation addresses the problem of setting due dates to minimize completion inaccuracy penalties in a general job shop environment. In this simulation study, lateness penalties are generated by four defined functions: lateness variance, mean squared lateness, mean absolute lateness, and semi-quadratic lateness. Each of these functions assigns positive penalties to both early and late job completions. The study proposes and demonstrates the benefits of an iterative simulation-regression procedure in determining allowance policies. Advantages of operation-based dispatching rules over job-based dispatching rules, as well as improvements to traditional methods of setting operation due dates, are demonstrated. Characteristics and benefits of incorporating shop congestion variables in due date setting procedures under different combinations of expected shop utilization and processing time assumptions are evaluated

04231 Abstracts Collection -- Scheduling in Computer and Manufacturing Systems

During 31.05.-04.06.04, the Dagstuhl Seminar 04231 "Scheduling in Computer and Manufacturing Systems" was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Parcus: Energy-Aware and Robust Parallelization of AUTOSAR Legacy Applications

Embedded multicore processors are an attractive alternative to sophisticated single-core processors for the use in automobile electronic control units (ECUs), due to their expected higher performance and energy efficiency. Parallelization approaches for AUTOSAR legacy software exploit these benefits. Nevertheless, these approaches focus on extracting performance neglecting the system's worst-case sensor/actuator latency and energy consumption. This paper presents Parcus, an energy-and latency-aware parallelization technique that combines both runnable-and tasklevel parallelism. Parcus explicitly models the traversal of data from sensor to actuator through task instances, enabling to consider the latency imposed by parallelization techniques. The parallel schedule quality (PSQ) metric quantifies the success of the parallelization, for which it takes the latency and the processor frequency into account. We demonstrate the applicability of Parcus with an automotive case study. The results show that Parcus can fully utilize the processor's energy-saving potential.This research received funding from the EU FP7 no. 287519 (parMERASA), the ARTEMIS-JU no. 621429 (EMC2), and the German Federal Ministry of Education and Research.Peer ReviewedPostprint (author's final draft

An Optimization Based Design for Integrated Dependable Real-Time Embedded Systems

Moving from the traditional federated design paradigm, integration of mixedcriticality software components onto common computing platforms is increasingly being adopted by automotive, avionics and the control industry. This method faces new challenges such as the integration of varied functionalities (dependability, responsiveness, power consumption, etc.) under platform resource constraints and the prevention of error propagation. Based on model driven architecture and platform based design’s principles, we present a systematic mapping process for such integration adhering a transformation based design methodology. Our aim is to convert/transform initial platform independent application specifications into post integration platform specific models. In this paper, a heuristic based resource allocation approach is depicted for the consolidated mapping of safety critical and non-safety critical applications onto a common computing platform meeting particularly dependability/fault-tolerance and real-time requirements. We develop a supporting tool suite for the proposed framework, where VIATRA (VIsual Automated model TRAnsformations) is used as a transformation tool at different design steps. We validate the process and provide experimental results to show the effectiveness, performance and robustness of the approach

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

A Control-Theoretic Design And Analysis Framework For Resilient Hard Real-Time Systems

We introduce a new design metric called system-resiliency which characterizes the maximum unpredictable external stresses that any hard-real-time performance mode can withstand. Our proposed systemresiliency framework addresses resiliency determination for real-time systems with physical and hardware limitations. Furthermore, our framework advises the system designer about the feasible trade-offs between external system resources for the system operating modes on a real-time system that operates in a multi-parametric resiliency environment. Modern multi-modal real-time systems degrade the system’s operational modes as a response to unpredictable external stimuli. During these mode transitions, real-time systems should demonstrate a reliable and graceful degradation of service. Many control-theoretic-based system design approaches exist. Although they permit real-time systems to operate under various physical constraints, none of them allows the system designer to predict the system-resiliency over multi-constrained operating environment. Our framework fills this gap; the proposed framework consists of two components: the design-phase and runtime control. With the design-phase analysis, the designer predicts the behavior of the real-time system for variable external conditions. Also, the runtime controller navigates the system to the best desired target using advanced control-theoretic techniques. Further, our framework addresses the system resiliency of both uniprocessor and multicore processor systems. As a proof of concept, we first introduce a design metric called thermal-resiliency, which characterizes the maximum external thermal stress that any hard-real-time performance mode can withstand. We verify the thermal-resiliency for the external thermal stresses on a uniprocessor system through a physical testbed. We show how to solve some of the issues and challenges of designing predictable real-time systems that guarantee hard deadlines even under transitions between modes in an unpredictable thermal environment where environmental temperature may dynamically change using our new metric. We extend the derivation of thermal-resiliency to multicore systems and determine the limitations of external thermal stress that any hard-real-time performance mode can withstand. Our control-theoretic framework allows the system designer to allocate asymmetric processing resources upon a multicore proiii cessor and still maintain thermal constraints. In addition, we develop real-time-scheduling sub-components that are necessary to fully implement our framework; toward this goal, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. Under malleable gang scheduling of implicit-deadline sporadic tasks upon multiprocessors, we show the non-necessity of dynamic voltage/frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor/frequency-selection algorithm. Finally, we verify the correctness of our framework through multiple measurable physical and hardware constraints and complete our work on developing a generalized framework

Bidding Strategy for Networked Microgrids in the Day-Ahead Electricity Market

In recent years, microgrids have drawn increasing attention from both academic and industrial sectors due to their enormous potential benefits to the power systems. Microgrids are essentially highly-customized small-scale power systems. Microgrids’ islanding capability enables microgrids to conduct more flexible and energy-efficient operations. Microgrids have proved to be able to provide reliable and environmental-friendly electricity to quality-sensitive or off-grid consumers. In addition, during the grid-connected operation mode, microgrids can also provide support to the utility grid. World-widely continuous microgrid deployments indicate a paradigm shift from traditional centralized large-scale systems toward more distributed and customized small-scale systems. However, microgrids can cause as many problems as it solves. More efforts are needed to address these problems caused by microgrids integration. Considering there will be multiple microgrids in future power systems, the coordination problems between individual microgrids remain to be solved. Aiming at facilitating the promotion of microgrids, this thesis investigates the system-level modeling methods for coordination between multiple microgrids in the context of participating in the market. Firstly, this thesis reviews the background and recent development of microgrid coordination models. Problems of existing studies are identified. Motivated by these problems, the research objectives and structure of this thesis are presented. Secondly, this thesis examines and compares the most common frameworks for optimization under uncertainty. An improved unit commitment model considering uncertain sub-hour wind power ramp behaviors is presented to illustrate the reformulation and solution method of optimization models with uncertainty. Next, the price-maker bidding strategy for collaborative networked microgrids is presented. Multiple microgrids are coordinated as a single dispatchable entity and participate in the market as a price-maker. The market-clearing process is modeled using system residual supply/demand price-quota curves. Multiple uncertainty sources in the bidding model are mitigated with a hybrid stochastic-robust optimization framework. What’s more, this thesis further considers the privacy concerns of individual microgrids in the coordination process. Therefore a privacy-preserving solution method based on Dantzig-Wolfe decomposition is proposed to solve the bidding problem. Both computational and economic performances of the proposed model are compared with the performances of conventional centralized coordination framework. Lastly, this thesis provides suggestions on future research directions of coordination problems among multiple microgrids

A study of interactive control scheduling and economic assessment for robotic systems

A class of interactive control systems is derived by generalizing interactive manipulator control systems. Tasks of interactive control systems can be represented as a network of a finite set of actions which have specific operational characteristics and specific resource requirements, and which are of limited duration. This has enabled the decomposition of the overall control algorithm simultaneously and asynchronously. The performance benefits of sensor referenced and computer-aided control of manipulators in a complex environment is evaluated. The first phase of the CURV arm control system software development and the basic features of the control algorithms and their software implementation are presented. An optimal solution for a production scheduling problem that will be easy to implement in practical situations is investigated