This paper considers job shop scheduling problem followed by an assembly stage and Lot Streaming (LS). It is supposed here that a number of products have been ordered to be produced. Each product is assembled with a set of several parts. The production system includes two stages. The first stage is a job shop to produce parts. Each machine can process only one part at the same time. The second stage is an assembly shop that contains several parallel machines. Maintenance operations and access restrictions to machines in the first stage are also considered. The objective function is to minimize the completion time of all products (makespan). At first, this problem is described and modelled as a mixed integer linear programming, and GAMS software is applied to solve small-sized problems. Since this problem has been proved to be strongly NP-hard, two new algorithms based on GA and SA are developed to solve the medium- and large-sized problems. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor and well-known criterion. IMP. Various problems are solved by the proposed algorithms. Computational results reveal that both of the two proposed algorithms have good performance. However, the method based on the genetic algorithm performs better than the other proposed algorithm with respect to the objective functi پرونده مقاله

This paper focuses on robust scheduling for an assembly flow shop where assembling times are uncertain. The considered manufacturing environment is a two-stage production system that consists of a processing stage followed by an assembly stage. There are two parallel machines in the first stage to process the components followed by an assembly stage wherein, the components are assembled to products. The majority of scheduling research considers a deterministic environment with pre-known and fixed data. However, in real-world condition, several kinds of uncertainties should be considered. In this article, the scheduling problem is tackle under uncertainty and the assembling time of each product at the second stage is the source of uncertainty. The problem is described and formulated as a mixed-integer linear programing model under deterministic condition. After that, the uncertainty issue is discussed and the robust scheduling procedure is introduced to minimize the maximum completion time of all products based on the min–max regret approach. To solve the robust scheduling an exact method is proposed to solve the problem on the small scales. Moreover, two approximate methods are modified and used to solve this NP-hard problem on the small and practical scales. The performances of the proposed methods are evaluated by several numerical examples taken from valid references. Computational results indicate that the proposed robust scheduling methods provide effective hedges against processing time uncertainty while maintaining excellent expected makespan performance. پرونده مقاله