In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the number of tardy jobs. Fuzzy sets are used to represent satisfaction grades for the objectives taking into consideration the preferences of...

In this paper, we propose a Pareto-based tabu search algorithm for multi-objective FJSP with Earliness/Tardiness (E/T) penalty. In the hybrid algorithm, several neighboring structure based approaches were proposed to improve the convergence capability of the algorithm while keep population diversity of the last Pareto archive set. In addition, an external Pareto archive was developed to record ...

Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as o...

This paper describes a constraint programming approach to solving a scheduling problem with earliness and tardiness cost using a problem specific search procedure. The presented algorithm is tested on a set of randomly generated instances of the job shop scheduling problem with earliness and tardiness costs. The experiments are executed also for three other algorithms, and the results are then ...

The unrelated parallel machine scheduling (PMS) problem is essential for the manufacturing industry. Scheduling will save company resources, especially time management. By solving problems quickly and precisely, can get more profit. On that note, this paper focused on PMS problems, which did not consider inherent uncertainty in processing set up by minimizing makespan tardiness. This aimed to m...

In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all non-dominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one pheromone matrix for each type of optimization criterion. The matrices are derived fr...

Four heuristic algorithms are developed for solving the generalized version of tardiness flow shop problems. We consider the generalized tardiness flow shop model with minimization of the total tardiness as its performance measure. We modify the concept of cost over time (COVERT) for the generalized version of the flow shop tardiness model and employ this concept for developing four algorithms....

We consider uniform parallel machine scheduling problems with unit-length jobs where every job is only allowed to be processed on a specified subset of machines. We develop efficient methods to solve problems with various objectives, including minimizing a total tardiness function, a maximum tardiness function, total completion time, the number of tardy jobs, the makespan, etc.

We consider the problem of scheduling of n jobs to minimize the total earliness and tardiness penalty. The scheduling objective is to minimize the sum of job dependent earliness and tardiness penalties.. After determining the scheduling for single machine common due date, using neural network, the result shows that the neural network solution performs very well.