Project Scheduling: Time-Cost Tradeoff Problems

Srivastava, Sanjay; Pathak, Bhupendra Kumar; Srivastava, Kamal

doi:10.1007/978-3-642-12775-5_14

Cited by 5 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, mathematical methods are not computationally tractable for real-life projects. Due to the limitations of mathematic methods, the application of EAs for the TCT problem has attracted more attention over past decade (Chen and Tsai, 2011; Sonmez and Bettemir, 2012; Srivastava et al , 2010; Zhang and Li, 2010). EAs are characterized by iterative progresses used to guide the randomly initiated population to the final optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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

Tran

Long

2018

ECAM

View full text Add to dashboard Cite

Purpose As 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/approach In 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. Findings A 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/value The 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Tran

Long

2018

ECAM

View full text Add to dashboard Cite

show abstract

“…A survey of various approaches to solve TCT problem is detailed out in [17]. Wide varieties of TCT problems encountered in real world engineering projects are dealt in [18].…”

Section: Introductionmentioning

confidence: 99%

Integrated ANN-HMH Approach for Nonlinear Time-Cost Tradeoff Problem

Pathak¹,

Srivastava²

2014

IJCIS

Self Cite

View full text Add to dashboard Cite

This paper presents an integrated Artificial Neural Network -Hybrid Meta Heuristic(ANN-HMH) method to solve the nonlinear time-cost tradeoff(TCT) problem of real life engineering projects. ANN models help to capture the existing nonlinear time-cost relationship in project activities. ANN models are then integrated with HMH technique to search for optimal TCT profile. HMH is a proven evolutionary multiobjective optimization technique for solving TCT problems. The study has implication in real time monitoring and control of project scheduling processes.

show abstract

Integrated Fuzzy–HMH for project uncertainties in time–cost tradeoff problem

Pathak

Srivastava

2014

Applied Soft Computing

View full text Add to dashboard Cite

Project Scheduling: Time-Cost Tradeoff Problems

Cited by 5 publications

References 27 publications

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

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

Integrated ANN-HMH Approach for Nonlinear Time-Cost Tradeoff Problem

Integrated Fuzzy–HMH for project uncertainties in time–cost tradeoff problem

Contact Info

Product

Resources

About