The Effect of Interaction Between the Production System (Ps) and a Looped Conveyor-based Material Handling System (Lcmhs) in a Manufacturing Facility

In this paper, we provide empirical evidence that shows the effect of the interaction between the production system (PS) and a looped conveyor-based material handling system (LCMHS) in a manufacturing facility. A rudimentary simulation model captures the interaction between the two systems. Varying several key factors, we test for a statistically significant difference in the work in process (WIP) of the production system with and without the LCMHS to find if the squared coefficient of variation (SCV) of the interarrival time distribution to the PS is affected. The results suggest the need to model the interaction between the two systems in order to obtain a more representative estimate of the WIP in a manufacturing facility

Modeling the Inventory Requirement and Throughput Performance of Picking Machine Order-fulfillment Technology

Picking machines, also known as remote-order-picking systems, are an example of a stock-to-picker piece-level order-fulfillment technology that consists of two or more pick stations and a common storage area. An integrated closed-loop conveyor decouples the pick stations from the storage area by transporting the needed totes to and from the storage area and the pick stations. We develop a probabilistic model capable of quantifying the inventory differences between order-fulfillment technologies that pool inventory with technologies that do not pool inventory. To determine the throughput of a picking machine, we develop a methodology that incorporates existing analytical models for the picking machine’s subsystems. We present a case study comparing a picking machine to a carousel-pod system to illustrate how a manager could use our methodology to answer system design questions. Finally, we present conclusions and future research

Modeling Conveyor Merges in Zone Picking Systems

In many order picking and sorting systems conveyors are used to transport products through the system and to merge multiple flows of products into one single flow. In practice, conveyor merges are potential points of congestion, and consequently can lead to a reduced throughput. In this paper, we study merges in a zone picking system. The performance of a zone picking system is, for a large part, determined by the performance of the merge locations. We model the system as a closed queueing network that describes the conveyor, the pick zones, and the merge locations. The resulting model does not have a product-form stationary queue-length distribution. This makes exact analysis practically infeasible. Therefore, we approximate the behavior of the model using the aggregation technique, where the resulting subnetworks are solved using matrix-geometric methods. We show that the approximation model allows us to determine very accurate estimates of the throughput when compared with simulation. Furthermore, our model is in particular well suited to evaluate many design alternatives, in terms of number of zones, zone buffer lengths, and maximum number of totes in the systems. It also can be used to determine the maximum throughput capability of the system and, if needed, modify the system in order to meet target performance levels

Modeling and performance analysis of sequential zone picking systems

This paper develops an analytical model of sequential zone picking systems. The systems belong to the most popular internal transport and order picking systems in practice, due to their scalability, flexibility, high-throughput ability, and fit-for-use for a wide range of products and order profiles. The major disadvantage of such systems, though, is congestion and blocking under heavy use, leading to long order lead times. In order to diminish blocking and congestion most systems make use of a dynamic block-and-recirculate protocol. The various elements of the system, like conveyor lanes and the pick zones, are modeled as a network of queues with multiple order classes and with capacity constraints on subnetworks, including the dynamic block-and-recirculate protocol. Due to this protocol, however, the stationary distribution of the queueing network is highly intractable. Therefore, an innovative approximation method, using jump-over blocking is proposed to accurately assess key performance statistics such as throughput and recirculation. Multi-class jump-over networks admit a product-form stationary distribution, and can be efficiently evaluated by Mean Value Analysis (MVA) and use of Norton's theorem. The method is most suitable to support rapid and optimal design of complex zone picking systems, in terms of number of segments, number and length of zones, buffer capacities, and storage allocation of products to zones, in order to meet prespecified performance targets. Comparison of the approximation results to simulation show that for a wide range of parameters the mean relative error in the system throughput is typically less than 1%. The approximation is also applied to evaluate a real-life zone picking system of a large wholesaler supplying non-food items

Design Of The Layout Of A Manufacturing Facility With A Closed Loop Conveyor With Shortcuts Using Queueing Theory And Genetic Algorithms

With the ongoing technology battles and price wars in today\u27s competitive economy, every company is looking for an advantage over its peers. A particular choice of facility layout can have a significant impact on the ability of a company to maintain lower operational expenses under uncertain economic conditions. It is known that systems with less congestion have lower operational costs. Traditionally, manufacturing facility layout problem methods aim at minimizing the total distance traveled, the material handling cost, or the time in the system (based on distance traveled at a specific speed). The proposed methodology solves the looped layout design problem for a looped layout manufacturing facility with a looped conveyor material handling system with shortcuts using a system performance metric, i.e. the work in process (WIP) on the conveyor and at the input stations to the conveyor, as a factor in the minimizing function for the facility layout optimization problem which is solved heuristically using a permutation genetic algorithm. The proposed methodology also presents the case for determining the shortcut locations across the conveyor simultaneously (while determining the layout of the stations around the loop) versus the traditional method which determines the shortcuts sequentially (after the layout of the stations has been determined). The proposed methodology also presents an analytical estimate for the work in process at the input stations to the closed looped conveyor. It is contended that the proposed methodology (using the WIP as a factor in the minimizing function for the facility layout while simultaneously solving for the shortcuts) will yield a facility layout which is less congested than a facility layout generated by the traditional methods (using the total distance traveled as a factor of the minimizing function for the facility layout while sequentially solving for the shortcuts). The proposed methodology is tested on a virtual 300mm Semiconductor Wafer Fabrication Facility with a looped conveyor material handling system with shortcuts. The results show that the facility layouts generated by the proposed methodology have significantly less congestion than facility layouts generated by traditional methods. The validation of the developed analytical estimate of the work in process at the input stations reveals that the proposed methodology works extremely well for systems with Markovian Arrival Processes