READ ME FILE OF THE ARTICLE: Benders' Decomposition for the balancing of assembly lines with stochastic demand

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The dataset in folder "Instances" contains 80 instances (numbered from 1 to 46, 48 to 60, 66 to 75, 81 to 90, and 101). Instance names are organized in form

instance_n=A_B_C_D_E

in which A is the number of tasks, B is the intance number, C is the ordering strength (OS), D is the average processing (PL), and E is the station length multiplier (LM).

Each instance contains the elements:
First line: number of tasks (NT)
Second line: number of stations (NS)
Third line: number of product models (NM)
Fourth line: number of demand scenarios (ND)
Fifth line: cycle time (CT)
Sixth line: station length multiplier (LM)

First table: the demand scenarios; each column represents a demand scenario; each raw represents a product model.

Next vector (Prob.): the probabilities of each of the demand scenarios.

Second table: the processing time of each model; each column represents a model; each raw represents a task.

Last element (Precedence): the precedence relations given by antecessor index and successor index separed by commas. One relation es expressed per line.

End of file: -1,-1
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Commentary to instance generation:

Each instance contains 10 different product models. The processing time data for each model is extracted from one instance of the dataset of Otto et al. (2013).
For the medium instances, Otto's dataset provided 10 replicas from each instances. These replicas are used to generate the processing times. Excel file "ListOfInstances.xsls" translates the index of the Otto's dataset to the proposed dataset.
Only the instances with the time distribution "Mixed" are used, so that the models presents significantly differences between each other.

5 instances were generated for every combination of ordering strength (OS), average processing load (PL), and station length multiplier (LM). Some instances were proved infeasible. New instances based on other processing time vector were used to substitute the infeasible instances. The combination OS = 0.9 and LM = 1.2 (for both PL = 0.9 and  PL = 0.95), however, produced only infeasible instances for all tested combinations. These 10 instances are then left out of the dataset, resulting in 80 instances.