Project scheduling with time, cost and risk trade-off using adaptive multiple objective differential evolution

2018 ◽  
Vol 25 (5) ◽  
pp. 623-638 ◽  
Author(s):  
Duc Hoc Tran ◽  
Luong Duc Long
Keyword(s):  
Differential Evolution ◽  
Project Scheduling ◽  
Project Managers ◽  
Adaptive Mutation ◽  
Multiple Objective ◽  
Time Cost ◽  
Trade Off ◽  
Content Type ◽  
Proposed Model ◽  
Mutation Strategy

PurposeAs often in project scheduling, when the project duration is shortened to reduce total cost, the total float is lost resulting in more critical or nearly critical activities. This, in turn, results in reducing the probability of completing the project on time and increases the risk of schedule delays. The objective of project management is to complete the scope of work on time, within budget in a safe fashion of risk to maximize overall project success. The purpose of this paper is to present an effective algorithm, named as adaptive multiple objective differential evolution (DE) for project scheduling with time, cost and risk trade-off (AMODE-TCR).Design/methodology/approachIn this paper, a multi-objective optimization model for project scheduling is developed using DE algorithm. The AMODE modifies a population-based search procedure by using adaptive mutation strategy to prevent the optimization process from becoming a purely random or a purely greedy search. An elite archiving scheme is adopted to store elite solutions and by aptly using members of the archive to direct further search.FindingsA numerical construction project case study demonstrates the ability of AMODE in generating non-dominated solutions to assist project managers to select an appropriate plan to optimize TCR problem, which is an operation that is typically difficult and time-consuming. Comparisons between the AMODE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm. The proposed model is expected to help project managers and decision makers in successfully completing the project on time and reduced risk by utilizing the available information and resources.Originality/valueThe paper presented a novel model that has three main contributions: First, this paper presents an effective and efficient adaptive multiple objective algorithms named as AMODE for producing optimized schedules considering time, cost and risk simultaneously. Second, the study introduces the effect of total float loss and resource control in order to enhance the schedule flexibility and reduce the risk of project delays. Third, the proposed model is capable of operating automatically without any human intervention.

scholarly journals CHAOTIC INITIALIZED MULTIPLE OBJECTIVE DIFFERENTIAL EVOLUTION WITH ADAPTIVE MUTATION STRATEGY (CA-MODE) FOR CONSTRUCTION PROJECT TIME-COST-QUALITY TRADE-OFF

2015 ◽  
Vol 22 (2) ◽  
pp. 210-223 ◽  
Author(s):  
Min-Yuan CHENG ◽  
Duc-Hoc TRAN ◽  
Minh-Tu CAO
Keyword(s):  
Differential Evolution ◽  
Evolutionary Process ◽  
Adaptive Mutation ◽  
Multiple Objective ◽  
Initial Population ◽  
Time Cost ◽  
Nsga Ii ◽  
Trade Off ◽  
Mutation Strategy ◽  
Chaos Sequences

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm.

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

2020 ◽  
Vol 27 (9) ◽  
pp. 2287-2313 ◽  
Author(s):  
Duc Hoc Tran
Keyword(s):  
Decision Making ◽  
Construction Projects ◽  
Project Managers ◽  
Multiple Objective ◽  
Cost Curve ◽  
Multi Criteria Decision Making ◽  
Construction Process ◽  
Time Cost ◽  
Content Type ◽  
Social Group Optimization

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.

Overall multiobjective optimization of construction projects scheduling using particle swarm

2016 ◽  
Vol 23 (3) ◽  
pp. 265-282 ◽  
Author(s):  
Emad Elbeltagi ◽  
Mohammed Ammar ◽  
Haytham Sanad ◽  
Moustafa Kassab
Keyword(s):  
Cash Flow ◽  
Construction Projects ◽  
Real Life ◽  
Particle Swarm ◽  
Project Managers ◽  
Time Cost ◽  
Swarm Optimization ◽  
Content Type ◽  
Proposed Model ◽  
Optimized Schedule

Purpose – Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule. Design/methodology/approach – In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes. Findings – Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources. Originality/value – The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

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

2020 ◽  
Vol 15 (3) ◽  
pp. 1187-1204
Author(s):  
Amin Mahmoudi ◽  
Saad Ahmed Javed
Keyword(s):  
Comparative Analysis ◽  
Project Scheduling ◽  
Design Methodology ◽  
Original Method ◽  
Project Managers ◽  
Sensitivity Analyses ◽  
Quality Loss ◽  
Time Cost ◽  
Content Type ◽  
New Models

Purpose The 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/approach The 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. Findings The 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/value The 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.

Time-cost-quality-environmental impact trade-off resource-constrained project scheduling problem with DEA approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sayyid Ali Banihashemi ◽  
Mohammad Khalilzadeh
Keyword(s):  
Environmental Impacts ◽  
Cost Optimization ◽  
Project Managers ◽  
Time Cost ◽  
Quality Factors ◽  
Trade Off ◽  
Content Type ◽  
Execution Mode ◽  
Project Objectives ◽  
Efficient Execution

PurposeThe purpose of this paper is to evaluate project activities' efficiency in different execution modes for the optimization of time–cost-quality and environmental impacts trade-off problem.Design/methodology/approachThis paper presents a parallel Data Envelopment Analysis (DEA) method for evaluation of project activities with different execution modes to select the best execution mode and find a trade-off between objectives. Also, according to the nature of the project activities, outputs are categorized into desirable (quality) and undesirable (time, cost and environmental impacts) and analyzed based on the DEA model. In order to rank efficient execution modes, the ideal and anti-ideal virtual units method is used. The proposed model is implemented on a real case of a rural water supply construction project to demonstrate its validity.FindingsThe findings show that the use of the efficient execution mode in each activity leads to an optimal trade-off between the four project objectives (time, cost, quality and environmental impacts).Practical implicationsThis study help project managers and practitioners with choosing the most efficient execution modes of project activities taking time–cost-quality-environmental impacts into account.Originality/valueIn this paper, in addition to time and cost optimization of construction projects, quality factors and environmental impacts are considered. Further to the authors' knowledge, there is no method for evaluating project activities' efficiency. The efficiency of different activity modes is also evaluated for the first time to select the most efficient modes. This research can assist project managers with choosing the most appropriate execution modes for the activities to ultimately accomplish the project with the lowest time, cost and environmental impacts along with the highest quality.

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

2021 ◽  
Vol 28 (10) ◽  
pp. 3346-3367
Author(s):  
Mohamed ElMenshawy ◽  
Mohamed Marzouk
Keyword(s):  
Construction Projects ◽  
Facility Management ◽  
Information Modeling ◽  
Generation Model ◽  
Time Cost ◽  
Nsga Ii ◽  
Trade Off ◽  
Content Type ◽  
Proposed Model ◽  
Building Information

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.

A new framework for time-cost trade-off considering float loss impact

2017 ◽  
Vol 22 (1) ◽  
pp. 20-36 ◽  
Author(s):  
Sameh Monir El-Sayegh ◽  
Rana Al-Haj
Keyword(s):  
Optimization Technique ◽  
Time Cost ◽  
Trade Off ◽  
Deterministic Optimization ◽  
Content Type ◽  
Project Duration ◽  
Trade Offs ◽  
Project Objectives ◽  
The Impact ◽  
New Framework

Purpose The purpose of this paper is to propose a new framework for time–cost trade-off. The new framework provides the optimum time–cost value taking into account the float loss impact. Design/methodology/approach The stochastic framework uses Monte Carlo Simulation to calculate the effect of float loss on risk. This is later translated into an added cost to the trade-off problem. Five examples, from literature, are solved using the proposed framework to test the applicability of the developed framework. Findings The results confirmed the research hypothesis that the new optimum solution will be at a higher duration and cost but at a lower risk compared to traditional methods. The probabilities of finishing the project on time using the developed framework in all five cases were better than those using the classical deterministic optimization technique. Originality/value The objective of time–cost trade-off is to determine the optimum project duration corresponding to the minimum total cost. Time–cost trade-off techniques result in reducing the available float for noncritical activities and thus increasing the schedule risks. Existing deterministic optimization technique does not consider the impact of the float loss within the noncritical activities when the project duration is being crashed. The new framework allows project managers to exercise new trade-offs between time, cost and risk which will ultimately improve the chances of achieving project objectives.

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