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

Abstract

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.

Scientometric Review of Construction Project Schedule Studies: Trends, Gaps and Potential Research Areas

“…(1) mathematical which considers an objective function in the existence of constraints; (2) heuristic to optimize project specific schedule instead of its universality; and (3) metaheuristic for scheduling problems having natural processes such as Pareto optimality genetic algorithm. Genetic algorithm has been widely applied in time-cost trade-off analysis to shorten project duration (Amiri et al, 2017;Tran & Long, 2018) and time-cost-quality trade-off to enhance quality (Abd El Razek et al, 2010). Al Haj and El-Sayegh (2015) used nonlinear-integer programming to shorten project duration and minimize project cost taking into total float time.…”

Robust Optimization Model for R&D Project Selection under Uncertainty in the Automobile Industry

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Project Portfolio Selection Considering Total Cost of Ownership in the Automobile Industry

“…Um and Kim [32] utilized structural equation modeling to prove a new product development project uncertainty that influences on the project performance. Tran and Long [33] considered the time, the cost, and the risk of projects for the project scheduling solved by the presented effective algorithm. Tofighian et al [34] solved the multi-period project portfolio selection problem, which, in each time period, deals with risks, stochastic incomes, and investing extra money.…”