Engineering

Walk-and-pick systems

This system is based on one or more pickers walking through the aisles using a pick cart. Each pick starts at a PD station after the picker empties his cart to pick the pick. This system is fairly flexible and requires a minimal capital investment hence mostly used in industries. The picker in this system spends most time walking rather than picking which is tiring and also wastes a lot of time. To reduce the amount of walking, the users of this system take either of the two measures; sequencing the picks to minimize the walking time or the pickers pick more than one order at each time.

The first measure is known as the Travelling Salesman Problem (TSP) while the second is referred to as Multiple Order Picking. During the TSP, many shoppers minimize their walking time by for example starting from one end of the store to the other end instead of entering the aisles in random order. The aim of the second measure is to reduce the walking distance the picker has to walk from one pick to the next in a given trip. To increase the picker’s efficiency there should be an increase in the number of picks per trip. The picker uses a picking cart with similar in both weight and volume and the items picked on a trip must be sorted by order number at the pickers return to the PD station. Some carts can contain separate compartments and this will reduce the sorting process since the items belonging to the same order are placed in the same compartments.

A number of variables such as the shape of the working area and the assignment of certain orders to each trip determine the operations of this system. The time expected to pick an order depends on the shape of the picking area, travel speed of the picker and the policy used in sequencing the picks. Estimating the picking time is straight forward. To estimate the walking time, one assumes the aisles are simple with no cross-aisles, are of equal length and the cross-aisles are only at the front and back of the picking area. The algorithms used to determine the optimum check are fast and not straightforward hence the heuristic policy is used to sequence the picks. Here the picker enters each aisle which has one or more picks and exits from the opposite end. The figures shown in the table contain examples of the heuristic policy.

End-of-aisle order picking

At this operating system, the containers are brought to the end of the aisle and the picker performs the pick and after completing the pick, the container is returned to the storage rack. This OP system analysis is only limited to the AS/RS miniload which is used for small or medium sized items. The results below are for the end-of-aisle as long as the chebyshev metric is used by the device and the storage area is rectangular. The assumptions used in-the-aisle OP systems are the same for end-of-aisle system. Randomized storage is used and a single pick is made from each container. The picker is then dedicated to the aisle. The storage rack is accurate with low accelerating and decelerating losses and if multiple aisles are required, they are similar in activity and the rack shape.