Basic techniques for lot streaming
Operations Research
Computers and Operations Research
Identical machine scheduling to minimize the number of tardy jobs when lot-splitting is allowed
Proceedings of the 21st international conference on Computers and industrial engineering
Simulation modeling of a dynamic job shop rescheduling with machine availability constraints
Proceedings of the 23rd international conference on on Computers and industrial engineering
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
A hybrid particle swarm optimization for job shop scheduling problem
Computers and Industrial Engineering
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
A new particle swarm optimization for the open shop scheduling problem
Computers and Operations Research
Lot streaming for product assembly in job shop environment
Robotics and Computer-Integrated Manufacturing
An evolutionary algorithm for assembly job shop with part sharing
Computers and Industrial Engineering
A resource-constrained assembly job shop scheduling problem with Lot Streaming technique
Computers and Industrial Engineering
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Computers and Operations Research
A high performing metaheuristic for job shop scheduling with sequence-dependent setup times
Applied Soft Computing
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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.