An Artificial Bee Colony with Random Key for Resource-Constrained Project Scheduling

Shi, Yufeng; Qu, Fu-Zhen; Chen, Wang

doi:10.1007/978-3-642-15597-0_17

Cited by 17 publications

(1 citation statement)

References 20 publications

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“…Moreover, the method uses SSGS to generate the feasible schedules (Akbari, Zeighami, & Ziarati, 2010). 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).…”

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

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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).

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Section: Bees Colony Optimization (Bco) and Rcpspmentioning

confidence: 99%