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Article
Publication date: 2 January 2019

Marimuthu Kannimuthu, Benny Raphael, Ekambaram Palaneeswaran and Ananthanarayanan Kuppuswamy

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

Abstract

Purpose

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

Design/methodology/approach

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Findings

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Research limitations/implications

Case study data sets were collected only from eight building construction projects in India.

Practical implications

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

Originality/value

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.

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Article
Publication date: 11 November 2019

Marimuthu Kannimuthu, Benny Raphael, Palaneeswaran Ekambaram and Ananthanarayanan Kuppuswamy

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an…

Abstract

Purpose

Construction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).

Design/methodology/approach

A direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.

Findings

Computational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.

Research limitations/implications

It is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.

Originality/value

An integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 4
Type: Research Article
ISSN: 0969-9988

Keywords

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Article
Publication date: 21 May 2021

Mohammad Khalilzadeh

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective…

Abstract

Purpose

This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources.

Design/methodology/approach

The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time.

Findings

The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon.

Originality/value

There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

Details

Journal of Engineering, Design and Technology , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1726-0531

Keywords

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Article
Publication date: 8 November 2018

Mahmood Kasravi, Amin Mahmoudi and Mohammad Reza Feylizadeh

Construction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the…

Abstract

Purpose

Construction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.

Design/methodology/approach

Due to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.

Findings

According to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.

Practical implications

ICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.

Originality/value

In this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

Details

Journal of Advances in Management Research, vol. 16 no. 2
Type: Research Article
ISSN: 0972-7981

Keywords

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Article
Publication date: 8 June 2021

Moaaz Elkabalawy and Osama Moselhi

This paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.

Abstract

Purpose

This paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.

Design/methodology/approach

The proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.

Findings

The developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.

Originality/value

The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 29 January 2021

Hongwei Zhu, Zhiqiang Lu, Chenyao Lu and Yifei Ren

To meet the requirement of establishing an effective schedule for the assembly process with overall detection and rework, this paper aims to address a new problem named…

Abstract

Purpose

To meet the requirement of establishing an effective schedule for the assembly process with overall detection and rework, this paper aims to address a new problem named resource-constrained multi-project scheduling problem based on detection and rework (RCMPSP-DR).

Design/methodology/approach

First, to satisfy both online and offline scheduling, a mixed integer programming model is established with a weighted bi-objective minimizing the expected makespan and the solution robustness. Second, an algorithm that combines a tabu search framework with a critical chain-based baseline generation scheme is designed. The tabu search framework focuses on searching for a reasonable resource flow representing the execution sequence of activities, while the critical chain-based baseline generation scheme establishes a buffered baseline schedule by estimating the tradeoff between two aspects of bi-objective.

Findings

The proposed algorithm can get solutions with gaps from −4.45% to 2.33% when compared with those obtained by the commercial MIP solver CPLEX. Moreover, the algorithm outperforms four other algorithms in terms of both objective performance and stability over instances with different weighting parameters, which reveals its effectiveness.

Originality/value

The represented RCMPSP-DR considering the overall detection and rework is an extension of the scheduling problem for large-scale equipment. An effective algorithm is proposed to establish the baseline schedule and determine the execution sequence of activities for the assembly process, which is significant for practical engineering applications.

Details

Assembly Automation, vol. 41 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

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Article
Publication date: 23 September 2019

Dheeraj Joshi, M.L. Mittal, Milind Kumar Sharma and Manish Kumar

The purpose of this paper is to consider one of the recent and practical extensions of the resource-constrained project scheduling problem (RCPSP) termed as the…

Abstract

Purpose

The purpose of this paper is to consider one of the recent and practical extensions of the resource-constrained project scheduling problem (RCPSP) termed as the multi-skill resource-constrained project scheduling problem (MSRCPSP) for investigation. The objective is the minimization of the makespan or total project duration.

Design/methodology/approach

To solve this complex problem, the authors propose a teaching–learning-based optimization (TLBO) algorithm in which self-study and examination have been used as additional features to enhance its exploration and exploitation capabilities. An activity list-based encoding scheme has been modified to include the resource assignment information because of the multi-skill nature of the algorithm. In addition, a genetic algorithm (GA) is also developed in this work for the purpose of comparisons. The computational experiments are performed on 216 test instances with varying complexity and characteristics generated for the purpose.

Findings

The results obtained after computations show that the TLBO has performed significantly better than GA in terms of average percentage deviation from the critical path-based lower bound for different combinations of three parameters, namely, skill factor, network complexity and modified resource strength.

Research limitations/implications

The modified TLBO proposed in this paper can be conveniently applied to any product or service organization wherein human resources are involved in executing project activities.

Practical implications

The developed model can suitably handle resource allocation problems faced in real-life large-sized projects usually administered in software development companies, consultancy firms, R&D-based organizations, maintenance firms, big construction houses, etc. wherein human resources are involved.

Originality/value

The current work aims to propose an effective metaheuristic for a more realistic version of MSRCPSP, in which resource requirements of activities may be more than one. Moreover, to enhance the exploration and exploitation capabilities of the original TLBO, the authors use two additional concepts, namely, self-study and examination in the search process.

Details

Journal of Modelling in Management, vol. 14 no. 4
Type: Research Article
ISSN: 1746-5664

Keywords

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Article
Publication date: 19 October 2020

Sayyid Ali Banihashemi and Mohammad Khalilzadeh

The purpose of this paper is to evaluate project activities' efficiency in different execution modes for the optimization of time–cost-quality and environmental impacts…

Abstract

Purpose

The 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/approach

This 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.

Findings

The 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 implications

This 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/value

In 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.

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Article
Publication date: 9 January 2019

Amir Hossein Hosseinian, Vahid Baradaran and Mahdi Bashiri

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t…

Abstract

Purpose

The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously.

Design/methodology/approach

The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method.

Findings

Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values.

Practical implications

The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects.

Originality/value

Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Details

Journal of Modelling in Management, vol. 14 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

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Article
Publication date: 7 June 2019

Felix T.S. Chan, Zhengxu Wang, Yashveer Singh, X.P. Wang, J.H. Ruan and M.K. Tiwari

The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also…

Abstract

Purpose

The purpose of this paper is to develop a model which schedules activities and allocates resources in a resource constrained project management problem. This paper also considers learning rate and uncertainties in the activity durations.

Design/methodology/approach

An activity schedule with requirements of different resource units is used to calculate the objectives: makespan and resource efficiency. A comparisons between non-dominated sorting genetic algorithm – II (NSGA-II) and non-dominated sorting genetic algorithm – III (NSGA-III) is done to calculate near optimal solutions. Buffers are introduced in the activity schedule to take uncertainty into account and learning rate is used to incorporate the learning effect.

Findings

The results show that NSGA-III gives better near optimal solutions than NSGA-II for multi-objective problem with different complexities of activity schedule.

Research limitations/implications

The paper does not considers activity sequencing with multiple activity relations (for instance partial overlapping among different activities) and dynamic events occurring in between or during activities.

Practical implications

The paper helps project managers in manufacturing industry to schedule the activities and allocate resources for a near-real world environment.

Originality/value

This paper takes into account both the learning rate and the uncertainties in the activity duration for a resource constrained project management problem. The uncertainty in both the individual durations of activities and the whole project duration time is taken into consideration. Genetic algorithms were used to solve the problem at hand.

Details

Industrial Management & Data Systems, vol. 119 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

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