A heuristic approach for resource constrained project scheduling with uncertain activity durations (original) (raw)
Resource Constrained Project Scheduling under Uncertainty: A Survey
Proceedings of the 23rd Central European Conference on Information and Intelligent Systems, Varazdin, Croatia, 2012
Resource Constrained Project Scheduling Problems (RCPSP), especially their stochastic variants, and the methods operating on them represent a general project scheduling optimization framework. This paper presents the survey of methods and models that are put into the historical context and are categorized according to their working principles. It aims to supplement and update existing RCPSP surveys. The current state of the research field is assessed and potential research venues are identified.
The project scheduling problem with non-deterministic activities duration: A literature review
Journal of Industrial Engineering and Management, 2018
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 non-deterministic 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...
Omega, 2017
This paper addresses the resource-constrained project scheduling problem with uncertain activity durations. An adaptive robust optimization model is proposed to derive the resource allocation decisions that minimize the worst-case makespan, under general polyhedral uncertainty sets. The properties of the model are analyzed, assuming that the activity durations are subject to interval uncertainty where the level of robustness is controlled by a protection factor related to the risk aversion of the decision maker. A general decomposition approach is proposed to solve the robust counterpart of the resource-constrained project scheduling problem, further tailored to address the uncertainty set with the protection factor. An extensive computational study is presented on benchmark instances adapted from the PSPLIB.
Timeslack-Based Techniques for Generating Robust Project Schedules Subject to Resource Uncertainty
SSRN Electronic Journal, 2000
The classical, deterministic resource-constrained project scheduling problem has been the subject of a great deal of research during the previous decades. This is not surprising given the high practical relevance of this scheduling problem. Nevertheless, extensions are needed to be better able to cope with situations arising in practice such as multiple activity execution modes, activity duration changes and resource breakdowns. In this paper we analytically determine the * impact of unexpected resource breakdowns on activity durations. Furthermore, using this information we develop an approach for inserting explicit idle time into the project schedule in order to protect it as well as possible from disruptions caused by resource unavailabilities. This strategy will be compared to a traditional simulation-based procedure and to a heuristic developed for the case of stochastic activity durations.
Springer eBooks, 2014
The purpose of this paper is to propose models for project scheduling when there is considerable uncertainty in the activity durations, to the extent that the decision maker cannot with confidence associate probabilities with the possible outcomes of a decision. Our modeling techniques stem from robust optimization, which is a theoretical framework that enables the decision maker to produce solutions that will have a reasonably good objective value under any likely input data scenario. We develop and implement a scenario-relaxation algorithm and a scenario-relaxation-based heuristic. The first algorithm produces optimal solutions but requires excessive running times even for medium-sized instances; the second algorithm produces high-quality solutions for medium-sized instances and outperforms two benchmark heuristics.
SSRN Electronic Journal, 2014
The purpose of this research is to develop a new procedure for generating a proactive baseline schedule for the resource constrained project scheduling problem. The main advantage of this new procedure is that it is completely independent of the reactive policy applied. This contrasts with the traditional methods that assume a predefined reactive policy. First, we define a new robustness measure, then we introduce a branchand-cut method for solving a sample average approximation of our original problem. In a computational experiment, we show that our procedure outperforms two other published methods, assuming different robustness measures.
Journal of Scheduling, 2008
Research concerning project planning under uncertainty has primarily focused on the stochastic resource-constrained project scheduling problem (stochastic RCPSP), an extension of the basic RCPSP, in which the assumption of deterministic activity durations is dropped. In this paper, we introduce a new variant of the RCPSP for which the uncertainty is modeled by means of resource availabilities that are subject to unforeseen breakdowns. Our objective is to build a robust schedule that meets the project due date and minimizes the schedule instability cost, defined as the expected weighted sum of the absolute deviations between the planned and actually realized activity starting times during project execution. We describe how stochastic resource breakdowns can be modeled, which reaction is recommended when a resource infeasibility occurs due to a breakdown and how one can protect the initial schedule from the adverse effects of potential breakdowns.
The Stochastic Resource-Constrained Project Scheduling Problem
Springer eBooks, 2014
Resource Constrained Project Scheduling Problem (RCPSP) is a well-known scheduling problem where aim is to optimize an objective under limited resources and activity constraints. From the real-life perspective, it has many applications such as construction, manufacturing, and R&D projects. It is shown by Blazewicz et al. [1] that RCPSP is NP-hard in the strong sense. Due to the nature of the problem itself, nature inspired algorithms are used extensively for the solution of the problem. Intelligent systems based on such algorithms can be used effectively if the proposed models can cover real life problems' complexities. Therefore, intelligent systems should be designed based on best fit models. Mainly RCPSP is modeled and solved in a deterministic environment where parameters are all assumed to be known [2]. Real life projects are consistent; production attributes are stochastic [3], and parameters are subject to change during execution of a project. Scheduling real life problems are subject to considerable uncertainties due to the dynamic nature of project environment [4]. These uncertainties and fluctuations may stem from project itself such as activity completion times, resource estimates, material delivery dates project externalities like severe weather conditions, owner's scope changes or imposed deadline changes. Thus, the limits of deterministic models are criticized by several researchers [5]. Contrary to the deterministic models, stochastic models portray the dynamic project environment with the assumption of varying project parameters. For the stochastic RCPSP, few researchers tried to model activity disruptions [6-7] and resource fluctuations separately [5]. Basic idea is to construct a
Time slack-based techniques for robust project scheduling subject to resource uncertainty
Annals of Operations Research, 2011
The resource-constrained project scheduling problem (RCPSP) has been the subject of a great deal of research during the previous decades. This is not surprising given the high practical relevance of this scheduling problem. Nevertheless, extensions are needed to be able to cope with situations arising in practice such as multiple activity execution modes, activity duration changes and resource breakdowns. In this paper we analytically determine the impact of unexpected resource breakdowns on activity durations. Furthermore, using this information we develop an approach for inserting explicit idle time into the project schedule in order to protect it as well as possible from disruptions caused by resource unavailabilities. This strategy will be compared to a traditional simulation-based procedure and to a heuristic developed for the case of stochastic activity durations.
Project scheduling under uncertainty: Survey and research potentials
European Journal of Operational Research, 2005
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, which is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling under uncertainty projects with deterministic network evolution structure.