Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking

When shortening a project’s duration, activity crashing, fast-tracking and substitution are the three most commonly employed compression techniques. Crashing generally involves allocating extra resources to an activity with the intention of reducing its duration. To date, the activity time-cost relationship has for the most part been assumed to be linear, however, a few studies have suggested that this is not necessarily the case in practice. This paper proposes two non-linear theoretical models which assume either collaborative or non-collaborative resources. These models closely depict the two most common situations occurring during construction projects. The advantages of these models are that they allow for both discrete and continuous, as well as deterministic and stochastic configurations. Additionally, the quantity of resources required for crashing the activity can be quantified. Comparisons between the models and another recent fast-tracking model from the literature are discussed, and a Genetic Algorithm is implemented for a fictitious application example involving both compression techniques

Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking

[EN] When shortening a project's duration, activity crashing, fast-tracking and substitution are the three most commonly employed compression techniques. Crashing generally involves allocating extra resources to an activity with the intention of reducing its duration. To date, the activity time-cost relationship has for the most part been assumed to be linear, however, a few studies have suggested that this is not necessarily the case in practice. This paper proposes two non-linear theoretical models which assume either collaborative or non-collaborative resources. These models closely depict the two most common situations occurring during construction projects. The advantages of these models are that they allow for both discrete and continuous, as well as deterministic and stochastic configurations. Additionally, the quantity of resources required for crashing the activity can be quantified. Comparisons between the models and another recent fast-tracking model from the literature are discussed, and a Genetic Algorithm is implemented for a fictitious application example involving both compression techniques.This research was supported by the CIOB Bowen Jenkins Legacy Research Fund (reference BLJ2016/BJL.01) and by NERC under the Environmental Risks to Infrastructure Innovation Programme (reference NE/R008876/1) at the University of Reading.Ballesteros-Pérez, P.; Elamrousy, KM.; González-Cruz, M. (2019). Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking. Automation in Construction. 97:229-240. https://doi.org/10.1016/j.autcon.2018.11.0012292409

Modelling the boundaries of project fast-tracking

Fast-tracking a project involves carrying out sequential activities in parallel, partially overriding their original order of precedence, to reduce the overall project duration. The current predominant mathematical models of fast-tracking are based on the concepts of activity sensitivity, evolution, dependency and, sometimes, information exchange uncertainty, and aim to determine optimum activity overlaps. However, these models require some subjective inputs from the scheduler and most of them neglect the merge event bias. In this paper, a stochastic model for schedule fast-tracking is proposed. Relevant findings highlight the existence of a pseudo-physical barrier that suggests that the possibility of shortening a schedule by more than a quarter of its original duration is highly unlikely. The explicit non-linear relationship between cost and overlap has also been quantified for the first time. Finally, manual calculations using the new model are compared with results from a Genetic Algorithm through a case study

An improved time-cost trade-off model with optimal labor productivity

Optimization of the time-cost trade off (TCT) has received considerable attention for several decades. However, few studies have considered improving performance/productivity of existing crews. To shorten the gap to real-world applications, this study presents an improved TCT model that considers variable productivity using genetic algorithms (GAs). Through an illustrative case and a real world case, the results demonstrate that improving labor productivity of selected activities by allocating existing crews and management can yield an optimized solution. As such, a decision maker can implement a better optimized technique to reduce a project duration under budget while reducing the risk of liquidated damages. The main contribution of this study is to apply managerial improvement of labor productivity to TCT optimization, the project duration can be reduced owing to improved productivity of existing crews rather than inefficient overmanning, overlapping or costly substitution. In the end, three important managerial insights are presented and future research is recommended

Comprehensive CP Optimization for Dynamic Scheduling in Construction

Delays and cost overruns are common facts in construction projects due to its increasing complexity, the day-to-day dynamic changes, the stricter execution constraints, and the general lack of efficient scheduling tools to support the optimization of construction plans. Currently, many scheduling tools and techniques are available, in addition to a large body of literature that focus on schedule optimization. Such tools and techniques, however, do not adequately represent or incorporate various practical decisions and constraints, nor provide the project manager with the ability to examine the combinations of actions in order to either plan or bring the project back within the constraints. This research enhances the schedule optimization research by efficiently modeling real-life decisions and constraints, and develops a framework to optimize planning and corrective-action decisions; dynamically before and during construction. The development of the proposed framework starts with a basic model that suits the schedule optimization decisions at the preconstruction stage. This model is then extended to a generic model that accommodates the dynamic schedule optimization needs during construction. The enhancements and extensions are formulated in a generic mathematical formulation to optimize the schedule’s decisions at any stage. This formulation integrates a wide range of scheduling options (e.g., linear crashing, activity multimodes, overlapping, and multipath networks), and incorporates the project manager’s preferences about the corrective-action decisions’ implementation. The formulation also considers a variety of practical constraints (e.g., variable resource availability, correlated modes, and intermediate milestones); and uses a multi-objective optimization to tradeoff among the project time, cost, resources, and permissible schedule changes during construction. Based on the mathematical formulation, the proposed framework was then coded using the advanced v constraint programming tool “IBM ILOG CPLEX Optimization Studio”. To validate the model, multiple experiments on four case studies were used to prove the functionality, practicality, and its better representation of real-life construction challenges. Two of these case studies are taken from the literature to prove the ability of the comprehensive model to achieve better solutions. Construction experts were also consulted at multiple stages of this work to investigate the relevance of the framework. Introducing the proposed framework as an add-on to standard project management software is expected to change the practitioners’ perception that optimization is a theoretical and complex tool. Therefore, it helps to present optimization as a useful decision support tool for construction scheduling

A rough-cut capacity planning model with overlapping

In the early phases of projects, capacity planning is performed to assess the feasibility of the project in terms of delivery date, resource usage and cost. This tactical approach relies on an aggregated representation of tasks in work packages. At this level, aggressive project duration objectives are achieved by adopting work package overlapping policies that affect both workload and resource usage. In this article, we propose a mixed-time MILP model for project capacity planning with different possibilities for overlapping levels between work packages. In the model, the planning time horizon is divided into time buckets used to evaluate resource usage, while starting and ending times for work packages are continuous. The model was tested on a benchmark of 5 sets of 450 theoretical instances each. More than half of the tested instances were solved to optimality within 500 s. Results also show that, while overlapping is more beneficial for accelerating project delivery times, it can still have a positive impact on project cost by allowing a better distribution of workload. Finally, overlapping options seem to have less influence on the performance of the model than project slack or number of work packages

A rough-cut capacity planning model with overlapping

In the early phases of projects, capacity planning is performed to assess the feasibility of the project in terms of delivery date, resource usage and cost. This tactical approach relies on an aggregated representation of tasks in work packages. At this level, aggressive project duration objectives are achieved by adopting work package overlapping policies that affect both workload and resource usage. In this article, we propose a mixed-time MILP model for project capacity planning with different possibilities for overlapping levels between work packages. In the model, the planning time horizon is divided into time buckets used to evaluate resource usage, while starting and ending times for work packages are continuous. The model was tested on a benchmark of 5 sets of 450 theoretical instances each. More than half of the tested instances were solved to optimality within 500 s. Results also show that, while overlapping is more beneficial for accelerating project delivery times, it can still have a positive impact on project cost by allowing a better distribution of workload. Finally, overlapping options seem to have less influence on the performance of the model than project slack or number of work packages

A Method for Improving Overlapping of Testing and Design

Testing is a critical activity in product development. The academic literature provides limited insight about overlapping between upstream testing and downstream design tasks, especially in considering the qualitative differences between activities that are overlapped. In general, the existing literature treats two overlapped sequential activities as similar, and suggests optimal overlapping policies, techniques, and time–cost assessment. However, this case study-based research identifies that the overlapping of upstream testing with downstream design activities has different characteristics than the overlapping of two design activities. This paper first analyzes the characteristics that affect the overlapping of upstream testing and downstream design activities, and then proposes a method to reduce the time of rework in cases where the upstream testing is overlapped with subsequent redesign phases

Multi-objective time-cost optimization using Cobb-Douglas production function and hybrid genetic algorithm

 Existing research on construction time-cost tradeoff issues rarely explore the origin of the crashing cost. Crashing cost function was either assumed without much justification, or came from historical data of some real pro­jects. As a result the conclusions of the papers can hardly be used to guide allocations of labor and equipment resources respectively. The authors believe Cobb-Douglas function provides a much-needed piece to modeling the cost functions in the construction time-cost tradeoff problem during the crashing process. We believe this new perspective fills a gap of existing time-cost tradeoff research by considering project duration, labor and equipment cost as parameters of the Cobb- Douglas production function. A case study was presented to show how the proposed framework works. Our conclusion is that introducing Cobb-Douglas function into time-cost tradeoff problem provides us extra capacity to further identify the optimal allocations of labor and equipment resources during crashing