This paper deals with Flow-shop Sequence-Dependent Group Scheduling and worker assignment problem. Flow-shop allows the process of a set of families of products applying the group technology concept to reduce setup costs, lead times, and work-in-process inventory costs. The worker assignment problem deals with assigning workers to workstations considering their different abilities and learning effect. The proposed model in this paper considers different objectives. The decision problems in this cellular manufacturing system are the jobs scheduling within of own group, the group scheduling and the workers assignment to the machines. The aim of this paper is to consider a more realistic profile of heterogeneous workers introducing the learning effect in the joint group scheduling and workers assignment problem. A mathematical model and an evolutionary procedure has been developed to solve this problem. A benchmark of test cases having different numbers of machines, groups, jobs, worker skills and learning index, has been taken into account to compare the efficiency of the proposed algorithm with two well known procedures.

In this paper, group scheduling problem in no-wait flexible flowshop is considered by considering two stages with group sequence-dependent setup times and random breakdown of the machines. Genetic algorithm and simulated annealing based heuristics have been proposed to solve the problem. The primary objective of scheduling is to minimize the maximum completion time of the jobs for two classes of small and large scale problems. Computational results show that both GA and SA algorithms perform properly, but SA appeared to provide better results for both small and large scale problems.

Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.