Using Bees Algorithm to Solve the Resource Constrained Project Scheduling Problem in PSPLIB

Sadeghi, Amir; Kalanaki, Abolfazl; Noktehdan, Azadeh; Samghabadi, Azamdokht Safi; Barzinpour, Farnaz

doi:10.1007/978-3-642-24999-0_67

Cited by 4 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed ABC algorithm uses a random number to select one of the two SGS methods, which is directly used to generate schedules (Shi, Qu, Chen, & Li, 2010). A bee algorithm introduced a new formula to evaluate the quality of the solutions found in the search space (Sadeghi, Kalanaki, Noktehdan, Samghabadi, & Barzinpour, 2011). Against the stochastic RCPSP, an artificial bee colony was proposed where the activity duration is variable with a certain probability.…”

Section: Bees Colony Optimization (Bco) and Rcpspmentioning

confidence: 99%

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Golab¹,

Gooya²,

Falou³

et al. 2022

10.5267/j.jpm

View full text Add to dashboard Cite

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

show abstract

Section: Bees Colony Optimization (Bco) and Rcpspmentioning

confidence: 99%

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Golab¹,

Gooya²,

Falou³

et al. 2022

10.5267/j.jpm

View full text Add to dashboard Cite

show abstract

“…Among other methods of solving RCPSP problem such as branch and bound method (Brucker et al , 1998), ant colony optimization approach (Yan et al , 2014) and the bees algorithm (Sadeghi et al , 2011) can be named. Cavalcante et al (2013) explored resource-constrained project scheduling problem with bounded multitasking.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The available optimization methods for solving this problem include many variables and limitations which decrease their practical efficiencies in solving problems with actual dimensions. Thus, using heuristic and meta-heuristic methods in solving these problems is reasonable (Sadeghi et al , 2011).…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm for solving resource-constrained project scheduling problems: a case study

Kasravi

Mahmoudi

Feylizadeh

2019

JAMR

View full text Add to dashboard Cite

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.

show abstract

“…A set of conditions and limitations are employed to make use of management knowledge in the field of project management while avoiding the creation of additional concepts or a new body of knowledge which would have little difference from their original version. The interference trend of project management can be investigated in three zones: production of project management knowledge, solutions for Enterprise Resource Planning and mobility of human resources [2]. The current paper investigates management elements for an optimal implementation in the case of Calcimine Company with the purpose of identifying and assessing such factors.…”

Section: Introductionmentioning

confidence: 99%

Assessing Market Development and Innovation Project Management Factors Using the PICEA-g Hybrid Evolutionary Multi-Criteria Decision Technique. The Calcimine Company Case Study

Ghaffari

Najafi

Eskandari

2017

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Project management includes the consideration of complex decision modes used in modern decision support techniques. The aim of this paper was to prioritize such factors and evaluate their effects on project management and optimal control. Their effect on management and optimal project control are evaluated in frame of a statistical hypothesis. A new algorithm, "IPICEA-g" is proposed for the assessment. A questionnaire is used for data collection distributed between 56 employees of the CALCIMINE Company. T-test, two-sentence test, ANP method, FUZZY SEAMATEL and the IPICEA-g hybrid algorithm, are employed for data analyzing. Results are further discussed and conclusions are drawn.

show abstract

Using Bees Algorithm to Solve the Resource Constrained Project Scheduling Problem in PSPLIB

Cited by 4 publications

References 20 publications

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

A novel algorithm for solving resource-constrained project scheduling problems: a case study

Assessing Market Development and Innovation Project Management Factors Using the PICEA-g Hybrid Evolutionary Multi-Criteria Decision Technique. The Calcimine Company Case Study

Contact Info

Product

Resources

About