Future Makespan Heuristic for job shop scheculing problem

Abstract

Job shop scheduling problem is well known NP-hard problem, this problem is difficult to find the good solution but industrials require the solution to competition and survive in the market. This paper focuses on developing algorithm to solve job shop scheduling problem. The new algorithm is called Future Makespan Heuristic. The concept of new algorithm is jobs are evaluated the makespan before selection one job to the schedule. Each iteration of selection, the new makespan is less than or equal to the makespan of the previous iteration. Iteration makes decision tree by using the candidate jobs (available jobs). Each branch of tree, the candidate jobs are evaluated the makespan by using basic heuristic such as dispatching rules. By this technique, the makespan is improved for all time of scheduling. The experiment reveals the solution is highly improved when comparing with original dispatching rules. Moreover, the computation time of new algorithm is short. The mathematic proof shows that the new algorithm can find the high quality solution in polynomial time algorithm if the evaluating procedure is polynomial time algorithm.

Metaheuristics as enablers for VNF scheduling in the network slice set up process

“…Based on the above observations, heuristic methods fit well to the JSSP, as they can provide a near-optimal solution by constructing solutions according to greedy decisions [51]. Heuristics have some remarkably interesting advantages.…”

“…The natureinspired methods, as their name implies, adopt their behavior from various nature functionalities and have been extensively used in the last decade to solve various optimisation problems in many research fields such as cloud computing [52], power consumption [53] and data-mining [54]. According to [51], nature-based methods can be classified into four divisions: Evolution-based, inspired by the theory of natural evolution, Physics-based, mimicking physical rules in the universe, Swarm-based, mimicking the social behavior of groups of animals, and Human-based, inspired by the advancement in level of searching strategy. In this study, we focus on the most efficient metaheuristic algorithms of the Evolution-based and the Swarm-based categories, namely the genetic algorithm and ant colony optimisation.…”

Estrategias de planificación para datos y procesos en computación Grid: estado del arte

“…Entre los criterios de rendimiento se incluyen: utilización de CPU de los recursos grid, balanceo de carga, uso del sistema, tiempo de cola, tiempo de respuesta, rendimiento acumulado, tiempo de espera, disponibilidad, escalabilidad, eficiencia de la planificación [31] entre otros; y entre los criterios de optimización se incluyen el Makespan Bassem [32], Mungwattana [33], tiempo de flujo, utilización de recursos, equilibrio de carga, tiempo de respuesta, tiempo total de realización ponderado, ponderado del número de trabajos tardíos, tiempo de respuesta ponderado, entre otros.…”

“…De acuerdo a lo tratado anteriormente y debido a la necesidad de optimizar el conjunto de criterios que pueden ser tenidos en cuenta a la hora de planificar [33], se puede decir que la planificación grid es multi-objetivo [34] en su formulación general, es por ello que se busca una buena solución de compromiso entre los criterios cuando se consideren en conjunto. En [35]se observa el tratamiento que se da a criterios tales como: el tiempo de ejecución, el consumo de energía y el coste económico.…”