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Machine Learning Heuristic for Solving Multi-Mode Resource-Constrained Project Scheduling Problems

Adamu, Patience I. and Agarana, M. C. and Okagbue, H. I. (2018) Machine Learning Heuristic for Solving Multi-Mode Resource-Constrained Project Scheduling Problems. In: International MultiConference of Engineers and Computer Scientists 2018 Vol I, March 14-16, 2018, Hong Kong.

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The non-preemptive resource-constrained project scheduling problem is considered in this work. It is assumed that each activity has many ways of execution and the objective is to find a schedule that minimizes the project’s completion time (multi-mode RCPSP). Methods that are based on priority rules do not always give the needed very good results when used to solve multi-mode RCPSP. In solving large real-life problems quickly though, these methods are absolutely necessary. Hence good methods based on priority rules to get the primary results for metaheuristic algorithms are needed. This work presents a novel method based on priority rules to calculate the primary solutions for metaheuristic algorithms. It is a machine learning approach. This algorithm first of all uses Preprocessing to reduce the project data in order to speed up the process. It then employs a mode assignment procedure to obtain the mode of each job. After which the algorithm uses machine learning priority rule to get the precedence feasible activity list of the project’s tasks. Finally, it then uses the Serial Schedule Generation Scheme to get the total completion time of the project. In our experiments, we use our algorithm to solve some problems in the literature that was solved with metaheuristic procedures. We compared our results with the initial solutions the authors started with, and our results competes favorably with the initial solutions, making our algorithm a good entry point for metaheuristic procedures.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: machine learning, project scheduling, resource constraints, metaheuristic procedures
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Engineering, Science and Mathematics > School of Mathematics
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 07 May 2018 14:47
Last Modified: 07 May 2018 14:47

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