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
Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling
Computers and Industrial Engineering
Scheduling rules for dynamic shops that manufacture multi-level jobs
Computers and Industrial Engineering
Computers and Operations Research
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
Path planning on a cuboid using genetic algorithms
Information Sciences: an International Journal
A particle swarm-based genetic algorithm for scheduling in an agile environment
Computers and Industrial Engineering
Advances in Engineering Software
Applications of particle swarm optimisation in integrated process planning and scheduling
Robotics and Computer-Integrated Manufacturing
A decomposition algorithm for the single machine total tardiness problem
Operations Research Letters
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To ensure effective shop floor production, it is vital to consider the capital investment. Among most of the operational costs, resource must be one of the critical cost components. Since each operation consumes resources, the determination of resource level is surely a strategic decision. For the first time, the application of Lot Streaming (LS) technique is extended to a Resource-Constrained Assembly Job Shop Scheduling Problem (RC_AJSSP). In general, AJSSP first starts with Job Shop Scheduling Problem (JSSP) and then appends an assembly stage for final product assembly. The primary objective of the model is the minimization of total lateness cost of all final products. To enhance the model usefulness, two more experimental factors are introduced as common part ratio and workload index. Hence, an innovative approach with Genetic Algorithm (GA) is proposed. To examine its goodness, Particle Swarm Optimization (PSO) is the benchmarked method. Computational results suggest that GA can outperform PSO in terms of optimization power and computational effort for all test problems.