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1.
Memetic self-adaptive firefly algorithm
Iztok Fister, Xin-She Yang, Janez Brest, Iztok Fister, 2013, independent scientific component part or a chapter in a monograph

Keywords: firefly algorithms, memetic algorithms, self-adaptation, local search, graph 3-coloring problem
Published: 10.07.2015; Views: 766; Downloads: 69
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2.
A modular hybrid approach to employee timetabling
Drago Bokal, Gašper Fijavž, Bor Harej, Andrej Taranenko, Klemen Žagar, 2008, published scientific conference contribution

Abstract: We consider a classical employee timetabling problem: a set of employees with various skill levels, qualifications, workload and availability distributions has to be assigned to a set of shifts, each requiring a prescribed number of qualified employees and spanning a given time period. The novelty of our approach is a hybrid combination of the methods proposed in bibliography, such that we leverage the advantages of known methods while minimizing their disadvantages. Thus, we first apply the generalized local hill-climbing with randomized neighborhoods to quickly reach the vicinity of local optima, and then use the tabu search to explore more of the search space around those solutions. Our experiments show that the resulting hybrid technique performs better than the comparable approaches presented in bibliography due to the hybrid nature of the technique. In addition, we propose a modular design that utilizes dependency injection to compose the the search algorithm. Together with careful modeling, this approach allows for constant-time evaluation of each possible step in the neighborhood and for an easy evaluation of different hybrid combinations that can be combined and parameterized at runtime.
Keywords: operacijsko raziskovanje, razporejanje zaposlenih, urniki, lokalna optimizacija, operations research, employee timetabling, generalized local hill-climbing, tabu search, hybrid local optimization
Published: 10.07.2015; Views: 266; Downloads: 31
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3.
Hybridization of stochastic local search and genetic algorithm for human resource planning management
Andrej Škraba, Vladimir Stanovov, Eugene Semenkin, Davorin Kofjač, 2016, original scientific article

Abstract: Background and Purpose: The restructuring of human resources in an organization is addressed in this paper, because human resource planning is a crucial process in every organization. Here, a strict hierarchical structure of the organization is of concern here, for which a change in a particular class of the structure influences classes that follow it. Furthermore, a quick adaptation of the structure to the desired state is required, where oscillations in transitions between classes are not desired, because they slow down the process of adaptation. Therefore, optimization of such a structure is highly complex, and heuristic methods are needed to approach such problems to address them properly. Design/Methodology/Approach: The hierarchical human resources structure is modeled according to the principles of System Dynamics. Optimization of the structure is performed with an algorithm that combines stochastic local search and genetic algorithms. Results: The developed algorithm was tested on three scenarios; each scenario exhibits a different dynamic in achieving the desired state of the human resource structure. The results show that the developed algorithm has successfully optimized the model parameters to achieve the desired structure of human resources quickly. Conclusion: We have presented the mathematical model and optimization algorithm to tackle the restructuring of human resources for strict hierarchical organizations. With the developed algorithm, we have successfully achieved the desired organizational structure in all three cases, without the undesired oscillations in the transitions between classes and in the shortest possible time.
Keywords: stochastic local search, system dynamics, human resources, simulation
Published: 04.04.2017; Views: 339; Downloads: 198
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4.
Heuristics for NP-hard optimization problems
Janez Žerovnik, 2015, original scientific article

Abstract: We provide several examples showing that local search, the most basic metaheuristics, may be a very competitive choice for solving computationally hard optimization problems. In addition, generation of starting solutions by greedy heuristics should be at least considered as one of very natural possibilities. In this critical survey, selected examples discussed include the traveling salesman, the resource-constrained project scheduling, the channel assignment, and computation of bounds for the Shannon capacity.
Keywords: optimization, metaheuristics, local search, greedy construction, traveling salesman problem
Published: 17.11.2017; Views: 262; Downloads: 61
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