| Order picking is considered as one of the most costly and time-consuming processes in a manual picker-to-order system. The process is as follows: pickers receive customer orders and then start collecting the order items by picking these items from their storage locations. Finding the most efficient route to visit all items locations is the main goal of optimizing order picking routing path. Because each warehouse runs its operations with multiple pickers and the pickers may work in the same zone to pick items on customer orders, congestion may occur during the picking time. This can affect the order picking finishing time. The optimization solution obtained without considering the order picking congestion might not provide the most efficient route.;Among all warehouse operations, order picking accounts for almost 55%-65% of the total cost. Around 50%-55% of the order picking time is for traveling between picking locations. Most of the studies focus on the single picker-to-order systems, where the effect of the congestion does not exist. However, in reality, multiple picker systems were used and thus congestion does have a significant impact on order picking. This represents an important gap in the literature, where there is no mathematical formulation for the congestion in the literature.;This research focuses on the congested order-picking problem, and solve for the best picking route while considering the effect of different factors inside the warehouse. Literature is investigated to survey the studies that considered the congestion effect in multiple picker systems and to review the models used to represent this problem.;Three models (represents order picking without congestion consideration, with picking face congestion, and with picking and aisle congestion) were defined. In addition, three levels of congestion were defined. The Model I was solved using Tabu Search, Genetic Algorithm, and Hybrid Algorithm. Algorithms validated by comparing with CPLEX solution. Then, the most efficient algorithm was used to solve both Model II and Model III. A sensitivity analysis was done to explain the effect of different combinations (order pickers number, the number of items to be picked, and the width of the aisle) on the level of congestion. |
