Small parts high volume order picking systems

This research investigates analytical models that might serve to support decisions in the early stages of designing high volume small parts order picking systems. Because the development of analytical closed-forms is challenging, a common approach is to use simulation models for detailed design performance assessment. However, simulation is not suitable for early stage design purposes; because simulation models are time-consuming (thus expensive) to construct and execute, especially when the number of alternatives to evaluate is large. If available, analytical models are computationally cheaper. They provide faster and more flexible solutions and though usually less detailed, may be adequate to support early stages of design. The challenge is to develop generic analytic models providing useful results for a class of problems.;This research focuses on a class of problems in high volume small parts order picking systems in which customer orders are progressively consolidated (or assembled) from pick buffers into order containers passing from one picking zone to another via conveyor, so that no sortation equipment is required downstream prior to packing and shipping and picking can precede order assembly. This is a new technology, and not yet in widespread use. The novelty in the modeling approach is the distinct separation of item-picking and order assembly operations which permits the development of performance models for both throughput and service level.;Essentially the system is modeled as a tandem queue, and the two detailed models for the picking and assembly subsystems are developed based on detailed description of the operations. Solving the model provides estimates for performance measures, such as order cycle time and system throughput, which are essential in design. The approximation method requires estimating the squared coefficient of interdeparture times from the classical GX/G/I queuing model, and a suitable approximation is derived in this thesis. Computational tests show the model to provide reasonably accurate estimates of system performance, with minimal computational overhead.;To support the proposed queuing model, new models are developed for estimating mean and squared coefficient of variation for pick and assembly operation times. These models include the variability of order contents and the picking process, along with the physical layout. Results of the estimation compare very well with that of simulation.