An efficient pseudo-polynomial algorithm for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem

Several algorithms for finding a lower bound on the makespan for the Resource Constrained Project Scheduling Problem (RCPSP) were proposed in the literature. However, fast computable lower bounds usually do not provide the best estimations and the methods that obtain better bounds are mainly based on the cooperation between linear and constraint programming and therefore are time-consuming. In this paper, a new pseudo-polynomial algorithm is proposed to find a makespan lower bound for RCPSP with time-dependent resource capacities. Its idea is based on a consecutive evaluation of pairs of resources and their cumulated workload. Using the proposed algorithm, several bounds for the PSPLIB benchmark were improved. The results for industrial applications are also presented where the algorithm could provide good bounds even for very large problem instances

Planification socio-responsable du travail dans les chaînes de montage d'aéronefs : comment satisfaire à la fois objectifs ergonomiques et économiques

Dans cette thèse, le problème de planification des tâches dans les chaînes de montage des aéronefs est étudié. Ces lignes de production sont principalement manuelles et tactées. L'échec de la livraison dans les délais pouvant entraîner des pénalités importantes pour le fabricant, il est essentiel de respecter le calendrier de chaque poste de travail en tenant compte à la fois de critères économiques et ergonomiques. Ce problème de planification peut être considéré comme un problème généralisé de planification de projets avec contraintes de ressources (RCPSP). Dans un premier temps, nous passons en revue les méthodes ergonomiques existantes qui peuvent être utilisées pour évaluer la charge de travail physique dans les lignes de production et examinons leur applicabilité au contexte des chaînes de montage d'aéronefs avec des temps de cycle longs. Sur la base de cette évaluation, nous développons des modèles mathématiques à introduire dans les problèmes considérés du RCPSP afin de prendre en compte l'impact ergonomique sur les opérateurs. Tenant compte de ces contraintes ergonomiques, le problème industriel initial est modélisé comme un RCPSP avec des contraintes et des objectifs spéciaux intégrant à la fois des aspects économiques et ergonomiques. Plusieurs formulations avec des opérateurs polyvalents, des ressources avec des capacités dépendantes du temps, des contraintes sur les facteurs ergonomiques et des tâches multimodales ordonnées par des relations de précédence complexes sont considérées. Des modèles de programmation par contraintes et de programmation linéaire en nombres entiers ont été développés pour ces formulations. Afin d'améliorer les procédures de solution, de nouvelles techniques de propagation de contraintes sont proposées et mises en œuvre. Un nouvel algorithme pour le calcul de la borne inférieure est également développé. L'efficacité des modèles et méthodes présentés est validée par des expériences numériques.In this thesis, the scheduling problem of tasks in aircraft assembly lines is studied. These production lines are mainly manual and paced. Since the failure of delivery on time may result in significant penalties for the manufacturer, it is crucial to meet the schedule at each workstation taking into account both economic and ergonomic criteria. This scheduling problem can be considered as a generalized Resource-Constraints Project Scheduling Problem (RCPSP). Firstly, we review the existing ergonomic methods that can be used to evaluate the physical workload in production lines and examine their applicability to the context of aircraft assembly lines with long takt times. On the basis of this evaluation, we develop mathematical models to be introduced in considered RCPSP problems in order to take into account the ergonomic impact on the operators. Taking into consideration these ergonomic constraints, the original industrial problem is modeled as a RCPSP with special constraints and objectives integrating both economic and ergonomic aspects. Several formulations with multi-skilled operators, resources with time-dependent capacities, constraints on ergonomic factors and multi-mode tasks ordered by precedence relations with time lags are considered. Constraint Programming and Integer Linear Programming models are developed for these formulations. In order to enhance the solution procedures, novel constraint propagation techniques are proposed and implemented. A new algorithm for lower bound calculation is developed as well. The efficiency of presented models and methods are validated through numerical experiments

Models for robust resource allocation in project scheduling.

The vast majority of resource-constrained project scheduling efforts assumes complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. In reality, however, project activities are subject to considerable uncertainty which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects the makespan of a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed robust resource allocation problem in exact and approximate formulations. The procedure relies on constraint propagation during its search. We report on computational results obtained on a set of benchmark problems.Model; Resource allocation; Scheduling;

Managing technology risk in R&D project planning: Optimal timing and parallelization of R&D activities.

An inherent characteristic of R&D projects is technological uncertainty, which may result in project failure, and time and resources spent without any tangible return. In pharmaceutical projects, for instance, stringent scientific procedures have to be followed to ensure patient safety and drug efficacy in pre-clinical and clinical tests before a medicine can be approved for production. A project consists of several stages, and may have to be terminated in any of these stages, with typically a low likelihood of success. In project planning and scheduling, this technological uncertainty has typically been ignored, and project plans are developed only for scenarios in which the project succeeds. In this paper, we examine how to schedule projects in order to maximize their expected net present value, when the project activities have a probability of failure, and where an activity's failure leads to overall project termination. We formulate the problem, show that it is NP-hard and develop a branchand- bound algorithm that allows to obtain optimal solutions. We also present polynomial-time algorithms for special cases, and present a number of managerial insights for R&D project and planning, including the advantages and disadvantages of parallelization of R&D activities in different settings.Applications; Branch-and-bound; Computational complexity; Exact algorithms programming; Integer; Pharmaceutical; Project management; Project scheduling; R&D projects analysis of algorithms; Risk industries;

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

Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

The application of robotics to traditionally manual manufacturing processes requires careful coordination between human and robotic agents in order to support safe and efficient coordinated work. Tasks must be allocated to agents and sequenced according to temporal and spatial constraints. Also, systems must be capable of responding on-the-fly to disturbances and people working in close physical proximity to robots. In this paper, we present a centralized algorithm, named 'Tercio,' that handles tightly intercoupled temporal and spatial constraints. Our key innovation is a fast, satisficing multi-agent task sequencer inspired by real-time processor scheduling techniques and adapted to leverage a hierarchical problem structure. We use this sequencer in conjunction with a mixed-integer linear program solver and empirically demonstrate the ability to generate near-optimal schedules for real-world problems an order of magnitude larger than those reported in prior art. Finally, we demonstrate the use of our algorithm in a multirobot hardware testbed