Sample Term Paper On Construction Scheduling Methods Compared
Estimating errors on project-related activities may hamper the attainment of a milestone and interfere with the rest of the activities in a project schedule. The delay and disruption resulting from inaccurate estimation may culminate in project failure or at the very best to failure of project management; untimely delivery of the project, within the set budget, and according to project specifications (Khamooshi & Cioffi 2013). The precision and the subsequent reliability of estimates should be put into consideration more thoroughly when making project schedules.
Comparisons between Critical Path Method (CPM) and Probabilistic Program Evaluation and Review Technique (PERT)
Two divergent methods are commonly used to plan project durations, which include the deterministic critical path method (CPM) and probabilistic program evaluation and review (PERT) or Monte Carlo approach. These techniques use single-valued task durations to formulate a baseline schedule, that is, the schedule against which the actual results must be realized. The major problem with current scheduling methods, particularly the precise and heuristic ones, is that they usually fail to provide effective solutions to complex real-world problems and do not allow for practical applications and construction constraints (Jaśkowski & Sobotka, 2006). For that reason, the scheduling problem is normally perceived as the domain of integer programming.
According to Hegazy and Menesi (2012), the critical path method (CPM) is an indispensable instrument for decision making in project control. Nonetheless, float calculation errors experienced when project managers are working on complex schedules inhibits its ability to provide accurate decision support required for project control. Basic CPM computations for scheduling are rather easy and straightforward and generate neat results as planned schedules to be used during execution. On the other hand, many ‘as-built’ schedules of finished projects are entangled with complex delays, relations, resource problems, execution events, and changes, thus, making them extremely hard to analyze.
Integration of Critical Path Segments (CPS) Method of Scheduling with CPM
In order to deal with CPM drawbacks, a critical path segments (CPS) method of scheduling can be used. Since both CPM and CPS yield anticipated schedules when complex project paths are evaded, the gains of CPS can be realized when it is used for project control that encompasses comprehensive as-built documentation, remedial actions, and forensic investigation of complex schedules. As a result, CPS can be incorporated with CPM to improve the efficiency of project control.
While doing CPM computations is quite simple, CPM-based scheduling is somewhat more challenging. At the planning phase, the CPM network may comprise complex relationships, which create difficulties for the scheduling process. In addition, the CPM algorithm lacks an appropriate formulation to account for the several factors that influence project outcomes such as time and resource constraints (Hegazy & Menesi, 2010). With CPS scheduling, float calculations are done through the forward pass process, and without a backward pass. When performing the forward pass process, project managers first identify all the paths present in the network and then calculate the total float of every path. Every time segment is then apportioned a total float value, which is computed as the minimum value of all the path floats in the network.
In contrast to the conventional denotation of activity duration as an endless block of time that symbolizes the activity duration, the CPS characterizes each activity as a set of different successive time segments that constitute the whole duration of the activity (Hegazy & Menesi, 2010). The CPS formulation facilitates resource allocation to generate more practical and realistic schedules because it will allow for the halting and restarting of all individual time segments when and if required so that the constrained resources are used optimally. As such, the CPM is most appropriate for planning purposes prior to any construction works whereas the effectiveness of the CPS can be best achieved at the project control phase (Hegazy & Menesi, 2010).
Distinguishing between Program Evaluation and Review Technique (PERT) and Monte Carlo approach
With PERT, by making full use of the central-limit theorem and applying average activity durations from the expected distributions of duration periods for each activity on the critical path, a project manager can assess the probabilities that associate with completion times less or more than the scheduled project duration, which is based on those average times (Khamooshi & Cioffi 2013). A Monte Carlo simulation, even though, a more detailed computation that removes PERT’s merge-bias challenge and its lack of knowledge pertaining to near-critical paths, uses what is a similar plan of execution. What is more, a distribution is assumed for each activity on the critical path, and the possibilities of several project durations are established. Both PERT and the Monte Carlo simulation fail to take into consideration the reality of these decreased probabilities; and so, they tend to overstate greatly the probabilities of project durations relative to the scheduled duration (Khamooshi & Cioffi 2013).
Need for Sequence Analysis: Dependency Structure Matrix (DSM)
Traditional tools such as CPM/PERT are not appropriate for sequence analysis since they are graphic descriptions of task flows rather than information flow models. For that reason, researchers have identified the Dependency Structure Matrix (DSM) as a powerful aid in planning the task sequences by expressing the feedback loops and managing information exchanges (Maheswari, 2005). A major benefit of the matrix representation as compared to other decision support tools is its superior level of compactness, and ability to produce a systematic roadmap for the components, which is easy to read no matter the size. It clearly indicates where interdependence arises and processes to identify and evaluate sequence choices. DSM offers a better planning framework or methodology for use in the managerial decisions.
Conventionally, all information related to a project network is retrieved from a certain upstream task only after it has reached its completion and in the same manner any downstream task starts to implement, after all, the information is obtained at the commencement of the activity. In reality, however, information can be provided before the completion of the preceding task, and the successor task can continue its implementation on the basis of this information. As a result, it needs not wait until the preceding activity is fully complete or the successor to commence leading to natural overlap. Although DSM is a remarkable tool for planning, it has a serious weakness as a stand-alone Project Management (PM) technique in indicating the timescale. For that reason, many researchers have combined DSM with PM tool to overcome this drawback. Because incorporating DSM with PM tool cannot still mitigate most scheduling problems, research work in consolidating DSM as a stand-alone PM approach is continuing. One of these problems involves modeling and approximating project duration for normal and natural overlaps.
Unified Scheduling Method (USM) Approach for Project Scheduling
Unified scheduling method (USM) is constrained not only by a probabilistic forecast of total project cost and duration but also schedule development (Khamooshi & Cioffi 2013). Single estimates with linked reliabilities produce the information required for the development of more practical, steady schedules. To begin with, the reliability of the estimates will be represented unambiguously, therefore, bestowing more responsibility on individuals who take part in the development of plans and schedules. Second, reliability creates an avenue for a better analysis of the uncertainties related to the cost and duration of the project. Third, USM offers an active approach to estimating schedule and budget contingencies directly, through the use of either analytic binary estimation or numerical simulation, which eradicates the need for numerical simulation. As the project goes on, schedule and cost buffers can be adjusted easily, making USM a more responsive approach to scheduling.
Using exact algorithms to tackle difficult practical issues is impossible since of the amount of time required to complete the calculations and the restricted memory capability of computers. Therefore, several estimation methods that use the heuristic approach, which is further categorized into two, namely, specialized heuristics and metaheuristics. Specialized heuristics are applied to solving only one maximization or minimization problem at a time. Priority heuristics is one of the most common heuristics used for solving scheduling problems and is readily available as project scheduling software. Priority heuristics comprises two stages. In the first stage, a so-called priority list, a list of processes, is prepared and arranged with regards to declining values of priority computed on the basis of an assumed rule. In the second stage, the start and finish durations of these processes are computed so as to make all the constraints manageable. In this stage, one of the two approaches to project scheduling can be used: parallel or serial, which have different ways of handling resources conflicts.
In order to solve single-criterion maximization or minimization problems encountered during project scheduling, metaheuristic algorithms too can be used. They define only a definite pattern of optimization system, which must be adjustable for specific applications (Jaśkowski, & Sobotka 2006). The most commonly used metaheuristic approaches include simulated annealing, evolutionary algorithms, and taboo search method. Both simulated annealing and taboo search techniques are associated with the group of the neighborhood. By examination the area of practical solutions needed to move from a present solution to a neighboring one, understanding the definition of “neighborhood”, and the manner in which neighboring solutions were generated.
Hegazy, T., & Menesi, W. (2010). Critical path segments scheduling technique. Journal of Construction Engineering and Management, 136(10), 1078-1085.
Hegazy, T., & Menesi, W. (2012). Enhancing the critical path segments scheduling technique for project control. Canadian Journal of Civil Engineering, 39, 968–977.
Jaśkowski, P., & Sobotka, A. (2006). Scheduling construction projects using evolutionary algorithm. Journal of Construction Engineering and Management, 132(8), 861-870.
Khamooshi, H., & Cioffi, D. F. (2013). Uncertainty in task duration and cost estimates: Fusion of probabilistic forecasts and deterministic scheduling. Journal of Construction Engineering and Management, 139(5), 488-497.
Maheswari, J. U. (2005). Project scheduling using dependency structure matrix. International Journal of Project Management, 23, 223–230.
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