The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

Stochastic Greedy-Based Particle Swarm Optimization for Workflow Application in Grid

The workflow application is a common grid application. The objective of a workflow application is to complete all the tasks within the shortest time, i.e., minimal makespan. A job scheduler with a high-efficient scheduling algorithm is required to solve workflow scheduling based on grid information. Scheduling problems are NP-complete problems, which have been well solved by metaheuristic algorithms. To attain effective solutions to workflow application, an algorithm named the stochastic greedy PSO (SGPSO) is proposed to solve workflow scheduling; a new velocity update rule based on stochastic greedy is suggested. Restated, a stochastic greedy-driven search guidance is provided to particles. Meanwhile, a stochastic greedy probability (SGP) parameter is designed to help control whether the search behavior of particles is exploitation or exploration to improve search efficiency. The advantages of the proposed scheme are retaining exploration capability during a search, reducing complexity and computation time, and easy to implement. Retaining exploration capability during a search prevents particles from getting trapped on local optimums. Additionally, the diversity of the proposed SGPSO is verified and analyzed. The experimental results demonstrate that the SGPSO proposed can effectively solve workflow class problems encountered in the grid environment

Project schedule optimisation utilising genetic algorithms

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class

Using Deep Neural Networks for Scheduling Resource-Constrained Activity Sequences

Eines der bekanntesten Planungsprobleme stellt die Planung von Aktivitäten unter Berücksichtigung von Reihenfolgenbeziehungen zwischen diesen Aktivitäten sowie Ressourcenbeschränkungen dar. In der Literatur ist dieses Planungsproblem als das ressourcenbeschränkte Projektplanungsproblem bekannt und wird im Englischen als Resource-Constrained Project Scheduling Problem oder kurz RCPSP bezeichnet. Das Ziel dieses Problems besteht darin, die Bearbeitungszeit einer Aktivitätsfolge zu minimieren, indem festgelegt wird, wann jede einzelne Aktivität beginnen soll, ohne dass die Ressourcenbeschränkungen überschritten werden. Wenn die Bearbeitungsdauern der Aktivitäten bekannt und deterministisch sind, können die Startzeiten der Aktivitäten à priori definiert werden, ohne dass die Gefahr besteht, dass der Zeitplan unausführbar wird. Da jedoch die Bearbeitungsdauern der Aktivitäten häufig nicht deterministisch sind, sondern auf Schätzungen von Expertengruppen oder historischen Daten basieren, können die realen Bearbeitungsdauern von den geschätzten abweichen. In diesem Fall ist eine reaktive Planungsstrategie zu bevorzugen. Solch eine reaktive Strategie legt die Startzeiten der einzelnen Aktivitäten nicht zu Beginn des Projektes fest, sondern erst unmittelbar an jedem Entscheidungspunkt im Projekt, also zu Beginn des Projektes und immer dann wenn eine oder mehrere Aktivitäten abgeschlossen und die beanspruchten Ressourcen frei werden. In dieser Arbeit wird eine neue reaktive Planungsstrategie für das ressourcenbeschränkte Projektplanungsproblem vorgestellt. Im Gegensatz zu anderen Literaturbeiträgen, in denen exakte, heuristische und meta-heuristische Methoden zur Anwendung kommen, basiert der in dieser Arbeit aufgestellte Lösungsansatz auf künstlichen neuronalen Netzen und maschinellem Lernen. Die neuronalen Netze verarbeiten die Informationen, die den aktuellen Zustand der Aktivitätsfolge beschreiben, und erzeugen daraus Prioritätswerte für die Aktivitäten, die im aktuellen Entscheidungspunkt gestartet werden können. Das maschinelle Lernen und insbesondere das überwachte Lernen werden für das Trainieren der neuronalen Netze mit beispielhaften Trainingsdaten angewendet, wobei die Trainingsdaten mit Hilfe einer Simulation erzeugt wurden. Sechs verschiedene neuronale Netzwerkstrukturen werden in dieser Arbeit betrachtet. Diese Strukturen unterscheiden sich sowohl in der ihnen zur Verfügung gestellten Eingabeinformation als auch der Art des neuronalen Netzes, das diese Information verarbeitet. Es werden drei Arten von neuronalen Netzen betrachtet. Diese sind neuronale Netze mit vollständig verbundenen Schichten, 1- dimensionale faltende neuronale Netze und 2-dimensionale neuronale faltende Netze. Darüber hinaus werden innerhalb jeder einzelnen Netzwerkstruktur verschiedene Hyperparameter, z.B. die Lernrate, Anzahl der Lernepochen, Anzahl an Schichten und Anzahl an Neuronen per Schicht, mittels einer Bayesischen Optimierung abgestimmt. Während des Abstimmens der Hyperparameter wurden außerdem Bereiche für die Hyperparameter identifiziert, die zur Verbesserung der Leistungen genutzt werden sollten. Das am besten trainierte Netzwerk wird dann für den Vergleich mit anderen vierunddreißig reaktiven heuristischen Methoden herangezogen. Die Ergebnisse dieses Vergleichs zeigen, dass der in dieser Arbeit vorgeschlagene Ansatz in Bezug auf die Minimierung der Gesamtdauer der Aktivitätsfolge die meisten Heuristiken übertrifft. Lediglich 3 Heuristiken erzielen kürzere Gesamtdauern als der Ansatz dieser Arbeit, jedoch sind deren Rechenzeiten um viele Größenordnungen länger. Eine Annahme in dieser Arbeit besteht darin, dass während der Ausführung der Aktivitäten Abweichungen bei den Aktivitätsdauern auftreten können, obwohl die Aktivitätsdauern generell als deterministisch modelliert werden. Folglich wird eine Sensitivitätsanalyse durchgeführt, um zu prüfen, ob die vorgeschlagene reaktive Planungsstrategie auch dann kompetitiv bleibt, wenn die Aktivitätsdauern von den angenommenen Werten abweichen

Energy-aware scheduling in heterogeneous computing systems

In the last decade, the grid computing systems emerged as useful provider of the computing power required for solving complex problems. The classic formulation of the scheduling problem in heterogeneous computing systems is NP-hard, thus approximation techniques are required for solving real-world scenarios of this problem. This thesis tackles the problem of scheduling tasks in a heterogeneous computing environment in reduced execution times, considering the schedule length and the total energy consumption as the optimization objectives. An efficient multithreading local search algorithm for solving the multi-objective scheduling problem in heterogeneous computing systems, named MEMLS, is presented. The proposed method follows a fully multi-objective approach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multithreading algorithm outperforms a set of fast and accurate two-phase deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve significant improvements in both makespan and energy consumption objectives in reduced execution times for a large set of testbed instances, while exhibiting very good scalability. The ME-MLS was evaluated solving instances comprised of up to 2048 tasks and 64 machines. In order to scale the dimension of the problem instances even further and tackle large-sized problem instances, the Graphical Processing Unit (GPU) architecture is considered. This line of future work has been initially tackled with the gPALS: a hybrid CPU/GPU local search algorithm for efficiently tackling a single-objective heterogeneous computing scheduling problem. The gPALS shows very promising results, being able to tackle instances of up to 32768 tasks and 1024 machines in reasonable execution times.En la última década, los sistemas de computación grid se han convertido en útiles proveedores de la capacidad de cálculo necesaria para la resolución de problemas complejos. En su formulación clásica, el problema de la planificación de tareas en sistemas heterogéneos es un problema NP difícil, por lo que se requieren técnicas de resolución aproximadas para atacar instancias de tamaño realista de este problema. Esta tesis aborda el problema de la planificación de tareas en sistemas heterogéneos, considerando el largo de la planificación y el consumo energético como objetivos a optimizar. Para la resolución de este problema se propone un algoritmo de búsqueda local eficiente y multihilo. El método propuesto se trata de un enfoque plenamente multiobjetivo que consiste en la aplicación de una búsqueda basada en dominancia de Pareto que se ejecuta en paralelo mediante el uso de varios hilos de ejecución. El análisis experimental demuestra que el algoritmo multithilado propuesto supera a un conjunto de heurísticas deterministas rápidas y e caces basadas en el algoritmo MinMin tradicional. El nuevo método, ME-MLS, es capaz de lograr mejoras significativas tanto en el largo de la planificación y como en consumo energético, en tiempos de ejecución reducidos para un gran número de casos de prueba, mientras que exhibe una escalabilidad muy promisoria. El ME-MLS fue evaluado abordando instancias de hasta 2048 tareas y 64 máquinas. Con el n de aumentar la dimensión de las instancias abordadas y hacer frente a instancias de gran tamaño, se consideró la utilización de la arquitectura provista por las unidades de procesamiento gráfico (GPU). Esta línea de trabajo futuro ha sido abordada inicialmente con el algoritmo gPALS: un algoritmo híbrido CPU/GPU de búsqueda local para la planificación de tareas en en sistemas heterogéneos considerando el largo de la planificación como único objetivo. La evaluación del algoritmo gPALS ha mostrado resultados muy prometedores, siendo capaz de abordar instancias de hasta 32768 tareas y 1024 máquinas en tiempos de ejecución razonables

A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

[EN] Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and 61572127), the Natural Science Foundation ofJiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30916014107), the Fundamental Research Funds for the Central University (Grant No. 30916015104). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DP12015-65895-R) co-financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Future Generation Computer Systems. 71:57-72. https://doi.org/10.1016/j.future.2017.01.020S57727

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn