Issue |
RAIRO-Oper. Res.
Volume 53, Number 5, November-December 2019
|
|
---|---|---|
Page(s) | 1877 - 1898 | |
DOI | https://doi.org/10.1051/ro/2018103 | |
Published online | 23 October 2019 |
Preemptive multi-skilled resource constrained project scheduling problem with hard/soft interval due dates
1
Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2
Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
3
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
* Corresponding author: afsharnb@alum.sharif.edu
Received:
13
August
2017
Accepted:
13
November
2018
This paper considers a preemptive multi-skilled resource constrained project scheduling problem in a just-in-time environment where each activity has an interval due date to be completed. In this problem setting, resuming a preempted activity requires an extra setup cost, while each time unit violation from the given due date incurs earliness or tardiness penalty. Also, processing cost of each skill to execute any activity depends on the assigned staff member to accomplish the skill. The objective function of the model aims to minimize the total cost of allocating staff to skills, earliness–tardiness penalties and preemption costs. Two integer formulations are proposed for the model which are compared in terms of number of variables, constraints and elapsed run-time to optimality. Furthermore, an ant colony based metaheuristic is developed to tackle real life scales of the proposed model. This algorithm relies on two intelligent local search heuristics. Parameters of the algorithm are calibrated using Taguchi method. The results of the experiments for the proposed algorithm confirm that the proposed algorithm has satisfying performance.
Mathematics Subject Classification: 90B35 / 90C11 / 90C59
Key words: Multi-skilled / project scheduling / due date / preemption / ant colony optimization
© EDP Sciences, ROADEF, SMAI 2019
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