Finance-based scheduling using meta-heuristics: discrete versus continuous optimization problems

Abstract

Purpose – The purpose of this paper is to compare the performance of the genetic algorithm (GA), simulate annealing (SA) and shuffled frog-leaping algorithm (SFLA) in solving discrete versus continuous-variable optimization problems of the finance-based scheduling. This involves the minimization of the project duration and consequently the time-related cost components of construction contractors including overheads, finance costs and delay penalties. Design/methodology/approach – The meta-heuristics of the GA, SA and SFLA have been implemented to solve non-deterministic polynomial-time hard (NP-hard) finance-based scheduling problem employing the objective of minimizing the project duration. The traditional problem of generating unfeasible solutions in scheduling problems is adequately tackled in the implementations of the meta-heuristics in this paper. Findings – The obtained results indicated that the SA outperformed the SFLA and GA in terms of the quality of solutions as well as the computational cost based on the small-size networks of 30 activities, whereas it exhibited the least total duration based on the large-size networks of 120 and 210 activities after prolonged processing time. Research limitations/implications – From researchers’ perspective, finance-based scheduling is one of the few domain problems which can be formulated as discrete and continuous-variable optimization problems and, thus, can be used by researchers as a test bed to give more insight into the performance of new developments of meta-heuristics in solving discrete and continuous-variable optimization problems. Practical implications – Finance-based scheduling discrete-variable optimization problem is of high relevance to the practitioners, as it allows schedulers to devise finance-feasible schedules of minimum duration. The minimization of project duration is focal for the minimization of time-related cost components of construction contractors including overheads, finance costs and delay penalties. Moreover, planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction through the expedient delivery of the completed project for clients to start reaping the anticipated benefits. Social implications – Planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction. Originality/value – SFLA represents a relatively recent meta-heuristic that proved to be promising, based on its limited number of applications in the literature. This paper is to implement SFLA to solve the discrete-variable optimization problem of the finance-based scheduling and assess its performance by comparing its results against those of the GA and SA.

Metaheuristic Algorithms for Multimode Multiproject Scheduling With the Objective of Positive Cash Flow Balance

“…The proposed problem was named the financebased scheduling problem, in which the total project duration was minimized, and the finance-availability constraint was fulfilled in the meantime. To account for large-size projects, Ali and Elazouni [13] and Alghazi et al [14] applied genetic algorithms to develop finance-based schedule models, while Elazouni et al [15] and Al-Shihabi and AlDurgam [16] used simulated annealing and max-min ant system algorithms, respectively, to address the problem. Further research efforts were undertaken by Fathi and Afshar [17] and Elazouni and Abido [18] to consider multiple objectives in integrating a project's cash flow with its schedule, as well as by Liu and Wang [19] and El-Abbasy et al [20] to consider project finance in a multiproject scheduling context.…”

Analyzing the impact of financial policies on construction projects performance using system dynamics

“…They suggested that the inclusion of overlap tends to incur a higher amount of overdraft while shortening the project completion time and leading to greater cash flow uncertainty. Moreover, some researchers developed cash flow models based on algorithms (Badell et al, 2005;Metwally, 2005, 2007;Elazouni et al, 2015). For instance, Metwally (2005, 2007) used genetic algorithms to devise finance-based schedules to maximize project profit by minimizing financing costs and indirect costs.…”

“…Moreover, some researchers developed cash flow models based on algorithms (Badell et al. , 2005; Elazouni and Metwally, 2005, 2007; Elazouni et al. , 2015).…”

The Impact of Overheads Variation on Time-Cost Optimisation of Building Infrastructure Projects

“…Nonetheless, contractors often overlook the importance of indirect expenses estimation due to its low percentage contribution to the contract sum (Chan, & Pasquire, 2002). Moreover, planning the optimal project completion is a major construction management aspect for building constructors towards maximising their profit margins and succeeding client's satisfaction through the expedient delivery of the final product for building owners to start reaping the anticipated benefits (Elazouni et al, 2015). Notwithstanding the significance of both indirect expenses' management and TCO on project success, to the author's knowledge, there is no research exploring the effect of indirect costs' variation on TCO and the resulting project profitability.…”