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

Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler

Risk-Based Decision Making Support for Construction Corporate Resource Management

Competitive bidding typically challenges contractors to stay in business by reducing contingency and limiting profit margin, which imposes more prudent resource utilization and allocation decisions during both planning and construction phases of projects. Many of these decisions must be made considering uncertainties that affect resource production and construction performance through several factors such as weather, managerial practices, job-type, and market conditions, etc. Construction decision makers will therefore have varied approaches to deal with these uncertainties based on their risk utility or perception. This research presents the development of a model for investigating the impact of risk-based approaches on construction network outcomes. The current study contributes to development of a model that enables corporate managers to understand the impact of different resource utilization and sharing policies on the overall outcome of their project and to select among optimum planning solutions that satisfy their profit margin and capital limitations. This research also enables corporate decision makers to have more realistic estimates for the profitability of their company, and understand consequences of their decisions in short and long term. Findings of this research provide decision makers with different solutions for profitability of their corporation based on non-dominated optimal time-cost trade-offs, and also broader perspective on how overall time and budget limitations, as well as risk perceptions, can affect the decision-making process. The model is verified and the results are validated through acquiring data from actual large scale construction projects in South Florida