Van den Berg and Zijim (1999), discusses in their article, the various warehouse systems and they further present a classification of warehouse management problems. The authors further developed a typology and a review of the different warehouse systems. They distinguished three types of warehouses, which are distribution, production, and contract warehouses. According to the level of automation, van den Berg identified three additional warehouse systems, and they include manual, automated, and automatic warehousing systems (1999). The article further highlights the various warehousing models that can be utilized for operations. One model is the classical inventory management and production planning. The model determines ordering and production policies for a single product. It aims at reduction of inventory levels. Another model is the Forward-Reserve- Problem (FRP). The approach decides which product should be stored in the forward area and the one to be picked in the reserve area. The storage location assignment problem (SLAP) is the other model, which caters for the assignment of incoming stock to the storage locations. The article illustrates that the selection of a higher warehouse service level and shorter response times may result in savings downstream the logistic chain.
Schuff, Corral, Louis, and Schymik (2016), elaborate the importance of a self-service Business Intelligence (BI) as a model management for warehouses. The authors outline a methodology that provides modelers with the crucial information to execute sample, explore, modify, and assess process (SEMMA). The information provided serves as a necessary condition for self-service BI. For this case, the authors developed a prototype for the United States Department of Labor, which they used to test the hypothesis and solve the problem that was facing the department. From the prototype, the authors found that dimensional model marts are the only available technique to support self-service BI. They also found that the dimensional model marts enable users with minimal knowledge regarding databases to receive the information they require (Schuff, Corral, Louis, & Schymik, 2016).
Another model that has received appreciation mainly from the central government of India is the public private partnership (PPP) model for warehouse management (Maritime Gateway, 2013). According to the ministry of food, the model is significant to the economy. The department suggested states to follow the approach since it brought an increase in both revenue and storage capacity in Rajasthan. The government stated that outsourcing had increased the storage capacity of the warehouse, and out of 90 storerooms, 38 were given to private companies while 52 remained with the government.
Das, Roy, and Kar (2015) wrote an article where they developed a multi-warehouse partial backlogging inventory model for worsening items under inflation when a delay in payment is bound to happen. The authors argue that in a majority of the stock-out inventory systems, the demand for the product is back-ordered, and customers have to wait for long periods until they receive their desired product. Therefore, the longer the waiting time, the smaller the backlogging rate. The authors attempt to formulate a mathematical model for deteriorating items with price and stock-dependent demand (Das, Roy, & Kar, 2015). The authors developed and solved the model via contractive mapping generic algorithm (CMGA) and particle swarm optimization (PSO). A sensitivity analysis was also conducted to study the impact of the decision variable on the changes of different parameters. The authors provided the recommendation that for further research one can utilize the model to include the case of the finite rate of replenishment and rate of the horizon (Das, Roy, & Kar, 2015).
Roy, Krishnamurthy, Heragu, & Malmborg (2015) present various stochastic models that can be used for multi-tier warehouse systems that handle unit load transactions utilizing autonomous vehicle-based technology. The authors argue that warehouses seek to adopt automated storage and retrieval technologies that would enhance the handling of large volumes of stock and serve customer transactions efficiently. They state that the autonomous vehicle-based storage and retrieval system is an attractive technology for unit load activities for warehouses with high-density storage capacity. By evaluating literature review from other studies, Roy, Krishnamurthy, Heragu, & Malmborg (2015) noted that the autonomous vehicles that utilize lift-based vertical transfer mechanisms had a waiting time of 16 to 44 percent of the transaction cycle times. To avoid this limitation, the authors proposed alternative AVS/RS settings that utilize conveyers for vertical transfer. The findings of the research state that the semi-open queuing network model functions appropriately by capturing the routing of vehicles in the network. The model captures the delays that arise due to blocking at the cross aisle and aisle nodes (Roy, Krishnamurthy, Heragu, & Malmborg, 2015). The decomposition approach solved the multi-tier model where with detailed simulations and validation experiments the errors were low. The authors believe that the models they designed captured the stochastic interactions in the multi-tier systems.
Chung, Her, and Lin (2009) argue that production-inventory systems have one weakness of assuming that all items produced by a warehouse are of good quality. They state that the production of flawed commodities is a normal phenomenon in a production process. Therefore, they developed an inventory model with two warehouses with imperfect quality production processes. They assumed that the management owns a storeroom that accommodates a fixed capacity of items and the rest of the units are stored in a rented warehouse. The transportation cost between the two stores is also included. The authors found that the total profit per unit time function is concave.
Agrawal, Banerjee, and Papachristos (2013) establish an inventory model with deteriorating items, ramp-type demand and partially backlogged shortages for a two-warehouse system. They argue that the control and regulation of deteriorating items like vegetables is a significant problem for inventory systems dealing with such products. The lack of storage space and proper storage equipment for storing the decaying commodities is also a major challenge. In their concluding remarks, they stated that an inventory control of products with less storage time is crucial for businesses. Therefore, a retailer with less storage space is supposed to rent another warehouse to accommodate the rest of the items. The model helped the researchers to determine the demand parameters, holding cost involved at the rented warehouse, shortage cost, cycle, length, and transport cost.
Bhunia, Jaggi, Sharma, and Sharma (2014), established a two-warehouse inventory model for decaying items under permissible delay in payment with partial backlogging. The model was for the single deteriorating product with two separate warehouses that contain different preserving equipment. In the article, the authors assumed demand to be known and constant. They also allowed and partially backlogged shortages with a rate dependent on the duration of waiting time up to the arrival of the next load. The development of a two-warehouse deteriorating inventory model enabled the researchers to formulate a new trade credit policy. They assumed that the preservation in the rented warehouse is better than the owned storeroom and that stocks in the rented warehouse are transferred with a continuous pattern (Bhunia, Jaggi, Sharma, & Sharma, 2014). The authors argue that the introduction of open market policy makes it possible for the model to be applicable in various practical situations.
Ouyang, Wu, and Yang (2006) conducted a study on an inventory model for non-instantaneous deteriorating items, with permissible delay in payments. They sought to determine an optimal replenishment policy to minimize the total relevant inventory cost. The authors developed useful theorems to characterize the optimal solutions and to provide an easy approach to identify the optimal replenishment cycle time and order quantity (Ouyang, Wu, & Yang, 2006). The research finding concluded that cost is an influential component when determining the optimal replenishment policy in the study. The authors also found out that the retailer can reduce annual inventory cost by ordering low quantity when the supplier dispenses a permissible delay in payments (Ouyang, Wu, & Yang, 2006). Equally important, an article by Worldwide Computer Products News (2010) illustrates how High Jump offers warehouse management system in cloud delivery model. The High Jump Company stated that the warehouse management system (WMS) would consist of the same functionalities as the on-premise High Jump WMS. The cloud delivery model eliminated upfront capital expenses and reduced the risks and time required by on-premise implementations.
Ramabu and Schade (2010) article discussed the importance of stochastic guaranteed service model with Multi-Echelon warehouse management. The research focused on the inventory control of a spare part distribution system. The authors argued that the guaranteed service model (GSM) could only manage bounded demands and deterministic delivery times in the network (Ramabu & Schade, 2010). Missed internal delivery times produced situations that are not identified by the model; thus, made it difficult for the GSM to account for corresponding costs. The authors introduced the new stochastic guaranteed service model with recourse (SGSM) and applied it to the inventory control problem of a multi-echelon warehouse. Simulated results showed that policies based on SGSM dominate GSM policies with regarding inventory cost and recourse cost. The findings indicated that the SGSM was capable of consolidating the volatilities in the model and accounting for the corresponding cost (Ramabu & Schade, 2010). The author concluded that SGSM could be utilized in a spare part distribution system due to its significance over the GSM.
Christine Connollys article aims to discover the different technologies used in warehouse stock control. The author starts by discussing the various labeling techniques that are available such as smart camera and off- the shelf image processing tools that provide optical character recognition and reading of the text (Connolly, 2008). The article also discusses the various formats of the barcode, and they include Universal Product Code (UPC) Pharmacode, Code 49, and the European Numbering Code (ENC). Connolly argues that an RFID reader is capable of reading all nearby tags at the same time and can read other details in the packaging material. Moreover, the Wrexham-based company group Ltd supplies hardware and software to be used in warehouses. The paper further continues to highlight various examples of devices and systems for reading different product labels that are helpful in integrating stock control into back-office databases. Connolly also discusses the various techniques for locating goods within the warehouse. Findings from the article indicate that the latest developmental technologies in labeling, supplement automatic product identification. The use of handheld computers with wireless communications ensures efficient resource management through real-time capability and integration of stock control.
The article, Efficient Formation of Storage Classes for Warehouse Storage Location Assignment: A Simulated Annealing Approach by Venkata Reddy Muppani and Gajendra Kumar Adil focuses on the various storage classes that are available in warehouses. Muppani and Gajendra (2008), argue that there are th...
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