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

Fostering Project Scheduling and Controlling Risk Management

Deployment of emerging technologies and rapid change in industries has created a lot of risk for initiating the new projects. Many techniques and suggestions have been introduced but still lack the gap from various prospective. This paper proposes a reliable project scheduling approach. The objectives of project scheduling approach are to focus on critical chain schedule and risk management. Several risks and reservations exist in projects. These critical reservations may not only foil the projects to be finished within time limit and budget, but also degrades the quality, and operational process. In the proposed approach, the potential risks of project are critically analyzed. To overcome these potential risks, fuzzy failure mode and effect analysis (FMEA) is introduced. In addition, several affects of each risk against each activity are evaluated. We use Monte Carlo simulation that helps to calculate the total time of project. Our approach helps to control risk mitigation that is determined using event tree analysis and fault tree analysis. We also implement distribute critical chain schedule for reliable scheduling that makes the project to be implemented within defined plan and schedule. Finally, adaptive procedure with density (APD) is deployed to get reasonable feeding buffer time and project buffer time

Generalization of the critical chain method supporting the management of projects with a high degree of uncertainty and imperfect information

In the critical chain method the fundamental notion is the project buffer, and its length is based on task estimation risk. This estimation is almost never unequivocal. If it is not correct, the whole method may turn out to be ineffective. Different experts may have different opinions about this risk. The critical chain method allows to take into account the opinion of only one expert, which may seriously falsify the image of the project situation. This paper proposes a generalization of the critical chain method allowing the use of the opinions of several experts – both while planning a project and while controlling it. Thanks to such an approach, in each phase of project planning and control we are aware of the opinions of various experts as to the correctness of the deadline which was agreed upon with the customer, as to the chances of meeting this deadline and as to the necessity of strengthening project control or introducing changes into the project.time management, risk management, project, critical chain.

Solution and quality robust project scheduling: a methodological framework.

The vast majority of the research efforts in project scheduling over the past several years has concentrated on the development of exact and suboptimal procedures for the generation of a baseline schedule assuming complete information and a deterministic environment. During execution, however, projects may be the subject of considerable uncertainty, which may lead to numerous schedule disruptions. Predictive-reactive scheduling refers to the process where a baseline schedule is developed prior to the start of the project and updated if necessary during project execution. It is the objective of this paper to review possible procedures for the generation of proactive (robust) schedules, which are as well as possible protected against schedule disruptions, and for the deployment of reactive scheduling procedures that may be used to revise or re-optimize the baseline schedule when unexpected events occur. We also offer a methodological framework that should allow project management to identify the proper scheduling methodology for different project scheduling environments. Finally, we survey the basics of Critical Chain scheduling and indicate in which environments it is useful.Framework; Information; Management; Processes; Project management; Project scheduling; Project scheduling under uncertainty; Stability; Robust scheduling; Quality; Scheduling; Stability; Uncertainty;

Comparing Critical Chain Project Managemenet with Critical Path Method: A Case Study

Scheduling is a major task in project management. The current scheduling technique, Critical Path Method (CPM), has been widely applied for several decades, but a large number of projects fail to be completed on time and schedule delays occur in many projects. This raises question about the validity of the current project scheduling system. Critical Chain Project Management (CCPM), derived from Theory of Constraints, is a relatively new alternative approach toward scheduling projects. This study compared CCPM and CPM to determine which scheduling method delivers a shorter project duration and has a better usage of resources. A scheduling software called ProChain was used to reschedule a CPM based construction project using CCPM. The study concluded that the CCPM has the possibility to deliver shorter project duration and better resource usage in comparison to CPM. It was revealed that ProChain has limitation in the process of transforming a CPM schedule to a CCPM schedule. For example, ProChain treats any tasks without any predecessor as a project terminating task and puts a project buffer after it

Railway scheduling reduces the expected project makespan.

The Critical Chain Scheduling and Buffer Management (CC/BM) methodology, proposed by Goldratt (1997), introduced the concepts of feeding buffers, project buffers and resource buffers as well as the roadrunner mentality. This last concept, in which activities are started as soon as possible, was introduced in order to speed up projects by taking advantage of predecessors finishing early. Later on, the railway scheduling concept of never starting activities earlier than planned was introduced as a way to increase the stability of the project, typically at the cost of an increase in the expected project makespan. In this paper, we will indicate a realistic situation in which railway scheduling improves both the stability and the expected project makespan over roadrunner scheduling.Railway scheduling; Roadrunner scheduling; Feeding buffer; Priority list; Resource availability;

Buffer Techniques For Stochastic Resource Constrained Project Scheduling With Stochastic Task Insertions Problems

Project managers are faced with the challenging task of managing an environment filled with uncertainties that may lead to multiple disruptions during project execution. In particular, they are frequently confronted with planning for routine and non-routine unplanned work: known, identified, tasks that may or may not occur depending upon various, often unpredictable, factors. This problem is known as the stochastic task insertion problem, where tasks of deterministic duration occur stochastically. Traditionally, project managers may include an extra margin within deterministic task times or an extra time buffer may be allotted at the end of the project schedule to protect the final project completion milestone. Little scientific guidance is available to better integrate buffers strategically into the project schedule. Motivated by the Critical Chain and Buffer Management approach of Goldratt, this research identifies, defines, and demonstrates new buffer sizing techniques to improve project duration and stability metrics associated with the stochastic resource constrained project scheduling problem with stochastic task insertions. Specifically, this research defines and compares partial buffer sizing strategies for projects with varying levels of resource and network complexity factors as well as the level and location of the stochastically occurring tasks. Several project metrics may be impacted by the stochastic occurrence or non-occurrence of a task such as the project makespan and the project stability. New duration and stability metrics are developed in this research and are used to evaluate the effectiveness of the proposed buffer sizing techniques. These robustness measures are computed through the comparison of the characteristics of the initial schedule (termed the infeasible base schedule), a modified base schedule (or as-run schedule) and an optimized version of the base schedule (or perfect knowledge schedule). Seven new buffer sizing techniques are introduced in this research. Three are based on a fixed percentage of task duration and the remaining four provide variable buffer sizes based upon the location of the stochastic task in the schedule and knowledge of the task stochasticity characteristic. Experimental analysis shows that partial buffering produces improvements in the project stability and duration metrics when compared to other baseline scheduling approaches. Three of the new partial buffering techniques produced improvements in project metrics. One of these partial buffers was based on a fixed percentage of task duration and the other two used a variable buffer size based on knowledge of the location of the task in the project network. This research provides project schedulers with new partial buffering techniques and recommendations for the type of partial buffering technique that should be utilized when project duration and stability performance improvements are desired. When a project scheduler can identify potential unplanned work and where it might occur, the use of these partial buffer techniques will yield a better estimated makespan. Furthermore, it will result in less disruption to the planned schedule and minimize the amount of time that specific tasks will have to move to accommodate the unplanned tasks

Integrating a Procurement Management Process into Critical Chain Project Management (CCPM): A Case-Study on Oil and Gas Projects, the Piping Process

Engineering, Procurement, and Construction (EPC) of oil and gas megaprojects often experience cost overruns due to substantial schedule delays. One of the greatest causes of these overruns is the mismanagement of the project schedule, with the piping works (prefabrication and installation) occupying a majority of that schedule. As such, an effective methodology for scheduling, planning, and controlling of piping activities is essential for project success. To meet this need, this study used the Critical Chain Project Management (CCPM) to develop a piping construction delay prevention methodology, incorporating material procurement processes for EPC megaprojects. Recent studies indicate that the traditional scheduling method used on oil and gas mega projects has critical limitations regarding resource scarcity, calculation of activity duration, and dealing with uncertainties. To overcome these limitations, the Theory of Constraints-based CCPM was proposed and implemented to provide schedule buffers management. Nonexistent in literature, and of critical importance, is this paper's focus on the resource buffer, representing material uncertainty and management. Furthermore, this paper presents a step-by-step process and flow chart for project, construction, and material managers to effectively manage a resource buffer through the CCPM process. This study extends the knowledge of traditional resource buffers in CCPM to improve material and procurement management, thus avoiding the shortage of piping materials and minimizing delays. The resultant process was validated by both deterministic and probabilistic schedule analysis through two case studies of a crude pump unit and propylene compressor installation at a Middle Eastern Refinery Plant Installation. The results show that the CCPM method effectively handles uncertainty, reducing the duration of piping works construction by about a 35% when compared to the traditional method. Furthermore, the results show that, in not considering material uncertainty (resource buffers), projects schedules have the potential for approximately a 5% schedule growth with the accompanying delay charges. The findings have far-reaching applications for both oil and gas and other sectors. This CCPM case-study exemplifies that the material management method represents an opportunity for industry to administrate pipeline installation projects more effectively, eliminate project duration extension, develop schedule-based risk mitigation measures pre-construction, and enable project teams to efficiently manage limited human and material resources.111sciessciscopu

Resource Allocation Optimization in Critical Chain Method

The paper presents resource allocation optimization in Critical Chain Project Management (CCPM). The cheapest project schedule is searched with respect to time constraints. The algorithm originally developed for the hardware-software co-design of heterogeneous distributed systems is adapted to work with human resources and CCPM method. The results of the optimization showed significant efficiency of the algorithm in comparison with a greedy algorithm. On average, the optimization gives 14.10% of cost reduction using the same number of resources. The gain varies depending on the number of resources and the time constraints. Advantages and disadvantages of such an approach are also discussed

Stochastic Resource Constrained Project Scheduling With Stochastic Task Insertion Problems

The area of focus for this research is the Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) with Stochastic Task Insertion (STI). The STI problem is a specific form of the SRCPSP, which may be considered to be a cross between two types of problems in the general form: the Stochastic Project Scheduling Problem, and the Resource Constrained Project Scheduling Problem. The stochastic nature of this problem is in the occurrence/non-occurrence of tasks with deterministic duration. Researchers Selim (2002) and Grey (2007) laid the groundwork for the research on this problem. Selim (2002) developed a set of robustness metrics and used these to evaluate two initial baseline (predictive) scheduling techniques, optimistic (0% buffer) and pessimistic (100% buffer), where none or all of the stochastic tasks were scheduled, respectively. Grey (2007) expanded the research by developing a new partial buffering strategy for the initial baseline predictive schedule for this problem and found the partial buffering strategy to be superior to Selim s extreme buffering approach. The current research continues this work by focusing on resource aspects of the problem, new buffering approaches, and a new rescheduling method. If resource usage is important to project managers, then a set of metrics that describes changes to the resource flow would be important to measure between the initial baseline predictive schedule and the final as-run schedule. Two new sets of resource metrics were constructed regarding resource utilization and resource flow. Using these new metrics, as well as the Selim/Grey metrics, a new buffering approach was developed that used resource information to size the buffers. The resource-sized buffers did not show to have significant improvement over Grey s 50% buffer used as a benchmark. The new resource metrics were used to validate that the 50% buffering strategy is superior to the 0% or 100% buffering by Selim. Recognizing that partial buffers appear to be the most promising initial baseline development approach for STI problems, and understanding that experienced project managers may be able to predict stochastic probabilities based on prior projects, the next phase of the research developed a new set of buffering strategies where buffers are inserted that are proportional to the probability of occurrence. The results of this proportional buffering strategy were very positive, with the majority of the metrics (both robustness and resource), except for stability metrics, improved by using the proportional buffer. Finally, it was recognized that all research thus far for the SRCPSP with STI focused solely on the development of predictive schedules. Therefore, the final phase of this research developed a new reactive strategy that tested three different rescheduling points during schedule eventuation when a complete rescheduling of the latter portion of the schedule would occur. The results of this new reactive technique indicate that rescheduling improves the schedule performance in only a few metrics under very specific network characteristics (those networks with the least restrictive parameters). This research was conducted with extensive use of Base SAS v9.2 combined with SAS/OR procedures to solve project networks, solve resource flow problems, and implement reactive scheduling heuristics. Additionally, Base SAS code was paired with Visual Basic for Applications in Excel 2003 to implement an automated Gantt chart generator that provided visual inspection for validation of the repair heuristics. The results of this research when combined with the results of Selim and Grey provide strong guidance for project managers regarding how to develop baseline predictive schedules and how to reschedule the project as stochastic tasks (e.g. unplanned work) do or do not occur. Specifically, the results and recommendations are provided in a summary tabular format that describes the recommended initial baseline development approach if a project manager has a good idea of the level and location of the stochasticity for the network, highlights two cases where rescheduling during schedule eventuation may be beneficial, and shows when buffering proportional to the probability of occurrence is recommended, or not recommended, or the cases where the evidence is inconclusive