Time-cost Trade-off Problem Research Articles

Optimizing of Discrete Time-Cost in Construction Projects Using New Adaptive Weight Formulations

In this paper, four new adaptive weight formulations are developed to enhance the performance of traditional Modified Adaptive Weight Approach (MAWA), which is the simplest way for solving the multi-objective optimization of discrete time-cost trade-off problem (DTCTP). Several numerical experiments are conducted to validate the newly proposed formulations. In addition, three meta-heuristic-based optimization algorithms are utilized to examine the variations within the results of the test instances. The proposed model provides several ways to solve DTCTP, as it includes different formulations and optimization algorithms. So, this model performs well for the medium and large-scale time-cost optimization problems on contrary to the traditional MAWA.

Selection of construction machine by considering time-cost trade-off problem

Selection of construction machine by considering time-cost trade-off problem

A crashing-based time-cost trade-off model considering quality cost and contract clauses

The success of a construction project is often evaluated by time, cost, and quality performances, which are interdependent. Shortening the project duration does not only increase the direct cost but also affects the project’s quality cost positively or negatively. In addition, the contract clauses in terms of penalty cost and early finish bonus have a significant impact on project duration and cost. This study proposes a crashing-based model, which simultaneously considers quality cost and contract clauses, to solve time-cost trade-off problems. The applicability of the proposed model is demonstrated with an illustrative example. The results of the proposed model were also compared with the results of two meta-heuristic algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), in order to validate the effectiveness of the model. The results revealed that the proposed model provides better solutions than these algorithms in terms of total project cost corresponding to different durations.

A Time–Cost Tradeoff Problem with Multiple Assessments and Release Times on a Chain Precedence Graph

We consider two variants of a time–cost tradeoff problem with multiple assessments on a chain precedence graph. Furthermore, each job can only be started after a release time, and a penalty cost is incurred when a job is not finished before its due date. The motivation is from the project such that a project owner can control the duration of each job and the support level of each project partner to avoid the penalty cost from the tardy jobs. We describe the penalty costs of the first and the second variants as the total weighted number of tardy jobs and the total weighted tardiness, respectively. These can be avoided by compressing the processing times or advancing the release times, which incurs a compression cost or release cost according to the linear and the piecewise constant functions, respectively. The objective is to minimize the total penalty, compression cost and release cost. In this paper, we propose the procedure based on the reduction to a shortest path problem, and show that the procedure can solve two variants in strongly polynomial time.

Time-cost optimization in repetitive project scheduling with limited resources

PurposeThe purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability.Design/methodology/approachThe model is based on the constraint programming (CP) framework; it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity.FindingsTwo case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation.Originality/valueThe original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.

Crashing construction projects considering schedule flexibility: an illustrative example

Time and cost are two of the most important performance indicators of construction projects. Project crashing is one of the effective methods for solving time-cost trade-off problems. However, in some instances, consuming all of the crash durations of the activities may lead to inflexible schedules. This paper proposes a project crashing model that aims to achieve more flexible schedules by means of assigning a crash duration consumption rate to each activity based on the preferred scheduling strategy. The applicability of the proposed model is presented along with an illustrative example. The Monte Carlo simulation is also performed to verify whether the proposed model is useful or not. The results revealed that the proposed model is an effective tool as it maintains schedule flexibility and increases the probability of completing the project in the targeted project duration. The proposed model also provides a new perspective for schedulers to solve project crashing problems.

Automated BIM schedule generation approach for solving time–cost trade-off problems

PurposeNowadays, building information modeling (BIM) represents an evolution in the architecture, engineering and construction (AEC) industries with its various applications. BIM is capable to store huge amounts of information related to buildings which can be leveraged in several areas such as quantity takeoff, scheduling, sustainability and facility management. The main objective of this research is to establish a model for automated schedule generation using BIM and to solve the time–cost trade-off problem (TCTP) resulting from the various scenarios offered to the user.Design/methodology/approachA model is developed to use the quantities exported from a BIM platform, then generate construction activities, calculate the duration of each activity and finally the logic/sequence is applied in order to link the activities together. Then, multiobjective optimization is performed using nondominated sorting genetic algorithm (NSGA-II) in order to provide the most feasible solutions considering project duration and cost. The researchers opted NSGA-II because it is one of the well-known and credible algorithms that have been used in many applications, and its performances were tested in several comparative studies.FindingsThe proposed model is capable to select the near-optimum scenario for the project and export it to Primavera software. A case study is worked to demonstrate the use of the proposed model and illustrate its main features.Originality/valueThe proposed model can provide a simple and user-friendly model for automated schedule generation of construction projects. In addition, opportunities related to the interface between an automated schedule generation model and Primavera software are enabled as Primavera is one of the most popular and common schedule software solutions in the construction industry. Furthermore, it allows importing data from MS Excel, which is used to store activities data in the different scenarios. In addition, there are numerous solutions, each one corresponds to a certain duration and cost according to the performance factor which often reflects the number of crews assigned to the activity and/or construction method.

An Integrated Multi-Criterion Decision-Making Analysis to Rank the Pareto-Front Solutions of Time-Cost Trade-Off Problems

In the construction management planning, both the client and the contractor are interested in completing the project within the planned schedule. To achieve it, time-cost trade-off problem (TCTP) is carried-out to obtain the optimal set of time-cost alternatives. Although Pareto front solutions are not preferred to each other, the decision-maker (DM) has to choose only the best solution. The DMs are neither well-educated nor have adequate knowledge to make proper decisions. Thus, such a choice has to be made through additional preferences not included in the original formulation of the optimization problem. To better support the meta-heuristic optimization outputs, in this paper, an integration of entropy weight, simple additive weighting (SAW), and technique for order of preference by similarity to ideal solution (TOPSIS) are modelled to solve the MCDM problem, while the Teaching Learning Based Optimization (TLBO) algorithm is applied to solve the proposed multi-objective decision-making model. In the proposed model, the weights selections are done objectively to demonstrate the variation on the rankings of MCDM approaches. While the entropy technique served to determine the weight of the criteria from the original matrix data objectively and the TOPSIS method is employed to rank the alternative Pareto front solutions. The proposed methodology is utilized to rank a set of Pareto optimal solutions of well-known optimization problems. The obtained results are compared against the rankings provided by (SAW) approach to investigate the efficiency of the proposed model. Results demonstrate that the present model can be a favorable decision-making model for the decision-makers.

A multi-agent based cooperative approach to decentralized multi-project scheduling and resource allocation

While project crashing is a prominent practice in project management and can be approached by the well-known time–cost tradeoff problem (TCTP), the existing methodologies assume a centralized decision paradigm. In this paper, we study a new decentralized multi-project time-cost tradeoff problem, called MPTCTP-D, where project scheduling and resource allocation decisions are made in a distributed way by autonomous project managers to optimize their local objective, subject to a global available budget for all the projects. We show and prove the existence of price of anarchy as the difference between the optimal objective value of MPTCTP-D and its centralized counterpart. A multi-agent based cooperative approach with negotiation protocol is proposed to mitigate resource competition of autonomous local decisions and achieve a reasonable allocation of resource for global decision-maker, with both exact and local-search based heuristic algorithms developed. Our exact algorithm is able to find optimal solutions and prove optimality for instances with up to five projects and 120 activities per project. The hybrid randomized and 2-opt improvement algorithm is able to efficiently find optimal solutions to small instances and significantly better solutions than other heuristics alone for instances with up to 20 projects and 120 activities per project. Managerial insights on the optimal solutions and the price of anarchy are obtained and discussed.

Integrated resource leveling and material procurement with variable execution intensities

This paper addresses the concurrent planning of resource leveling and material ordering, aiming to minimize the completion time of the project and the total cost of project execution. The latter is comprised of material ordering, purchase and holding costs as well as the cost of fluctuations in resource utilization. All activities of the project are performed regarding variable execution intensities, which means that the effort spent to execute an activity is considered to vary over time. A bi-objective mixed-integer linear programming model is proposed to deal with the above-mentioned time-cost trade-off problem. The proposed model determines the start time of activities, the fraction of executing activities in each time period as well as the quantity and timing of orders for materials, while satisfying precedence relationships among activities. The problem belongs to the class of NP-hard problems. Hence, a genetic algorithm as well as two multi-objective meta-heuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective vibration damping optimization (MOVDO) algorithm, calibrated by Taguchi method, are utilized to solve 30 different sized test problems. Then, four performance metrics are used to assess the performance of both NSGA-II and MOVDO algorithms. Results show that MOVDO outperforms NSGA-II in the small-, medium- and large-sized test problems. Moreover, a sensitivity analysis on different-sized test problems, using MOVDO, shows that when the durations of activities are considered to be flexible, the Pareto front is significantly improved in terms of objective values, and the number of Pareto-optimal solutions (NPS) increases. This helps the project manager(s) make decisions on the schedule and the execution intensity of activities and material procurement in a more flexible manner.

Activity Uncrashing Heuristic with Noncritical Activity Rescheduling Method for the Discrete Time-Cost Trade-Off Problem

AbstractDespite intensive research efforts that have been devoted to discrete time-cost optimization of construction projects, the current methods have very limited capabilities for solving the pro...

Preprocessing the Discrete Time-Cost Tradeoff Problem with Generalized Precedence Relations

This study investigates the deadline of the discrete time-cost tradeoff problem (DTCTP-D) with generalized precedence relations (GPRs). This problem requires modes to be assigned to the activities of a project such that the total cost is minimized and the total completion time and the precedence constraints are satisfied. Anomalies under GPRs are irreconcilable with many current theories and methods. We propose a preprocessing technology, an equivalent simplification approach, which is an effective method for solving large-scale complex problems. We first study a way to deal with the anomalies under GPRs, such as the reduce (increase) in project completion as a consequence of prolonging (shortening) an activity, and discover relationships between time floats and path lengths. Then, based on the theories, we transform the simplification into a time float problem and design a polynomial algorithm. We perform the simplification and improve the efficiency of the solution by deleting redundant calculation objects.

Optimizing time–cost in generalized construction projects using multiple-objective social group optimization and multi-criteria decision-making methods

PurposeProject managers work to ensure successful project completion within the shortest period and at the lowest cost. One of the main tasks of a project manager in the planning phase is to generate the project time–cost curve, and furthermore, to determine the most appropriate schedule for the construction process. Numerous existing time–cost tradeoff analysis models have focused on solving a simple project representation without regarding for typical activity and project characteristics. This study aims to present a novel approach called “multiple-objective social group optimization” (MOSGO) for optimizing time–cost decisions in generalized construction projects.Design/methodology/approachIn this paper, a novel MOGSO to mimic the time–cost tradeoff problem in generalized construction projects is proposed. The MOSGO has slightly modified the mechanism operation from the original algorithm to be a free-parameter algorithm and to enhance the exploring and exploiting balance in an optimization algorithm. The evidential reasoning technique is used to rank the global optimal obtained non-dominated solutions to help decision makers reach a single compromise solution.FindingsTwo case studies of real construction projects were investigated and the performance of MOSGO was compared to those of widely considered multiple-objective evolutionary algorithms. The comparison results indicated that the MOSGO approach is a powerful, efficient and effective tool in finding the time–cost curve. In addition, the multi-criteria decision-making approaches were applied to identify the best schedule for project implementation.Research limitations/implicationsAccordingly, the first major practical contribution of the present research is that it provides a tool for handling real-world construction projects by considering all types of construction project. The second important implication of this study derives from research finding on the hybridization multiple-objective and multi-criteria techniques to help project managers in facilitating the time–cost tradeoff (TCT) problems easily. The third implication stems from the wide-range application of the proposed model TCT.Practical implicationsThe model can be used in early stages of the construction process to help project managers in selecting an appropriate plan for whole project lifecycle.Social implicationsThe proposal model can be applied to multi-objective contexts in diversified fields. Moreover, the model is also a useful reference for future research.Originality/valueThis paper makes contributions to extant literature by: introducing a method for making TCT models applicable to actual projects by considering general activity precedence relations; developing a novel MOSGO algorithm to solving TCT problems in multi-objective context by a single simulation; and facilitating the TCT problems to project managers by using multi-criteria decision-making approaches.

Project scheduling by incorporating potential quality loss cost in time-cost tradeoff problems

PurposeThe study aims to introduce two new models of project scheduling by incorporating potential quality loss cost (PQLC) in time–cost tradeoff problems by overcoming the drawbacks of the existing Kim, Khang and Hwang (KKH) model. The proposed methods are named the Revised KKH-I (RKKH-I) and Revised KKH-II (RKKH-II) models for project scheduling.Design/methodology/approachThe performance of the existing KKH model has been tested using a numerical example followed by the identification of the main shortcomings of the KKH method. Later, a concrete effort has been made to address its shortcomings while improving its performance significantly. The comparative analysis of the Revised KKH models with the original model has also been presented along with sensitivity analyses.FindingsThe study recognizes that the construct on which the original KKH method was built is important; however, certain drawbacks make it unable to consider PQLC in projects, thus making its practical use questionable. The comparative analysis of the proposed methodology with the original method demonstrated that the new models (RKHH-I and II) are more comprehensive and intelligent than the existing KKH model.Originality/valueThe comparative analysis of the original KKH model and its improved version reveals that the revised model is far more suitable for project scheduling. The study is important for project managers who recognize project scheduling being one of the key parameters associated with project management process, crucial to control every day during the management of projects.

Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling

Construction projects are becoming increasingly larger and more complex in terms of size and cost. An optimization tool is necessary for the construction management system to develop the desired construction schedule to save time and cost. However, only a few efforts have been made to deal with the time-cost trade-off problem (TCTP) in the large-scale construction projects, and the existing optimization methods are slightly limited by the trouble of parameter tuning. As TCTP is known to be an NP-hard problem, this paper aims to introduce a new variant of Symbiotic Organisms Search (SOS) algorithm that does not contain control parameters, called DSOS (Discrete Symbiotic Organisms Search) which generates the parasite organism using a heuristic rule based on the network levels. This enhancement helps to improve the exploration phase and avoid premature stagnation. Performances are evaluated on project instances with different numbers of activities varying from 180 to 6300, as well as nine newly generated project instances with 720 activities but different network structures. The obtained results show a good performance of DSOS in terms of robustness and deviation from optimum in comparison with other meta-heuristics and variants of DSOS without using the heuristic rule. The good performance implies that DSOS is sufficient to serve as an effective tool to generate an optimized construction schedule.

Time-Cost Trade-Off Optimization with a New Initial Population Approach

Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project. This favorable target is achieved by finding the optimal set of time-cost alternatives and this is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It put a predefined number of the solutions of the single objective TCTP into the initial population of teaching learning-based algorithm, which is utilized as an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

Resource-Constrained Time-Cost Tradeoff for Repetitive Construction Projects

The time-cost tradeoff problem is to find optimal combinations of construction options with the objective of minimizing project time and cost. In order to search for such a set of optimal solutions, the total time and cost of projects needs to be calculated properly. In repetitive construction projects (RCPs), due to division of work into several units and involvement of many resources for activities completion, scheduling is unique and more complicated. Scheduling method in this paper besides addressing common constraints such as precedence relationships, required lead time and lag distance between activities, enables project managers to consider all resources and their shortages in scheduling. To raise the practicality of model, in addition to direct and indirect costs, resource idle cost is considered as a cost element in estimating total cost of project. The time variant multi-objective particle swarm optimization is applied to find non-dominated solutions on the basis of minimizing time and cost of project. An application example is presented at the end to illustrate the performance of the model. This research presents a resource-constrained time-cost tradeoff model to find the optimal set of crew combinations for project activities in RCPs, considering time and cost simultaneously.

Genetic algorithm for determining the construction logistics of precast components

Purpose Precast construction has become increasingly popular in the construction industry. Nonetheless, the logistics of construction materials has been a neglected topic, and this neglect has resulted in delays and cost overruns. Careful planning that considers all of the factors affecting construction logistics can ensure project success. The purpose of this paper is to examine the potential for using genetic algorithms (GAs) to derive logistics plans for materials production, supply and consumption. Design/methodology/approach The proposed GA model is based on the logistics of precast components from the supplier’s production yard, to the intermediate warehouse and then to the construction site. Using an activity-based costing (ABC) approach, the model not only considers the project schedule, but also takes into account the production and delivery schedule and storage of materials. Findings The results show that GAs are suitable for solving time-cost trade-off problems. The optimization process helps to identify the activity start time during construction and the delivery frequency that will result in the minimal cost. What-if scenarios can be introduced to examine the effects of changes in construction logistics on project outcomes. Originality/value This paper presents a method for using GAs and an ABC approach to support construction logistics planning decisions. It will help construction planners and materials suppliers to establish material consumption and delivery schedules, rather than relying on subjective judgment.

Multipurpose linear programming optimization model for repetitive activities scheduling in construction projects

Repetitiveness in project's activities has gained an important role in the construction industry. Multiple linear scheduling methods have been proposed in order to fully take advantage of the spatial and temporal information these type of project can provide to practitioners. Besides the advances in the optimization models in these fields, to the extent of the authors knowledge, there is still pending a complete and flexible mathematical linear programming formulation that allow practitioners to easily and jointly solve the Resource allocation, Resource-Constrained Project Scheduling and Time-Cost Tradeoff problem, taking into account as many scheduling properties, benefits and challenges that linear scheduling of repetitive activities imply. This paper shows a complete guide and computational experimentation, of a novel mathematical model that can be easily used by practitioners to optimize construction schedules considering to the largest extent the time and space conditions repetitive projects offer. Particularly, it contributes to the repetitive activities scheduling body of knowledge by successfully implementing a robust linear programing optimization model in a real construction project, while considering as much linear scheduling characteristics as possible. It proves that relationships in the sub-activity level, continuity conditions, multiple modes of execution, controlled acceleration routines and execution mode shifts, and multiple crews can be easily and jointly integrated to a linear optimization model by adding simple linear restrictions to the model.

A NEW ANALYTICAL METHODOLOGY TO HANDLE TIME-COST TRADE-OFF PROBLEM WITH CONSIDERING QUALITY LOSS COST UNDER INTERVAL-VALUED FUZZY UNCERTAINTY

This paper proposes a new framework in addressing time-cost trade-off problem (TCTP) under uncertainty. First critical path analysis is carried out based on developing a new interval-valued fuzzy (IVF)-program evaluation and review technique (PERT) approach. Then, non-conformance risks that influence on execution quality of activities are identified and evaluated based on a new approach that considers probability of risk along with impacts on time, cost, and performance. Then, a new mathematical model under IVF uncertainty is presented to decrease project total time while considering time, cost and quality loss cost that is determined in form of rework or modification cost. Finally, the approach categorizes the activities in three groups based on their level of criticality. Outcome of this methodology is a scheduling that addresses time, cost and quality trade-offs in addition to categorizing activities in different groups based on being on the critical path. Therefore, the project manager not only gets a scheduling based on the TCTP with considering quality loss cost but also has a knowledge of activities that require extra attentions. To show the steps of this methodology, an existing application from the literature is adopted and solved.