Abstract
A model is developed to plan material flow congestion avoidance on a network of conveyors for handling discrete batches of items, e.g. scanned packages. Minimizing maximum material flow congestion is used as a guide to design the conveyor network by evenly distributing the flow of such batches by considering capacities of the conveyors and existing flow. Problems of this type are typically NP-hard which makes them unsolvable by standard mixed integer programming solvers. A method based on results from probability theory is used to solve the formulated problem. Experimental results are presented to demonstrate convergence within 10-100 iterations of the described method for most problems. The increase in CPU time is observed to be approximately linear with respect to the number of constraints and with respect to the number of binary variablesfor a constant number ofiterationsofthe algorithm. A procedure for estimation of bounds is derived to show the proximity of the obtained solution to an optimal solution.
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