Jedule: A Tool for Visualizing Schedules of Parallel Applications

International audienceTask scheduling is one of the most prominent problems in the era of parallel computing. We find scheduling algorithms in every domain of computer science, e.g., mapping multiprocessor tasks to clusters, mapping jobs to grid resources, or mapping fine-grained tasks to cores of multicore processors. Many tools exist that help understand or debug an application by presenting visual representations of a certain program run, e.g., visualizations of MPI traces. However, often developers want to get a global and abstract view of their schedules first. In this paper we introduce Jedule, a tool dedicated to visualize schedules of parallel applications. We demonstrate the effectiveness of Jedule by showing how it helped analyzing problems in several case studies

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Train scheduling with application to the UK rail network

Nowadays, transforming the railway industry for better performance and making the best usage of the current capacity are the key issues in many countries. Operational research methods and in particular scheduling techniques have a substantial potential to offer algorithmic solutions to improve railway operation and control. This thesis looks at train scheduling and rescheduling problems in a microscopic level with regard to the track topology. All of the timetable components are fixed and we aim to minimize delay by considering a tardiness objective function and only allowing changes to the order and to the starting times of trains on blocks. Various operational and safety constraints should be considered. We have achieved further developments in the field including generalizations to the existing models in order to obtain a generic model that includes important additional constraints. We make use of the analogy between the train scheduling problem and job shop scheduling problem. The model is customized to the UK railway network and signaling system. Introduced solution methods are inspired by the successful results of the shifting bottleneck to solve the job shop scheduling problems. Several solution methods such as mathematical programming and different variants of the shifting bottleneck are investigated. The proposed methods are implemented on a real-world case study based on London Bridge area in the South East of the UK. It is a dense network of interconnected lines and complicated with regard to stations and junctions structure. Computational experiments show the efficiency and limitations of the mathematical programming model and one variant of the proposed shifting bottleneck algorithms. This study also addresses train routing and rerouting problems in a mesoscopic level regarding relaxing some of the detailed constraints. The aim is to make the best usage of routing options in the network to minimize delay propagation. In addition to train routes, train entry times and orders on track segment are defined. Hence, the routing and scheduling decisions are combined in the solutions arising from this problem. Train routing and rerouting problems are formulated as modified job shop problems to include the main safety and operational constraints. Novel shifting bottleneck algorithms are provided to solve the problem. Computational results are reported on the same case study based on London Bridge area and the results show the efficiency of one variant of the developed shifting bottleneck algorithms in terms of solution quality and runtime

Applications of simulation and optimization techniques in optimizing room and pillar mining systems

The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes --Abstract, page iii

A survey on scheduling and mapping techniques in 3D Network-on-chip

Network-on-Chips (NoCs) have been widely employed in the design of multiprocessor system-on-chips (MPSoCs) as a scalable communication solution. NoCs enable communications between on-chip Intellectual Property (IP) cores and allow those cores to achieve higher performance by outsourcing their communication tasks. Mapping and Scheduling methodologies are key elements in assigning application tasks, allocating the tasks to the IPs, and organising communication among them to achieve some specified objectives. The goal of this paper is to present a detailed state-of-the-art of research in the field of mapping and scheduling of applications on 3D NoC, classifying the works based on several dimensions and giving some potential research directions

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development