Minimizing Project Duration Research Articles

Optimizing the utilization of multiple labor shifts in construction projects

Evening and night shifts are often used in construction projects to accelerate schedules despite their negative impacts on construction cost and productivity. In order to minimize these negative impacts, this paper presents a multi-objective optimization model for scheduling multiple labor shifts in construction projects. The optimization model incorporates (1) an initialization module that initializes the scheduling optimization computations; (2) a scheduling module that develops practical multiple shift schedules and evaluates the impacts of decision variables on project performance; and (3) a multi-objective genetic algorithm module that searches for and identifies optimal/near optimal tradeoffs among minimizing project duration, reducing cost, and minimizing labor utilization in evening and night shifts. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the planning and scheduling of multiple shifts in construction projects while complying with all job logic and labor availability constraints.

Multi‐objective particle swarm optimization for construction time‐cost tradeoff problems

Reduction in construction project duration is generally linked to additional cost due to more expensive resources required. Hence tradeoff between time and cost is crucial to the efficiency of a construction project. The time‐cost tradeoff (TCT) issue has been studied through various multi‐objective optimization methodologies to determine an optimal set of activity methods with the objectives of minimizing project duration and total cost. A multi‐objective particle swarm optimization that adopts a combined scheme for determining the global best of each particle is presented for solving the TCT problem. The candidate TCT solutions in terms of a set of construction methods for activities are represented through the multidimensional particles. The framework of the combined scheme‐based multi‐objective particle swarm optimization (CSMOPSO) is developed. Computational analyses are conducted to investigate the performance of the CSMOPSO, including comparison with other methods. This study is expected to provide an alternative solving methodology for the TCT problem and help contractors or engineers plan construction methods with optimal time–cost tradeoff.

Automated trade‐off between time and cost in planning repetitive construction projects

An automated model is developed to support the optimization of the planning and scheduling of repetitive construction projects. The model provides the capability of optimizing two important objectives commonly sought in scheduling repetitive construction projects: minimizing project duration; and minimizing project cost. The model performs this multi‐objective optimization using a genetic algorithm approach. The output of the model is a set of optimal solutions that represent the trade‐off between time and cost in planning repetitive construction projects. Furthermore, the model can be utilized to find a single scheduling solution that provides the minimum overall project cost by simply adding project indirect cost to the obtained project direct cost for each of the obtained scheduling solutions on the Pareto optimal curve. Other important time‐related costs are also considered in the model including: early completion incentives, late completion penalties and lane rental costs. Providing the planners of repetitive construction projects with an automated set of optimal time–cost trade‐off solutions should contribute to cost‐effective and speedy delivery of this type of construction project. An application example is analysed to illustrate the use of the model and demonstrate its capabilities in generating optimal trade‐off solutions between minimizing the project time and cost for repetitive construction projects.

Activity Prioritization Under Resource Constraints Using a Utility Index Method

Resource availability constraints are a typical real-life construction scheduling problem; a problem that limits a constructor's ability to execute and deliver a project as originally planned. It is, thus, imperative that developed project schedules should have not only well-thought project logic networks (successor/predecessor information and activity durations) but also resource assignments (including cost) for each activity in the network so that the effects of resource constraints can effectively be accounted for. The paper presents a new approach to resource-constrained scheduling that allows for activity prioritization when a project is subject to limited resources. The methodology proposed is based on a utility index, hereby defined as the ratio of the number of required resources for a specific activity to the total number of required resources among competing activities. A dynamic programming technique is adopted to maximize the utility value for each activity so that the resource allocation among competing activities, as suggested by the method, results in the minimum overall project duration.

Productivity Scheduling Method: Linear Schedule Analysis with Singularity Functions

This paper describes a new integrated method of linear schedule analysis using singularity functions. These functions have previously been used for structural analysis and are newly applied to scheduling. Linear schedules combine information on time and amount of work for each activity. A general model is presented with which activities and their buffers can be mathematically described in detail. The algorithm of the new method forms the body of the paper, including the steps of setting up initial equations, calculating pairwise differences between them, differentiating these to obtain the location of any minima, and deriving the final equations. The algorithm consolidates the linear schedule under consideration of all constraints and, thus, automatically generates the minimum overall project duration. The model distinguishes time and amount buffers, which bears implications for the definition and derivation of the critical path. Future research work will address float and resource analysis using the new model.

A heuristic project scheduling approach for quick response to maritime disaster rescue

Maritime disaster can cause loss of human life and economy, as well as environment damage. Also the disaster rescue is difficult for the complexity of rescue process. Disaster rescue is emergent and urgent most of the time; therefore, quick response is rather important. The existing knowledge always supplies some strategies and generates a rescue plan by human expertise manual decision-making. The rescue plan obtained manually may be not a good one due to the lack of time available for human decision-makers to make decisions. To overcome limited time pressure, while retaining minimum rescue project duration, a rescue plan for the maritime disaster rescue is obtained with the application of heuristic resource-constrained project scheduling approach in this paper. Meanwhile a heuristic algorithm is proposed to generate the minimum project duration and the activities start times. To study its performance, 20 maritime rescue examples (21–50 activities) were tested. By comparing with those generated by the manual decision-making method, the results generated by heuristic algorithm indicate high efficiency. It also identifies the critical chain of the rescue project, which can help decision-makers control the rescue process efficiently.

Fuzzy Activity Network Method for Project Scheduling Under Resource Constraints

In this article, a fuzzy activity network method is developed for project scheduling under resource constraints. Trapezoidal fuzzy numbers are used for estimating uncertain durations of activities, and then these fuzzy numbers are replaced by suitable crisp durations for project scheduling under resource constraints. In the next step, the critical chain is identified for determining the project duration, and uncertainties associated with activities are addressed by using feeding/project buffers to protect the project schedule from disturbances. For minimizing project duration, the proposed method considers both the suitable crisp durations and the start times of activities as decision variables. Hence, a new procedure based on genetic algorithm and priority heuristics is also developed for efficiently determining these decision variables. Furthermore, the method also considers selecting the best possible relationships between activities to minimize project duration. The proposed method using buffers makes it possible to improve project scheduling under resource constraints.

Particle Swarm Optimization for Preemptive Scheduling under Break and Resource-Constraints

This paper introduces a particle swarm optimization (PSO)-based methodology to implement preemptive scheduling under break and resource-constraints (PSBRC) for construction projects. The PSBRC under study allows the preemptive activities to be interrupted in off-working time and not to resume immediately in the next working period because all the limited resources are to be reallocated during a break. The potential solution to the PSBRC, i.e., a set of priorities deciding the order to start the activities or restart the interrupted activities, is represented by the multidimensional particle position. Hence PSO is applied to search for the optimal schedule for the PSBRC, in which a parallel scheme is adopted to transform the particle-represented priorities to a schedule. Computational analyses are presented to verify the effectiveness of the proposed methodology. This paper provides an attempt to make use of preemption and break for the resource-constrained construction project with the objective of minimizing project duration.

Permutation-Based Particle Swarm Optimization for Resource-Constrained Project Scheduling

In the light of particle swarm optimization (PSO) which utilizes both local and global experiences during search process, a permutation-based scheme for the resource-constrained project scheduling problem (RCPSP) is presented. In order to handle the permutation-feasibility and precedence-constraint problems when updating the particle-represented sequence or solution for the RCPSP, a hybrid particle-updating mechanism incorporated with a partially mapped crossover of a genetic algorithm and a definition of an activity-move-range is developed. The particle-represented sequence should be transformed to a schedule (including start times and resource assignments for all activities) through a serial method and accordingly evaluated against the objective of minimizing project duration. Experimental analyses are presented to investigate the performances of the permutation-based PSO. The study aims at providing an alternative for solving the RCPSP in the construction field by utilizing the advantages of PSO.

Heuristic scheduling of resource‐constrained, multiple‐mode and repetitive projects

An alternative heuristic method for scheduling repetitive projects in which resources are limited and activities may be executed with multiple modes of resource demands associated with different durations is proposed. Unlike general heuristic methods that separately analyze each competing activity and schedule only one at a time, the proposed heuristic algorithm ranks possible combinations of activities every time and simultaneously schedules all activities in the selected combination leading to minimal project duration. All alternative combinations of activities in consideration of resource constraints, multiple modes and characteristics of the repetitive projects are determined through a permutation tree‐based procedure. The heuristic method is implemented based on the corresponding framework. An example is presented to demonstrate the efficiency of the proposed heuristic method. The study is expected to provide an efficient heuristic methodology for solving the project scheduling problem.

Optimal Planning and Scheduling for Repetitive Construction Projects

This paper presents a multiobjective optimization model for the planning and scheduling of repetitive construction projects. The model enables construction planners to generate and evaluate optimal construction plans that minimize project duration and maximize crew work continuity, simultaneously. The computations in the present model are organized in three major modules: scheduling, optimization, and ranking modules. First, the scheduling module uses a resource-driven scheduling algorithm to develop practical schedules for repetitive construction projects. Second, the optimization module utilizes multiobjective genetic algorithms to search for and identify feasible construction plans that establish optimal tradeoffs between project duration and crew work continuity. Third, the ranking module uses multiattribute utility theory to rank the generated plans in order to facilitate the selection and execution of the best overall plan for the project being considered. An application example is analyzed to illustrate the use of the model demonstrate its new capabilities in optimizing the planning and scheduling of repetitive construction projects.

Particle swarm optimization for resource-constrained project scheduling

This paper introduces the particle swarm optimization (PSO)-based approach to resolve the resource-constrained project scheduling problem (RCPSB) with the objective of minimizing project duration. Activities priorities for scheduling are represented by particles and a parallel scheme is utilized to transform the particle-represented priorities to a feasible schedule according to the precedence and resource constraints so as to be evaluated. Then the framework of the PSO scheme for the RCPSB is developed. Computational analyses are provided so as to investigate the performance of the PSO-based approach for the RCPSB. The study aims at developing an alternative and efficient optimization methodology for solving the RCPSB and opening the application of PSO to the optimization issues for construction project management.

Particle swarm optimization-based schemes for resource-constrained project scheduling

Particle swarm optimization (PSO) performed through particle flying along the trajectory that will be continuously updated is based to develop a solution-solving scheme for the resource-constrained project scheduling problem (RCPSP). The potential solution to the RCPSP in view of minimizing project duration is represented by the multidimensional particle, where two solution representations, i.e., priority-based representation and permutation-based representation, are respectively considered. The frameworks of the PSOs for the RCPSP according to the two solution representations are developed. Experimental analyses are presented to investigate the performance of the proposed PSO-based methodology, including comparison of the two representations and comparison with other approaches for the RCPSP. The study aims at providing an alternative means for the RCPSP by utilizing the features of PSO, such as particle-updating mechanism that may benefit from the searching experience of one particle itself or the best of all particles in the swarm.

Scheduling system with focus on practical concerns in repetitive projects

The line of balance (LOB) method has long been used to model construction projects with repetitive units. Critics, however, indicate two major shortcomings of applying LOB in the construction industry: (1) it has not yet been adapted to numerical computation as readily as network methods; and (2) it relies on restrictive assumptions and therefore cannot treat the practical concerns concluded in this paper. To treat all the practical concerns and provide necessary calculation power, a new scheduling system is proposed: the Repetitive Scheduling Method (RSM) and its computerized implementation, Repetitive Project Planner (RP2). RSM includes necessary modelling elements (i.e. activity and relationship types) and a set of computational algorithm to calculate the start time of every activity as well as the minimum project duration. RP2 automatically calculates and generates RSM diagrams that are particularly useful in serving as a test‐bed for project managers to perform what‐if analyses for different crew utilization strategies. A real‐life pipeline project is used to demonstrate the application of RP2 and to compare that with the critical path method (CPM) and traditional LOB models.

Simulation-based optimization for dynamic resource allocation

An optimization methodology that integrates discrete-event simulation (DES) with a heuristic algorithm is developed to optimize dynamic resource allocation for construction scheduling. This heuristic algorithm is based on the objective of minimizing project duration and would consider activating multiple activities at the same time based on limited quantities of resources. The optimization is implemented through discrete-event simulation that is able to describe complex operational systems through simulation models, without the need to build mathematical models. The effectiveness of the simulation-based optimization is demonstrated through an application, where the impact of the optimized allocation is compared with those of other ones. The proposed methodology provides an alternative to optimize resource flow for scheduling and broadens the application potential of discrete-event simulation in the construction field.

The Value of Resource Flexibility in the Resource-Constrained Job Assignment Problem

We consider the problem of minimizing project duration in an environment where each project activity can be executed by a number of different flexible resources. The capabilities of the flexible resources are modeled using a binary activity-resource matrixAcalled theavailabilitymatrix. Activity durations are known deterministically. We develop tight lower bounds and a variety of heuristics accompanied with extensive computational tests regarding their performance. It is shown that our algorithms consistently perform near optimally. Using these heuristics, we perform experiments on the effect of operating flexibility on project duration, where resource flexibility is measured by the number of resources available per activity, and the form of the availability matrixA. Our experiments lead to important managerial guidelines regarding the role of resource flexibility on project duration. For instance, it is found that when the flexible capabilities of the resources are evenly distributed across the activities, small improvements in resource flexibilities provide nearly the same benefits in project duration as a system of fully flexible resources.

Scheduling of linear projects with single loop structures

In the last decade, a number of linear scheduling methods based on the theories of linear and dynamic programming were developed for the scheduling of linear projects. However, these methods are only suitable for the scheduling of serial linear projects (i.e. sequential activities). In this paper, a nonserial dynamic programming formulation for the scheduling and control of linear projects that involve single loop structures is presented. The objective is to provide for the selection of crew formations, interruptions, and lags for the production activities that result in the minimum project duration. The advantage of this nonserial dynamic programming approach is its ability to handle both serial and nonserial linear projects with variable crew formations and work interruptions. In addition, the system presented produces a graphical representation of the project schedule of minimum duration and updates the original schedule based on current information supplied by the user. A numerical example is included to illustrate the input and output of the system presented.

Stochastic Construction Time-Cost Trade-Off Analysis

Traditional time-cost trade-off analysis assumes that the time and cost of an option within an activity are deterministic. However, in reality the time and cost are uncertain. Therefore, in analyzing the time-cost trade-off problem, uncertainties should be considered when minimizing project duration or cost. Simulation techniques are useful for analyzing stochastic effects, but a general strategy/algorithm is needed to guide the analysis to obtain optimal solutions. This paper presents a hybrid approach that combines simulation techniques and genetic algorithms to solve the time-cost trade-off problem under uncertainty. The results show that genetic algorithms can be integrated with simulation techniques to provide an efficient and practical means of obtaining optimal project schedules while assessing the associated risks in terms of time and cost of a construction project. This new approach provides construction engineers with a new way of analyzing construction time/cost decisions in a more realistic manner. Historical time/cost data and available options to complete a project can be modeled, so that construction engineers can identify the best strategies to take to complete the project at minimum time and cost. Also, what-if scenarios can be explored to decide the desired/optimal time and/or cost in planning and executing project activities.

GA-Based Multicriteria Optimal Model for Construction Scheduling

Resources for construction activities are limited in the real construction world. To avoid the waste and shortage of resources on a construction jobsite, scheduling must include resource allocation. A multicriteria computational optimal scheduling model, which integrates the time/cost trade-off model, resource-limited model, and resource leveling model, is proposed. A searching technique using genetic algorithms (GAs) is adopted in the model. Furthermore, the nondominated solutions are found by the multiple attribute decision-making method, technique for order preference by similarity to ideal solution. The model can effectively provide the optimal combination of construction durations, resource amounts, minimum direct project costs, and minimum project duration under the constraint of limited resources.

A genetic-algorithm-based resource-constrained construction scheduling system

Resources for construction activities are limited in the real construction world. To avoid waste and shortage of resources on a construction jobsite, scheduling must include resource allocation. A new resourceconstrained construction scheduling system is proposed in this paper. A GA-based searching technique is adopted in the system. In this paper, new GA crossover and mutation operators, UX3 and UM3, are presented. These new operators overcome the drawback of traditional GA operators for sequencing problems. The system effectively can provide the optimal combination of construction duration, resource quantities and minimum project duration under the constraint of limited resources.