Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Scheduling en línea minimizando la tardanza total ponderada para una máquina mediante metaheurística Grasp

Programar actividades es una tarea que consciente o inconscientemente todos aplicamos para planear nuestras actividades diarias. Para cualquier persona se hace necesario plantear y evaluar el orden en las que éstas deben ser desarrolladas dependiendo de factores externos y el criterio único del individuo. De igual forma, la planeación de la producción en la industria requiere organizar los trabajos de una manera óptima para aprovechar al máximo unos recursos limitados (máquinas por ejemplo) y cumplir con objetivos enmarcados por la estrategia de la organización (un ejemplo es la política Just-in-Time). Sin embargo, a diferencia de un individuo, la industria cuenta con especializadas herramientas analíticas cuyo único propósito es apoyar a la toma de decisiones, y a lo que se conoce comúnmente dentro del campo de la investigación de operaciones como scheduling.Ingeniero (a) IndustrialPregrad