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

Integrated product and process models: towards an integrated framework and review

While product models and process models have a long standing transiting, there are few models that integrate the two type of models. Those that exist are research systems, which even if validated in industry do not have a broad uptake to date. This paper develops an integrated framework for product and process models based on the purpose the models are put it building on a model of Browning and Ramasesh. Selected integrated model are classified according to the framework. This revealed the no model to date gives equal weight to product and process models

Otimização e simulação da programação de projetos em estaleiros nacionais considerando restrições de conteúdo local

The growth of oil exploration and production in Brazil, especially after the pre-salt oil reserves discovery, implied in ships, rigs and platforms construction demand increase. In this context, the activities planning and scheduling in Brazilian shipyards has become a challenging task to minimize exposure to fines for local content non-compliance clauses and, at the same time, meet the demand of the national oil industry. In order to support the decision-making process of allocating projects to shipyards and dimensioning the exposure to foreign content, this work proposes a mixed integer programming model, based on the classic resource constrained project scheduling problem. Due to the historical delays in the completion of activities by the national shipbuilding industry, a robust model was developed to address the anticipation of possible delays. The parameter known as degree of conservatism was scaled according to the activities lateness probabilities, based on the historical data. Finally, an event discrete simulation model is proposed for sensitivity analysis and delays dimensioning in the projects delivery, and the opportunity cost in the oil production delay.Com o crescimento das atividades de exploração e produção de óleo e gás no país, principalmente com a descoberta das reservas de petróleo na camada do Pré-sal, houve aumento na demanda por construção de navios, sondas e plataformas. Nesse contexto, o planejamento e programação de atividades nos estaleiros nacionais tornou-se uma tarefa desafiadora, visando minimizar a exposição a multas por não atendimento de cláusulas de conteúdo nacional e, ao mesmo tempo, atender a demanda da indústria petrolífera nacional. Para apoiar o processo decisório de alocação de projetos a estaleiros e dimensionamento da exposição ao conteúdo estrangeiro, foi proposto um modelo de programação inteira mista, baseado no problema clássico de programação de projetos com restrição de recursos. Devido aos atrasos históricos na conclusão das atividades pela indústria naval nacional, uma modelagem robusta foi desenvolvida para tratar a antecipação de possíveis atrasos. O parâmetro conhecido como grau de conservadorismo foi dimensionado de acordo com as probabilidades de atrasos das atividades, baseadas nos dados históricos. Por fim, foi proposto um modelo de simulação para análise de sensibilidade e dimensionamento de atrasos na entrega dos projetos, e do custo de oportunidade no atraso na produção de óleo

Solution model to the resource constrained project scheduling problem RCPSP with insertion task and random duration

In this doctoral thesis, an optimization model is developed in order to provide a solution strategy to the scheduling problem in new product development projects. This projects face diferent risks that affect the normal execution of activities and their due date. Therefore, the problem has been analyzed as a resource-constrained project scheduling problem (RCPSP) under a probabilistic context. Specifically, it includes parameters like the random duration of the activities and the probability of inserting additional tasks in the project network. The optimization model developed in this research has four stages: the identification of risks, the estimation of the activities duration from four redundancy based methods, the resolution of an integer linear program in order to generate the project baselines, and the selection of the best baseline through two robustness indicators. A case study to applied the proposed model is presented, which refers to the development of a leadframe material for a semiconductor package. In the developed model, two fundamental contributions are hightlighted: the integration of a detail project’s risks analysis with an optimization model that generate a robust baseline, and the adaptation of the RCPSP with random duration of activities and stochastic insertion tasks to the case of new product development project.En esta tesis doctoral, se desarrolla un modelo de optimización como estrategia de solución al problema de programación de proyectos de desarrollo de nuevos productos. Teniendo en cuenta que este tipo de proyectos son afectados por diversos riesgos que al materializarse pueden afectar la ejecución normal de las actividades y sus plazos de finalización, se ha optado por modelar el problema dentro de un contexto probabilístico y tomando como referente el problema de programación de proyectos con recursos restringidos (Resource Constrained Project Scheduling Problem: RCPSP). El RCPSP adoptado incluye como parámetros: la duración aleatoria de las actividades y la probabilidad de insertar tareas adicionales en la red del proyecto. El modelo de optimización desarrollado en esta investigación contempla cuatro etapas: la identificación de los riesgos, la estimación de la duración de las actividades a partir de cuatro procedimientos basados en duraciones redundantes, la resolución de un programa lineal entero que genera las líneas-base del proyecto, y la selección de la mejor línea-base evaluada por medio de dos indicadores de robustez. Con el fin de aplicar el modelo propuesto, se presenta un caso de estudio que hace referencia al desarrollo de un material para el marco de conexión de un circuito integrado. En el modelo desarrollado se destacan dos aportes fundamentales: la integración de un análisis detallado de riesgos del proyecto con un modelo de optimización que genera una línea-base robusta, y la adaptación del RCPSP con duración aleatoria de actividades e inserción de tareas al caso de proyectos de desarrollo de nuevos productos.Doctorad

NONLINEAR OPTIMIZATION FOR PROJECT SCHEDULING AND RESOURCE ALLOCATION UNDER UNCERTAINTY

Project planning and scheduling when there are both resource constraints and uncertainty in task durations is an important and complex problem. There is a long history of work on deterministic resource-constrained project scheduling problems, but efforts directed at stochastic versions of that problem are fewer and more recent. Incorporating the ability to reallocate resources among tasks to change the characteristics of their duration probability distributions adds another important dimension to the problem, and enables integration of project planning and scheduling. Among the small number of previous works on this subject, there are two very different perspectives. Golenko-Ginzburg and Gonik (1997, 1998) have created a simulation-based approach that ?operates? the project through time and attempts to optimize locally regarding decisions on starting specific tasks at specific times. Turnquist and Nozick (2004) have formulated a nonlinear optimization model to plan resource allocations and schedule decisions a priori. This has the advantage of taking a global perspective on the project in making resource allocation decisions, but it is not adaptive to the experience with earlier tasks when making later decisions in the same way that the simulation approach is. Although the solution to their model produces a ?baseline schedule? (i.e., times when tasks are planned to start), the formulation puts much greater emphasis on resource allocation decisions. The paper by Turnquist and Nozick (2004) describes the problem formulation as a nonlinear optimization. For small problem instances (up to about 30 tasks), good solutions can be found using standard nonlinear programming packages(e.g., NPSOL). However, for larger problems, the standard packages often fail to find any solution in a reasonable amount of computational time. One major contribution of this dissertation is the development of a solution method that can solve larger problem instances efficiently and reliably. In this dissertation, we recommend using the partially augmented Lagrangian (PAL) method to solve the suggested nonlinear optimization. The test problems considered here include projects with up to 90 tasks, and solutions to the 90-task problems take about 2 minutes on a desktop PC. A second contribution of this dissertation is exploration of insights that can be gained through systematic variation of the basic parameters of the model formulation on a given problem. These insights have both computational and managerial implications for practical application of the model