Search

Abstract: Very often, studies of job shop scheduling problem (JSSP) ignore assembly relationship and lot splitting. If an assembly stage is appended to JSSP for the final product, the problem then becomes assembly job shop scheduling problem (AJSSP). To allow lot splitting, lot streaming (LS) technique is examined in which jobs may be split into a number of smaller sub-jobs for parallel processing on different stages such that the system performance may be improved. In this study, the system objective is defined as the makespan minimization. In order to investigate the impact of LS on the system objective under different real-life operating conditions, part sharing ratio (PSR) and system congestion index (SCI) are considered. PSR is used to differentiate product-specific components from general-purpose, common components, and SCI for creating different starting conditions of the shop floor. Both PSR and CSI are useful as part sharing (also known as component commonality) is a common practice for manufacturing with assembly operations and system loading is a significant factor in influencing the shop floor performance. Since the complexity of AJSSP is NP-hard, a hybrid genetic algorithm (HGA) and a hybrid particle swarm optimization (HPSO) are proposed and developed to solve AJSSP in consideration of LS technique. Computational results show that for all test problems under various system conditions, HGA can significantly outperform HPSO. Also, equal-sized lot splitting is found to be the most beneficial LS strategy especially for medium-to-large problem size. [Copyright &y& Elsevier]

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem's characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider a job-shop scheduling problem with mixed blocking constraints. Contrary to most previous studies, where no blocking or only one type of blocking constraint was used among successive operations, we assume that, generally, we may address several different blocking constraints in the same scheduling problem depending on the intermediate storage among machines, the characteristics of the machines, the technical constraints, and even the jobs. Our objective was to schedule a set of jobs to minimize the makespan. Thus, we propose, for the first time, a mathematical model of the job-shop problem taking into account the general case of mixed blocking constraints, and the results were obtained using Mosel Xpress software. Then, after explaining why and how groups of jobs have to be processed, a blocking constraint conflict-free warranted evaluation function is proposed and tested with the particle swarm optimization and genetic algorithm methods. The results prove that we obtained a near-optimal solution to this problem in a very short time. [ABSTRACT FROM AUTHOR]