A general flow shop scheduling problem with consideration of position-based learning effect and multiple availability constraints

In this paper, a more general version of the flow shop scheduling problem with the objective of minimizing the total flow time is investigated. In order to get closer to the actual conditions of the problem, some realistic assumptions including non-permutation scheduling, learning effect, multiple availability constraints, and release times are considered. It is assumed that the real processing time of each job on a machine depends on the position of that job in the sequence, and after processing a specified number of jobs at each machine, an unavailability period is occurring because of maintenance activities. Moreover, it is supposed that each job may not be ready for processing at time zero and may have a release time. According to these assumptions, a new mixed integer linear programming (MILP) model is proposed to formulate the problem. Due to the high complexity of the problem, a heuristic method and a simulated annealing algorithm are presented to find the nearly optimal solutions for medium- and large-sized problems. To obtain better and more robust solutions, the Taguchi method is used in order to calibrate the simulated annealing algorithm parameters. Finally, the computational results are provided for evaluating the performance and effectiveness of the proposed solution methods.     

Minimizing total flow time for the non-permutation flow shop scheduling problem with learning effects and availability constraints

The flow shop scheduling problem is an attractive subject in the field of scheduling, which has attracted the attention of many researchers in the past five decades. In this paper, the non-permutation flow shop scheduling problem with the learning effects and machine availability constraints has been studied for minimizing the total flow time as a performance measure. First, a mixed integer linear programming model has been proposed for the modeling of the problem and then, an effective improving heuristic method, which is able to find proper non-permutation solutions, has been presented. Finally, the computational results are used for evaluation the performance and effectiveness of the proposed heuristic.     

Transductive multi-label learning from missing data using smoothed rank function

Pattern Analysis and Applications - In this paper, we propose two new algorithms for transductive multi-label learning from missing data. In transductive matrix completion (MC), the challenge is...