Ashraf Elazouni

Finance‐based CPM/LOB scheduling of projects with repetitive non‐serial activities

Projects of repetitive non‐serial activities constitute a major category of construction projects which can be scheduled more conveniently using the line of balance (LOB) technique. Generally, scheduling activities such that the expenditures are always in balance with the available cash is a must to devise financially feasible schedules. The objective is to integrate a CPM/LOB model for a project of repetitive non‐serial activities with a cash flow model and utilize the integrated model to devise financially feasible schedules. The genetic algorithms (GAs) technique is employed to maximize the profit at the end of the project under the constraints of available cash. The optimization of the integrated models was demonstrated using an example project of 15 activities carried out at five units. The CPM/LOB model was validated against the results of a dynamic programming model in the literature and further by conducting a sensitivity analysis of the results of the integrated model. Finally, the model offers an effective financial planning tool for projects of repetitive non‐serial activities.

Multiobjective Evolutionary Finance-Based Scheduling: Entire Projects’ Portfolio

A strength Pareto evolutionary algorithm (SPEA) is proposed and was modified by incorporating logic-preserving crossover and mutation operators and employed to devise a set of optimum finance-based schedules of multiple projects being implemented simultaneously by a construction contractor. The problem involves the minimization of the conflicting objectives of financing costs, duration of the group of projects, and the required credit. The modified SPEA was employed to obtain the Pareto-optimal fronts for the two-objective combinations as well as the three objectives. In addition, a fuzzy-based technique was used to help the contractors select the best compromise solution over the Pareto-optimal solutions. The proposed approach has been developed and implemented on projects with different sizes. The results obtained by the modified SPEA, fuzzy-based approach demonstrated its potential and effectiveness in finance-based scheduling of multiple projects.

Precedence-Preserving GAs Operators for Scheduling Problems with Activities’ Start Times Encoding

Precedence-preserving crossover and mutation operators for scheduling problems with activities’ start times encoding are proposed and employed in this paper. The objective is to tackle the incapability of the genetic algorithms (GAs) operators to preserve the precedence relationships among activities and generate feasible solutions in scheduling problems. The proposed operators employ an embedded precedence-preserving algorithm that determines the activities’ forward free float and backward free float and utilize them in randomly selected backward and forward paths, respectively. The proposed operators were evaluated using finance-based scheduling problems for large-scale projects of 120 repetitive activities. Moreover, the proposed operators were validated by comparing the results with the optimum results of a resource-constrained scheduling problem reported in the literature. The results exhibited the robustness of the proposed operators to reduce the computational costs. In addition, the results demons...

Improved Genetic Algorithm for Finance-Based Scheduling

AbstractCurrently, the genetic algorithm (GA) technique has been used in finance-based scheduling to devise critical path method (CPM) schedules exhibiting cash flows of periodical finance needs below preset cash constraints. The chromosomes of the schedules that violate this condition are referred to as finance-infeasible chromosomes. Infeasibility related to finance is peculiar to finance-based scheduling problems. In scheduling problems, chromosomes that are infeasible based on precedence relationships are typically penalized. This paper introduces a repair algorithm for the finance-infeasible chromosomes generated within the GA systems. The repair algorithm identifies the periods exhibiting finance needs that exceed the constrained cash, calculates the amounts of finance needs above the constraints, identifies the ongoing activities, selects randomly an activity for delaying its start time, determines the impact of the delay on the finance needs, and repeats the procedure until finance feasibility is ...

Performance of Shuffled Frog-Leaping Algorithm in Finance-Based Scheduling

AbstractCurrently, meta-heuristics including the genetic algorithms (GA) and simulated annealing (SA) have been used extensively to solve non-deterministic polynomial-time hard (NP-hard) problems. Continued efforts of researchers to upgrade the performance of the meta-heuristics in use resulted in the evolution of new ones. Shuffled frog-leaping algorithm (SFLA) is one of the recently introduced heuristics. The few applications of the SFLA in the literature in different areas demonstrated the capacity of the SFLA to provide high-quality solutions. The main objective of this paper is to further bring the SFLA to the attention of researchers as a potential technique to solve the NP-hard combinatorial problem of finance-based scheduling. The performance of the SFLA is evaluated through benchmarking its results against those of the GA and SA. The traditional problem of generating infeasible solutions in scheduling problems is adequately tackled in the implementations of the GA, SA, and SFLA. Fairly large proj...

Progress monitoring of construction projects using pattern recognition techniques

Project monitoring involves collecting the actual‐progress data, and comparing them against the relevant planned‐progress data to evaluate the overall project progress at specified cut‐off dates. Inevitable issues including variations in reporting skills as well as the willingness to record accurate data impact on the quality of the collected data. Comparison against multiple possible benchmarks (one‐to‐many) rather than a single benchmark (one‐to‐one) offers the potential to alleviate the negative impact of low‐quality data on the progress evaluation. Special patterns, which can be readily manipulated within computer programs, are devised to encode the planned and actual progress at the cut‐off dates. Basically, pattern recognition techniques are utilized to classify the multiple patterns representing the planned progress at a given cut‐off date and the classification is used to evaluate the pattern representing the actual progress at the same date. The pattern recognition techniques generalize a virtual benchmark to represent the planned progress based on multiple patterns generated at a given cut‐off date and representing possible benchmarks. In addition to the alleviation of the negative impact of low‐quality data on the progress evaluation, the generalization feature potentially encourages a long‐run attitude in site personnel to report high‐quality data. Finally, the pattern recognition concept and technique proved their robustness to monitor and evaluate the overall progress of the projects based on the technique of critical path method.

Enhanced Trade-Off of Construction Projects: Finance-Resource-Profit

AbstractThe parameters of finance requirements, resource leveling, and anticipated profit have significant influence on many aspects of project management. These parameters interact and occasionally conflict with each other. Accordingly, achievement of a balance between these three parameters is crucial to ensure the accomplishment of project objectives. A multi-objective multimode scheduling optimization algorithm is proposed to establish the optimal trade-off between these three parameters. The strength Pareto evolutionary algorithm (SPEA) was implemented to obtain the solutions comprising the Pareto-optimal trade-off. The developed SPEA was validated by reproducing identical results of a time/cost trade-off problem in the literature. The developed SPEA was used to obtain the Pareto-optimal trade-off of a network of nine multimode activities that comprised fifty solutions. The trade-off of fifty solutions allows decision makers explore the impact of finance upon the efficiency of resource utilization an...

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

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 ...

Enhanced heuristic for finance-based scheduling of construction projects

Typically, construction contractors operate under cash-constrained operating conditions. The lag between the time when contractors spend money to accomplish work on site and the time when payments are actually made by clients, which partially compensate contractors for the accomplished work, constantly creates a finance deficit. Contractors often supplement finance deficits using external funds procured through establishing credit-line bank accounts which typically allow contractors to withdraw cash up to specified credit limits. This makes the task of project scheduling considering the constraints of specified finance very important for financial and operational planning. This scheduling concept and technique are referred to as finance-based scheduling. An enhanced heuristic is proposed to devise finance-based schedules of multiple projects within contractors’ portfolios. The enhancement is achieved by replacing the exhaustive enumeration technique employed in the heuristic to specify activities’ start times with a polynomial shifting algorithm. This enhancement resulted in a substantial reduction in the number of solutions explored before a feasible solution is encountered. The enhanced heuristic was validated through comparison with the integer programming technique using 240 problems of randomly generated networks of sizes that range from 30 to 240 activities. Further, it was proved that the enhanced heuristic can be easily scaled up to handle portfolios of multiple large-size projects.

FINANCE-BASED SCHEDULING FOR MULTIPLE PROJECTS WITH MULTIMODE ACTIVITIES

Lack of financing and cash deficit is considered as a primary threat to contractor’s financial management. Therefore, in case of insufficient cash, many contractors find it difficult to stick with the project schedule leading to extra overhead costs and liquidated damages. Mainly contractors deal with the project scheduling and financing as two independent functions of construction project management. Thus, the main objective of this research is to develop a multi-objective elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for solving finance-based scheduling problem of multi-projects with multi-mode activities. A CPM scheduling model is constructed with its associated cash flow to calculate the values of the multiple objectives. The problem involves the minimization of conflicting objectives: duration of multiple projects, financing costs, and maximum negative cumulative balance. The designed optimization model performs operations of NSGA-II in three main phases: (1) Population initialization; (2) Fitness evaluation; and (3) Generation evolution. An application example is analyzed to illustrate the use of the model and to demonstrate its capabilities in generating optimum solutions. The model can be considered relevant for practitioners to use in large construction projects to make decisions regarding financing.