Uncertainty In Activity Durations Research Articles

Software project scheduling under activity duration uncertainty

Software project scheduling under activity duration uncertainty

Cash Flow Optimization for Renewable Energy Construction Projects with a New Approach to Critical Chain Scheduling

This study concerns the use of the critical chain method to schedule the construction of renewable energy facilities. The critical chain method is recognized as a useful project management tool, transforming a stochastic problem of uncertainty in activity durations into a deterministic one. However, this method has some shortcomings. There are no clear principles of grouping non-critical activities into feeding chains. Another ambiguity is sizing the feeding buffers with regard to the topology of the network model and the resulting dependencies between activities, located in different chains. As a result, it is often necessary to arbitrarily adjust the calculated sizes of feeding buffers before inserting them into the schedule. The authors present the new approach to sizing the time buffers in the schedule, enabling a quick assessment of the quality of a given solution variant and finding a solution that best meets the established criteria, conditions, and constraints. The essence of the presented approach is the two-step sizing of time buffers with the use of deterministic optimization and stochastic optimization techniques. Taking into account construction management needs, the optimization criteria are based on the construction project cash flow analysis. The effectiveness of the presented approach is illustrated by an example of developing a wind power plant construction schedule. According to the results, the presented approach ensures the protection of the scheduled completion date of the construction and the stability of the schedule.

Activity-travel scheduling in stochastic multi-state supernetworks with spatial and temporal correlations

Multi-state supernetworks are capable of representing activity-travel patterns at a high level of detail and thus are a powerful tool for activity-travel scheduling (ATS) of multidimensional choice facets. To alleviate the limitations of the common deterministic network representation, travel time and activity duration uncertainty has been incorporated in multi-state supernetworks. However, the extension unrealistically assumed that all uncertain components are independent. This study suggests an approach of ATS considering spatially and temporally correlated travel times and activity durations in stochastic time-dependent (STD) multi-state supernetworks. Support points are used as a representation of the stochasticity of the STD multi-state supernetwork. ATS is formulated as a pathfinding problem subject to space–time constraints based on recursive formulations. A series of numerical experiments is implemented to demonstrate the applicability of the suggested approach.

Integrating Schedule Risk Analysis with Multi-Skilled Resource Scheduling to Improve Resource-Constrained Project Scheduling Problems

Construction projects are planned in a complex and dynamic environment characterized by high risks and uncertainties amidst resource constraints. Assessing construction schedule risk facilitates informed decision-making, especially in a resource-constrained situation, and allows proactive actions to be taken so that project objectives are not jeopardized. This study presents a stochastic multiskilled resource scheduling (SMSRS) model for resource-constrained project scheduling problems (RCSPSP) considering the impact of risk and uncertainty on activity durations. The SMSRS model was developed by integrating a schedule risk analysis (SRA) model (developed in MS Excel) with an existing multiskilled resource scheduling (MSRS) algorithm for the development of a feasible and realistic schedule. The computational experiment carried out on three case projects using the proposed SMSRS model revealed an average percentage deviation of 10.50%, indicating the inherent risk and uncertainty in activity durations of the project schedule. The core contribution of the proposed SMSRS model is that it: (1) presents project practitioners with a simple tool for assessing the risks and uncertainty associated with resource-constrained project schedules so that necessary response actions can be taken to ensure project success; (2) provides the small-scale construction businesses with an affordable tool for evaluating schedule risk and developing a feasible and realistic project schedule.

Fuzzy Analytical Solution for Activity Duration Estimation under Uncertainty

AbstractThe dynamic nature of construction projects triggers uncertainty in activity durations. Several approaches such as interval analysis, probabilistic models, and fuzzy set theory have been pr...

A Statistical Method for Estimating Activity Uncertainty Parameters to Improve Project Forecasting

Just like any physical system, projects have entropy that must be managed by spending energy. The entropy is the project’s tendency to move to a state of disorder (schedule delays, cost overruns), and the energy process is an inherent part of any project management methodology. In order to manage the inherent uncertainty of these projects, accurate estimates (for durations, costs, resources, …) are crucial to make informed decisions. Without these estimates, managers have to fall back to their own intuition and experience, which are undoubtedly crucial for making decisions, but are are often subject to biases and hard to quantify. This paper builds further on two published calibration methods that aim to extract data from real projects and calibrate them to better estimate the parameters for the probability distributions of activity durations. Both methods rely on the lognormal distribution model to estimate uncertainty in activity durations and perform a sequence of statistical hypothesis tests that take the possible presence of two human biases into account. Based on these two existing methods, a new so-called statistical partitioning heuristic is presented that integrates the best elements of the two methods to further improve the accuracy of estimating the distribution of activity duration uncertainty. A computational experiment has been carried out on an empirical database of 83 empirical projects. The experiment shows that the new statistical partitioning method performs at least as good as, and often better than, the two existing calibration methods. The improvement will allow a better quantification of the activity duration uncertainty, which will eventually lead to a better prediction of the project schedule and more realistic expectations about the project outcomes. Consequently, the project manager will be able to better cope with the inherent uncertainty (entropy) of projects with a minimum managerial effort (energy).

A robust reliability-based scheduling for the maintenance activities during planned shutdown under uncertainty of activity duration

Scheduling of the maintenance activities is considered, in which the duration of activities is uncertain, delivery date of the maintenance shutdown is predefined, and activities are multi-mode. The problem is a multi-mode time-constrained project scheduling problem (MTCPSP). The glycol regeneration unit in a gas refinery is as the case study. Based on the effect of equipment or sub-equipment on the system reliability, the importance value of equipment and sub-equipment within the system is determined, and these values are used in a heuristic algorithm as a priority rule for choosing activities in the schedule. Outcomes demonstrate the performance of the algorithm in early or on time delivery of the planned shutdown and reveal that if during the execution of the maintenance shutdown, it is forced to shorten the shutdown period, at each time before the deadline, the plant can be started up with the maximum possible improvement of the system reliability.

Robust project scheduling integrated with materials ordering under activity duration uncertainty

This article addresses the project scheduling and the materials ordering problem (PSMOP) with uncertain activity durations. A two-stage optimisation approach is proposed to generate a robust schedule in order to protect the project execution from the duration uncertainty. First of all, a mixed-integer programming model under a deterministic environment is formulated to address this issue, which aims to minimise the corresponding costs and is solved by the genetic algorithm (GA). Second, a robust project scheduling integrated with materials ordering method (RPSMOM) is developed to build a proactive schedule on the basis of the baseline schedule generated by the given deterministic model. In this method, a novel index that measures the risk of activity delay (RAD) is suggested by taking the uncertain activity durations into account. Finally, computational experiments are conducted under different levels of activity duration uncertainty. The results demonstrate that the integrated proactive scheduling method is highly effective in decreasing the total cost and improving the robustness of the project schedule in most circumstances.

A robust scheduling for the multi-mode project scheduling problem with a given deadline under uncertainty of activity duration

The problem of this paper deals with the multi-mode project scheduling problem under uncertainty of activity duration where only the renewable resources are taken into account and a given deadline has to be met at the cost of recruiting additional resources. A heuristic algorithm is employed to solve this problem, and to maintain the robustness of the baseline schedule, the concept of critical chain project management (CCPM) is applied in which a new definition to resource buffer is considered. A simulation methodology is used to determine the size and location of resource buffers in the schedules in which three different buffer sizes and three different uncertainty levels are considered. Results and analysis of the simulation outcomes illustrate that resource buffers are useful and should be simulated by the CCPM schedules, as they help to decrease the total duration of the project during implementation and meet the deadline of the project with more assurance.

Important classes of reactions for the proactive and reactive resource-constrained project scheduling problem

The proactive and reactive resource-constrained project scheduling problem (PR-RCPSP), that has been introduced recently (Davari and Demeulemeester, 2017), deals with activity duration uncertainty in a very unique way. The optimal solution to an instance of the PR-RCPSP is a proactive and reactive policy (PR-policy) that is a combination of a baseline schedule and a set of required transitions (reactions). In this research, we introduce two interesting classes of reactions, namely the class of selection-based reactions and the class of buffer-based reactions, the latter in fact being a subset of the class of selection-based reactions. We also discuss the theoretical relevance of these two classes of reactions. We run some computational results and report the contributions of the selection-based reactions and the buffer-based reactions in the optimal solution. The results suggest that although both selection-based reactions and buffer-based reactions contribute largely in the construction of the optimal PR-policy, the contribution of the buffer-based reactions is of much greater importance. These results also indicate that the contributions of non-selection-based reactions (reactions that are not selection-based) and selection-but-not-buffer-based reactions (selection-based reactions that are not buffer-based) are very limited.

Measuring the Impact of Risk Events and Uncertainty in Activity Durations on Project Schedule

Construction projects are usually over budget and beyond their planned schedule. One possible reason for these situations is that proper risk management techniques are not applied during the planning and execution of projects. This paper introduces a method which uses the impacts of risk in project scheduling. A simple infrastructure project is considered for analysis. Uncertainty in activity durations is assigned using various statistical distributions. Further, certain risk events are added to the project using risk register. The impact of these uncertainties in activity durations and risk events are modeled and simulated in @Risk software. The software uses Monte Carlo simulations for risk analysis. The results show that the model can predict the impact of uncertainty in activity durations on the pro-ject completion time. The additional days added to the planned schedule because of occurrence of risk events can also be predicted with confidence. The results provide confirmation that risks associated with project schedules can be managed if properly accounted for

The Proactive and Reactive Resource-Constrained Project Scheduling Problem: The Crucial Role of Buffer-Based Reactions

The proactive and reactive resource-constrained project scheduling problem (PR-RCPSP), that has been introduced recently (Davari and Demeulemeester, 2016a), deals with activity duration uncertainty in a very unique way. The optimal solution to an instance of the PR-RCPSP is a proactive and reactive policy (PR-policy) that is a combination of a baseline schedule and a set of required transitions (reactions). In this research, we introduce two interesting classes of reactions, namely the class of selection-based reactions and the class of buffer-based reactions. We also discuss the theoretical relevance of these two classes of reactions. We run some computational results and report the contributions of the selection-based reactions and the buffer-based reactions in the optimal solution. The results suggest that although both selection-based reactions and buffer-based reactions contribute largely in the construction of the optimal PR-policy, the contribution of the buffer-based reactions is of much greater importance.

Robust Optimization for RCPSP Under Uncertainty

The aim of the present article is to optimize the robustness objective for the Resource-Constrained Project scheduling Problem (RCPSP) dealing with activity duration uncertainty. The studied robustness consists in minimizing the worst-case performance, referred to as the min-max robustness objective, among a set of initial scenarios. We propose an enhanced GRASP approach as a solution to the given scenario-based robust model. This approach is based on different priority rules in the construction phase and a forwardbackward heuristic in the improvement phase. We investigate two different benchmark data sets, the Patterson set and the PSPLIB J30 set. Experiments show that the proposed enhanced GRASP outperforms the basic procedure, and a based-evolutionary algorithm, in robustness optimization.

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, logistics and project management. RCPSP/max with durational uncertainty is a general model that can be used to represent 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 provide a principled approximation approach based on Sample Average Approximation (SAA) to compute proactive schedules for RCPSP/max with durational uncertainty. We further contribute an extension to SAA for improving scalability significantly without sacrificing on solution quality. Not only is our approach able to compute schedules at comparable 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 benchmark problems from the literature.

Minimizing the expected makespan of a project with stochastic activity durations under resource constraints

The resource-constrained project scheduling problem (RCPSP) has been widely studied. A fundamental assumption of the basic type of RCPSP is that activity durations are deterministic (i.e., they are known in advance). In reality, however, this is almost never the case. In this article, we illustrate why it is important to incorporate activity duration uncertainty, and develop an exact procedure to optimally solve the stochastic resource-constrained scheduling problem. A computational experiment shows that our approach works best when solving small- to medium-sized problem instances where activity durations have a moderate-to-high level of variability. For this setting, our model outperforms the existing state-of-the-art. In addition, we use our model to assess the optimality gap of existing heuristic approaches, and investigate the impact of making scheduling decisions also during the execution of an activity rather than only at the end of an activity.

A Fuzzy Scheduling Methodology for Energy R&D Projects

The efficient management of energy research and development (R&D) projects is important to reduce the required the development time and cost. However, each development project is unique and innovational in nature and the duration of activities involved in an energy R&D project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this paper is to develop a fuzzy scheduling methodology to deal with these problems. Fuzzy variables theory is used to model the uncertain and flexible temporal information. A fuzzy scheduling algorithm with depth-first search is developed to find the possible critical paths based on fuzzy expected value simulation. A numerical example with solar photovoltaics development project is used to illustrate the effectiveness of the methodology.

A Genetic Algorithm for Solving Multi-Mode Critical Chain Project Scheduling Problem

This paper studies the application of multi-mode critical chain project scheduling problem based on genetic algorithm. A model of multi-mode critical chain project scheduling problem is addressed by combining the characteristics of multi-mode project scheduling problem and the critical chain method. Comprehensively considering the renewable resource constraints, nonrenewable resource constraints, activities with multi-mode and the uncertainty of activity duration, a bi-level genetic algorithm to solve multi-mode critical chain project scheduling problem is proposed. We use the shared benchmark from PSPLIB to test this new genetic algorithm and the results show that the algorithm in this paper has obvious superiority over most currently existing approach.

A chaotic PSO approach to multi-mode resource-constraint project scheduling with uncertainty

In this paper, a new optimisation method based on chaotic particle swarm optimisation (CPSO) algorithm for solving multi-mode resource-constraint project scheduling problem (MRCPSP) under uncertainty is proposed. Fuzzy set theory is used to represent the uncertainties of activity duration. A searching technique using CPSO algorithm is adopted to search for the minimum of fuzzy project completion time. A case with uncertain activity duration is used to illustrate the performance of the proposed method. The result indicates that CPSO algorithm can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment.

An optimal construction resource leveling scheduling simulation model

To meet the physical limits of construction resources, to avoid day-to-day fluctuation in resource demands, and to maintain an even flow of application for construction resources, resource leveling is needed in the construction industry. Traditional resource leveling models assume activity durations to be deterministic. Nevertheless, activity duration may be uncertain, owing to variations in the overall environment, such as weather, site congestion, and productivity level. A new optimal construction resource leveling model is proposed in this paper, in which the combinative effects of both uncertain activity duration and resource leveling are taken into consideration. Monte Carlo simulation is used to model the uncertainties of activity duration. A searching technique using genetic algorithms (GAs) is then adopted to search for the impact of uncertain activity durations on the probabilistic optimal resource leveling indices. The model can effectively provide probabilistic optimal resource leveling indices for multiple construction resources subjected to the objective of resource leveling, and the impact of influence factors on the probabilistic resource-leveling scheduling problems.Key words: resource leveling, genetic algorithms, simulation, probabilistic scheduling.

A fuzzy project scheduling approach to minimize schedule risk for product development

The efficient management of product development projects is important to reduce the required development time and cost. However, each project is unique in nature and the duration of activities involved in a project often cannot be predicted accurately. The uncertainty of activity duration may lead to incorrect scheduling decisions. The objective of this research is to develop a fuzzy scheduling methodology to deal with this problem. Possibility theory is used to model the uncertain and flexible temporal information. The concept of schedule risk is proposed to evaluate the schedule performance. A fuzzy beam search algorithm is developed to determine a schedule with the minimum schedule risk and the start time of each activity is selected to maximize the minimum satisfaction degrees of all temporal constraints. In addition, the properties of schedule risk are also discussed. We show that the proposed methodology can assist project managers in selecting a schedule with the least possibility of being late in an uncertain scheduling environment. An example with an electronic product development project is used to illustrate the developed approach.