A Permutation-Based Bees Algorithm for Solving Resource-Constrained Project Scheduling Problem

Author:

Nemmich Mohamed Amine1ORCID,Debbat Fatima1,Slimane Mohamed2

Affiliation:

1. Department of Computer Science, University Mustapha Stambouli of Mascara, Mascara, Algeria

2. Université de Tours, Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Tours, France

Abstract

In this article, a novel Permutation-based Bees Algorithm (PBA) is proposed for the resource-constrained project scheduling problem (RCPSP) which is widely applied in advanced manufacturing, production planning, and project management. The PBA is a modification of existing Bees Algorithm (BA) adapted for solving combinatorial optimization problems by changing some of the algorithm's core concepts. The algorithm treats the solutions of RCPSP as bee swarms and employs the activity-list representation and moves operators for the bees, in association with the serial scheduling generation scheme (Serial SGS), to execute the intelligent updating process of the swarms to search for better solutions. The performance of the proposed approach is analysed across various problem complexities associated with J30, J60 and J120 full instance sets of PSPLIB and compared with other approaches from the literature. Simulation results demonstrate that the proposed PBA provides an effective and efficient approach for solving RCPSP.

Publisher

IGI Global

Subject

Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications

Reference61 articles.

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

3. Azarbad, M., Ebrahimzade, A., & Izadian, V. (2011). Segmentation of infrared images and objectives detection using maximum entropy method based on the bee algorithm. International Journal of Computer information Systems and industrial Management Applications, 3, 26-33.

4. Tabu Search Algorithms and Lower Bounds for the Resource-Constrained Project Scheduling Problem

5. Scheduling subject to resource constraints: classification and complexity

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