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

Paweł B Myszkowski,Krzysztof Oślizło,Łukasz P Olech,Marek E Skowroński

doi:10.1007/s00500-014-1455-x

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.

Highlights

Resource-constrained project scheduling problem (RCPSP) is one of the most investigated types of scheduling problems
It could lead to conclusion that pheromone update method is not as crucial as for classical ant colony optimization (ACO)
We needed to distinguish the best results obtained for duration optimization (DO) and cost optimization (CO) modes from balanced optimization (BO) mode, because no heuristic scheduling method has been proposed for BO

Summary

Introduction

Resource-constrained project scheduling problem (RCPSP) is one of the most investigated types of scheduling problems. Description of RCPSP in Blazewicz et al (1983) as combinatorial, NP-hard problem encouraged scientists to find good enough methods that would be able to produce approximate, (sub)optimal solutions in finite, polynomial computing time. Those methods are called (meta)heuristics and are used to solve problems for which finding optimal solution in an acceptable time is impossible. Beside Evolutionary Algorithms (EA), Taboo Search (TS), Simulated Annealing (SA) and some other techniques, metaheuristics contain a group of methods called swarm intelligence methods, as particle swarm optimization (PSO) or ant colony optimization (ACO) Those methods assume that separate individuals, representing given problem solutions, can interact with each other and cooperate to achieve their common goals. The optimization goal is to find the optimal path between food and nest, while definition of path’s quality is varied and dependent on the considered problem

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Conclusion