An adaptable scheduling algorithm for flexible flow lines
Operations Research
BPSS: a scheduling support system for the packaging industry
Operations Research
An evaluation of sequencing heuristics in flow shops with multiple processors
Computers and Industrial Engineering
Hybrid flow shop scheduling: a survey
Computers and Industrial Engineering
Scheduling of drilling operations in printed circuit board factory
Computers and Industrial Engineering
Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines
Computers and Operations Research
Dispatching in flowshops with bottleneck machines
Computers and Industrial Engineering
Computers and Operations Research
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
An exact approach for batch scheduling in flexible flow lines with limited intermediate buffers
Mathematical and Computer Modelling: An International Journal
Computers and Industrial Engineering
Minimizing resource consumption on uniform parallel machines with a bound on makespan
Computers and Operations Research
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This study developed a bottleneck-based heuristic (BBFFL) to solve a flexible flow line problem with a bottleneck stage, where unrelated parallel machines exist in all the stages, with the objective of minimizing the makespan. The essential idea of BBFFL is that scheduling jobs at the bottleneck stage may affect the performance of a heuristic for scheduling jobs in all the stages. Therefore, in BBFFL, a variant of Johnson's rule is used to develop a bottleneck-based initial sequence generator (BBISG). Then, a bottleneck-based multiple insertion procedure (BBMIP) is applied to the initial sequence to control the order by which jobs enter the bottleneck stage to be the same as that at the first stage. Five experimental factors were used to design 243 different production scenarios and 10 test problems were randomly generated in each scenario. These test problems were used to compare the performance of BBFFL with several well-known heuristics. Computational results show that the BBFFL significantly outperforms all the well-known heuristics.