A proactive sampling approach to project scheduling under uncertainty

Uncertainty in activity durations is a key characteristic of many real world scheduling problems in manufacturing, lo-gistics and project management. RCPSP/max with dura-tional uncertainty is a general model that can be used to rep-resent durational uncertainty in a wide variety of scheduling problems where there exist resource constraints. However, computing schedules or execution strategies for RCPSP/max with durational uncertainty is NP-hard and hence we focus on providing approximation methods in this paper. We pro-vide a principled approximation approach based on Sample Average Approximation (SAA) to compute proactive sched-ules for RCPSP/max with durational uncertainty. We fur-ther contribute an extension to SAA for improving scala-bility significantly without sacrificing on solution quality. Not only is our approach able to compute schedules at com-parable runtimes as existing approaches, it also provides lower α-quantile makespan (also referred to as α-robust makespan) values than the best known approach on bench-mark problems from the literature

Optimization Models for Multiple Resource Planning

Multiple resource planning is a very crucial undertaking for most organizations. Apart from reducing operational complexity, multiple resource planning facilitates efficient allocation of resources which reduces costs by minimizing the cost of tardiness and the cost for additional capacity. The current research investigates multiple resource loading problems (MRLP). MRLPs are very prevalent in today's organizational environments and are particularly critical for organizations that handle concurrent, time-intensive, and multiple-resource projects. Using data obtained from the Ministry of Administrative Development, Labor and Social Affairs (ADLSA), an MRLP is proposed. The problem utilizes data regarding staff, time, equipment, and finance to ensure efficient resource allocation among competing projects. In particular, the thesis proposes a novel model and solution approach for the MRLP. Computational experiments are then performed on the model. The results show that the model performs well, even in higher instances. The positive results attest to the effectiveness of the proposed MRLP proble

A new mixed-integer modeling approach for capacity-constrained continuous-time scheduling problems

Nowadays, scheduling and resource management are increasingly important issues for organizations. Indeed, they do not only constitute an underlying necessity to make things work properly within the companies, but are and will always be more critical means to reduce costs and get competitive advantage in the market. Different approaches have been typically employed for these problems during the years. Among the others, linear programming techniques represent a valid tool that, despite applicable only to instances of limited dimension, offers an extremely flexible modeling opportunity, able to produce either optimal or approximate solutions of certified quality. In this spirit, the definition of suitable indicator variables and the use of particular constraints are proposed in the present work, with the aim of providing a useful basis for different mathematical models, taking into account scarce resources and other potential limitations. More in detail, a very well-known problem from the literature, the Resource Constrained Project Scheduling Problem, is investigated, and a new mixed-integer linear formulation is introduced, which treats time as a continuous variable. The considered model presents several advantages from the computational point of view, that are deeply studied and compared with those of one of the best methods recently developed in the same field. Extensive experiments reveal the good performances achieved by the proposed formulation over all the KPIs included in the analysis, thus motivating further applications to derived problems, such as the workforce planning and scheduling framework presented at the end of this dissertation

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