Solving resource constrained project scheduling problem (RCPSP) has important role in the context of project scheduling. Considering a single objective RCPSP, the goal is to find a schedule that minimizes the makespan. This is NP-hard problem (Blazewicz et al., 1983) and one may use meta-heuristics to obtain a global optimum solution or at least a near-optimal one. Recently, various meta-heuristics such as ACO, PSO, GA, SA etc have been applied on RCPSP. Bee algorithms are among most recently introduced meta-heuristics. This study aims at adapting artificial bee colony as an alternative and efficient optimization strategy for solving RCPSP and investigating its performance on the RCPSP. To evaluate the artificial bee colony, its performance is investigated against other meta-heuristics for solving case studies in the PSPLIB library. Simulation results show that the artificial bee colony presents an efficient way for solving resource constrained project scheduling problem.
Differential Search (DS) algorithm is a new meta-heuristic for solving real-valued numerical optimization. This paper introduces a new method based on DS for solving Resource Constrained Project Scheduling Problem (RCPSP). The RCPSP is aimed to schedule a set of activities at minimal duration subject to precedence constraints and the limited availability of resources. The proposed method is applied to PSPLIB case studies and its performance is evaluated in comparison with some of state of art methods. Experimental results show that the proposed method is effective. Also, it is among the best algorithms for solving RCPSP.Growing Science Ltd. All rights reserved. 5
Cette version est miseà votre disposition conformémentà la politique de libre accès aux publications des organismes subventionnaires canadiens et québécois. Avant de citer ce rapport, veuillez visiter notre site Web (https://www. gerad.ca/fr/papers/G-2017-41) afin de mettreà jour vos données de référence, s'il aété publié dans une revue scientifique. This version is available to you under the open access policy of Canadian and Quebec funding agencies. Before citing this report, please visit our website (https://www.gerad. ca/en/papers/G-2017-41) to update your reference data, if it has been published in a scientific journal. Les textes publiés dans la série des rapports de recherche Les Cahiers du GERAD n'engagent que la responsabilité de leurs auteurs.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.