Hybrid ant colony optimization in solving multi-skill resource-constrained project scheduling problem

Abstract

In this paper, hybrid ant colony optimization (HAntCO) approach in solving multi-skill resource-constrained project scheduling problem (MS-RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with ant colony optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS-RCPSP. Experiments have been performed using artificially created dataset instances based on real-world ones. We published those instances that can be used as a benchmark. Presented results show that ACO-based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.

iMOPSE: a library for bicriteria optimization in Multi-Skill Resource-Constrained Project Scheduling Problem

“…However, in research the simple EDU dataset is no challenge. Thus, we provided and published dataset of 36 instances (Myszkowski et al 2015b) (see Table 3) to compare results of given methods (e.g., Myszkowski et al 2015a;Myszkowski and Siemieński 2016). It differs not only in number of tasks, but also in number of resources, relations and used skills.…”

“…There are several types of (meta)heuristics that can be effectively applied to MS-RCPSP problem, such as Priority Rules (Skowroński et al 2013b), Greedy algorithm (Myszkowski and Siemieński 2016;Myszkowski et al 2015b), Tabu Search (Skowroński et al 2013a), Simulated Annealing (Yannibelli and Amandi 2013), Genetic Algorithms (GA) (Skowroński and Myszkowski 2013), Bee Colony Optimization (Ziarati et al 2011), Particle Swarm Optimization (Zhang et al 2006), Ant Colony Optimization (Myszkowski et al 2015a), Differential Evolution (Myszkowski et al 2018), Greedy Randomized Adaptive Search Procedure (GRASP) (Myszkowski and Siemieński 2016), Coevolutionary Algorithms (Myszkowski et al 2017a) and many others. It is worth mentioning that such methods give solution in acceptable time, but the main disadvantage is the lack of determinism.…”

Co-Evolutionary Algorithm solving Multi-Skill Resource-Constrained Project Scheduling Problem

“…Fig. 1: Example of skill matrix [9] In the skill matrix presented in Fig. 1, skills required by task to be performed have been written over task definition.…”

“…More formal definition of MS-RCPSP as optimization problem has been presented in II-A -and is based on work [6] [9]. The MS-RCPSP has been defined in cooperation with international corporation (Volvo Group IT).…”

“…Thus, several types of (meta)heuristic methods can be effectively applied. There can be found applications of (MS-)RCPSP in literature based on: heuristics [3] [12], tabu search [11], evolutionary algorithms (EA) with specialized operators [10], (hybridized) ant colony optimization [9], teaching-learningbased optimization algorithm [13], differential evolution [14], hybridized differential evolution [6] and many others.…”