Allocation of products in a warehouse is done by various storage policies. These are broadly classified into three main categories: dedicated storage, randomized storage, and class-based storage. In dedicated storage policy a product is assigned a designated slot while in random storage policy incoming product is randomly assigned a storage location close to the input/output point. Finally, the class-based storage is a mixed policy where products are randomly assigned within their fixed class. Dedicated storage policy is most commonly used in practice. While designing large warehouse layout, the product information in terms of throughput and storage level is either uncertain or is not available to the warehouse designer. Hence it is not possible to locate products on the basis of the throughput to storage ratio method used in the above mentioned storage location policies. To take care of this uncertainty in product data we propose a fuzzy C-means clustering (FCM) approach. This research is mainly directed to improve the efficiency (distance or time traveled) by designing a fuzzy logic based warehouse with large number of products. The proposed approach looks for similarity in the product data to form clusters. The obtained clusters can be directly utilized to develop the warehouse layout. Further, it is investigated if the FCM approach can take into account other factors such as product size, similarity and/or characteristics to generate layouts which are not only efficient in terms of reducing distance traveled to store/retrieve products but are effective in terms of retrieval time, space utilization and/or better material control.

<p>In today’s competitive global economy, the focus is on faster delivery of orders at lower total costs. In this paper we are interested in several aspects of order picking systems. We examine the influence of station layout, storage policy, picking policy and sorting solution on order picking system performance. On each of the analysis we consider a few solutions. We determine influence of different number of station in raw on picking system performance. We design the replenishment system supported by kanban philosophy with implemented economical order quantity (EOQ) and reorder point (ROP). The picking system is designed for assumed product’s demand values. The assessment of each of the solution is done through Arena simulation model. The results show that properly designed station with reasonable storage policy and implemented batching policy brings significant raise in order picking system productivity. In addition, we found that proper sortation system logic allows for more equal workload and reduction of maximum queue lengths. The results offer solutions to managers looking to implement improvements in order picking systems.</p>

In today’s competitive global economy, the focus is on faster delivery of orders at lower total costs. In this paper we are interested in several aspects of order picking systems. We examine the influence of station layout, storage policy, picking policy and sorting solution on order picking system performance. On each of the analysis we consider a few solutions. We determine influence of different number of station in raw on picking system performance. We design the replenishment system supported by kanban philosophy with implemented economical order quantity (EOQ) and reorder point (ROP). The picking system is designed for assumed product’s demand values. The assessment of each of the solution is done through Arena simulation model. The results show that properly designed station with reasonable storage policy and implemented batching policy brings significant raise in order picking system productivity. In addition, we found that proper sortation system logic allows for more equal workload and reduction of maximum queue lengths. The results offer solutions to managers looking to implement improvements in order picking systems.