Flexible job shops are characterized by the existence of alternative –not necessarily identicalmachines, which permits parts to have different alternative routes in the shop. In a dynamic scheduling situation in which jobs continuously arrive over time, part routing rules are needed to deal with such routing flexibility as much as sequencing rules which define the order by which parts are admit...

When faced with a multiobjective optimization problem, it is necessary to consider the decision-maker preferences in order propose best compromise solution. We flexible job shop scheduling problem and that represented using non-compensatory reference level-based preference model. show how integrating this model into genetic algorithm allows obtain solutions surpass more aspiration levels when c...

The job-shop scheduling problem has attracted many researchers’ attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of par...

General job-shop scheduling is a difficult problem to be solved. The complexity of the problem is highlighted by the fact that in a general job-shop having m machines and n jobs the total number of schedules can be as high as (n!), hence if “n=20 m=10” the number of possible solutions is 7.2651x10. Many approaches such as Branch and Bound, Simulated Annealing, Tabu Search and others have been t...

The n-job, m-machine flow shop scheduling problem is one of the most general job scheduling problems. This study deals with the multi objective optimization with the criteria of makespan minimization and minimization of total weighted flow time for the flow shop scheduling problem. The n-job mmachine problem is known to be NP hard problem, several meta heuristics have been applied so far in the...