An efficient hybrid algorithm for resource-constrained project scheduling

Chen, Wang; Shi, Yufeng; Teng, Hong‐Fei; Lan, Xiao-ping; Li-chen, HU

doi:10.1016/j.ins.2009.11.044

Cited by 107 publications

(32 citation statements)

References 49 publications

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“…This set of heuristics include works that use Genetic Algorithms (GA) [42,43,44], ACO algorithms for solving the RCPSP problem [45], and the majority of these works include other methods such as Forward-Backward improvement (FBI), or the Latest Table 10. Average deviation (%) from the critical path lower bound for j60.sm.…”

Section: Experiments 3: Comparison To the State-of-the-artmentioning

confidence: 99%

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

González-Pardo

Ser

Camacho

2017

Applied Soft Computing

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Section: Experiments 3: Comparison To the State-of-the-artmentioning

confidence: 99%

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

González-Pardo

Ser

Camacho

2017

Applied Soft Computing

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“…However, the parallel SGS has the ability to produce schedules of better average quality because it utilizes resources as early as possible. Under this way, a more compact schedules may be obtained [4] . Hence, one can select one of them for schedule generation.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Nowadays, different types of hybrid methods have been proposed to solve the RCPSP problems. The ANGEL [3] , ACOSS [4] , Neurogenetic [5] , and GA-Hybrid [6] are known as some of representative hybrid methods presented in literature.…”

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confidence: 99%

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An ABC-Genetic method to solve resource constrained project scheduling problem

Akbari¹,

Zeighami²,

Akbari³

2012

AIR

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The aim of this work is to study the effect of hybridization on the performance of the Artificial Bee Colony (ABC) as a recently introduced metaheuristic for solving Resource Constrained Project Scheduling Problem (RCPSP). For this purposes, the ABC is combined with a Genetic Algorithm (GA). At the initial time, the algorithm generates a set of schedules randomly. The initial solution has been evaluated against constraints, and the infeasible solutions have been resolved to feasible ones. Then, the initial schedules are to be improved iteratively using a hybrid method until termination condition is met. The proposed method works by integrating the ABC and GA search processes. The GA method updates schedules by including the best solutions found by the ABC approach. Next, the ABC method picks the solutions found by GA search. The new methodological approach is used by the algorithm to maintain the priorities of the activities in feasible ranges. The performance of the proposed algorithm has been compared against a set of state-of-art algorithms. The simulation results have demonstrated that the proposed algorithm provides an efficient way for solving RCPSP and produce competitive results compared to other algorithms investigated in this work.

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“…Also, the study of interaction between evolution and learning for solving optimization problems has been attracting much attention. The diversity of these approaches has motivated Li-Ning Xing et al [17]. The authors proposed a frame work called KnowledgeBased Heuristic Searching Architecture (KBHSA), which integrates knowledge model and heuristic searching model to search an optimal solution.…”

Section: Schedulingmentioning

confidence: 99%

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

B.¹,

Baskaran²

2011

IJCIS

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Ant colony optimization (ACO) takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.

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An efficient hybrid algorithm for resource-constrained project scheduling

Cited by 107 publications

References 49 publications

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

An ABC-Genetic method to solve resource constrained project scheduling problem

Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

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