Maximization of the assembly line efficiency using an approach based on Genetic Algorithm

Optimisation of Simple Assembly Line Balancing Problem Type E: A Systematic Literature Review

“…Computational tests on 10 test sets taken from the literature showed that h-GA and neighbourhood GA (n-GA) outperformed classical GA on all test problems, confirming the ability of these strategies to improve the solution quality of classical GA. Recently, Belkharroubi and Yahyaoui [30] proposed a genetic algorithm-based approach to deal with SALBP-E with the primary objective of maximising assembly line efficiency. This method involves determining the interval of the potential number of workstations [𝑀 𝑚𝑖𝑛 , 𝑀 𝑚𝑎𝑥 ] and then searching for the best cycle time for 𝑀 𝑚𝑖𝑛 , 𝑀 𝑚𝑎𝑥 , and all values in between.…”

“…Solution methods Type of methods SO MO 1 2 3 4 5 6 7 SL MuL MiL S U T [37] SALBP-E x x x x Fix-and-relax Metaheuristic [36] UALBP-E x x x x Branch and Bound Exact [31] SALBP-E x x x x Excel Software tool [42] Fuzzy SALBP-E x x x x GA Metaheuristic [51] SALBP-E x x x x GA Metaheuristic [32] MALBP-E x x x x x PN-based heuristic Heuristic [29] SALBP-E x x x x x x x CPLEX Software tool [44] PTALBP-E x x x x ACO based approach Metaheuristic [40] ALBE-RC x x x x IP 1 and EIP 2 derived heuristics Heuristic [48] SALBP-E x x x x x Hybrid GA Metaheuristic [49] SALBP-E x x x x x h-GA 3 and n-GA 4 Metaheuristic [30] SALBP-E x x x x GA based approach Metaheuristic [45] SALBP-E x x x x Excel Software tool 1 Iterative Procedure. 2 Enhanced Iterative Procedure.…”

“…Reducing the number of workstations or line cycle time is equivalent to maximising assembly line efficiency, i.e., minimising idle time as much as possible [17], and it also allows us to define our SALB-E problem as follows [28,29,30]: (8) and/or 𝐶 𝑇𝑚𝑖𝑛 ≤ 𝐶 𝑇 ≤ 𝐶 𝑇𝑚𝑎𝑥 (9) Where (8) represents the number of workstations necessary and (9) represents the necessary cycle time.…”