Warehouse design and product assignment and allocation: A mathematical programming model

Warehouses can be considered one of the most important nodes in supply chains. The dynamic nature of today's markets compels organizations to an incessant reassessment in an effort to respond to continuous challenges. Therefore warehouses must be continually re-evaluated to ensure that they are consistent with both market's demands and management's strategies. In this paper we discuss a mathematical programming model aiming to support product assignment and allocation to the functional areas as well as the size of each area. In particular a large mixed-integer programming model (MILP) is presented to capture the tradeoffs among the different warehouse costs in order to achieve global optimal design satisfying throughput requirements

Association rule based approach for improving operation efficiency in a randomized warehouse

Author name used in this publication: King Wah PangRefereed conference paper2010-2011 > Academic research: refereed > Refereed conference paperOther VersionPublishe

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions

Correlated storage assignment approach in warehouses: A systematic literature review

Purpose: Correlation-based storage assignment approach has been intensively explored during the last three decades to improve the order picking efficiency. The purpose of this study is to present a comprehensive assessment of the literature about the state-of-the-art techniques used to solve correlated storage location assignment problems (CSLAP). Design/methodology/approach: A systematic literature review has been carried out based on content analysis to identify, select, analyze, and critically summarize all the studies available on CSLAP. This study begins with the selection of relevant keywords, and narrowing down the selected papers based on various criteria. Findings: Most correlated storage assignment problems are expressed as NP-hard integer programming models. The studies have revealed that CSLAP is evaluated with many approaches. The solution methods can be mainly categorized into heuristic approach, meta-heuristic approach, and data mining approach. With the advancement of computing power, researchers have taken up the challenge of solving more complex storage assignment problems. Furthermore, applications of the models developed are being tested on actual industry data to comprehend the efficiency of the models. Practical implications: The content of this article can be used as a guide to help practitioners and researchers to become adequately knowledgeable on CSLAP for their future work. Originality/value: Since there has been no recent state-of-the-art evaluation of CSLAP, this paper fills that need by systematizing and unifying recent work and identifying future research scopes

Designing a hybrid model for clustering warehouse items and allocating them to storage locations

Management and warehousing operations are essential parts of manufacturing and service organizations. Warehousing is a significant component of an organization's activities, which incurs high costs and deserves more attention from researchers in this field. The aim of this research is to investigate the storage problem based on item clustering while considering all factors that affect the storage of bulky and varied products in a warehouse. The main objective of this research is to reduce transportation costs for collecting and delivering orders and to achieve more efficient use of storage space. The K-means technique is employed to solve the clustering problem, and the Generalized Allocation mathematical programming model is used to address the assignment of item categories to storage locations. This model is an integer programming model that aims to minimize transportation costs for collecting and delivering orders. This research provides a comprehensive approach to clustering and item allocation by identifying and considering effective indexes and utilizing the generalized allocation mathematical planning model to formulate and solve the problem optimally. Company managers can utilize this model to reduce their inventory costs. The innovation of this research lies in the use of clustering for the allocation of storage sites to warehouse items, followed by mathematical modeling. The proposed model was implemented at Mashhad Housebuilding Company, and several simulated problems were solved using GAMS software for validation.IntroductionThe issue of storage and warehousing is one of the main axes of industries and companies. If the warehouse is properly managed, the efficiency and productivity of the organization can be increased optimally. Warehousing is one of the main costly components in the organization's activities and deserves more attention from researchers in this field. Therefore, the main goal of this research is to reduce transportation costs during the collection and delivery of orders and more effective use of warehouse space. For this purpose, the warehouse of the Mashhad house-building factory was studied. The warehouse of the Mashhad house building factory incurs a lot of costs to collect the orders, which is the result of the improper arrangement of the warehouse. Therefore, in the current research, to achieve a suitable deployment plan and reduce storage costs, the objectives are: 1) to identify the influential criteria in the clustering of items and the model for assigning clusters to their storage location according to the studied warehouse and clustering of warehouse items, 2) to provide a model for improvement The arrangement of the group of items is followed by considering the identified criteria, parameters, and limitations.Materials and MethodsIn the present research, first, according to ABC analysis, the items are divided into three groups (A, B, and C) based on their importance in terms of storage volume and circulation. Group A items are selected for clustering, while for groups B and C, a virtual cluster is considered in the allocation problem. Influential indicators for item clustering in the warehouse were determined through content analysis. The relationship of these indicators with the problem model and their importance were identified using a questionnaire. Cummins cluster analysis was employed for item clustering. Subsequently, a generalized allocation mathematical programming model was utilized to allocate groups of items to their storage locations. This model considered limitations such as warehouse access space, interdependence between groups, demand volume, physical dimensions of items and storage locations, and crane movement during order delivery. The objective of the model was to minimize transportation costs during order collection and delivery. The problem addressed in this research is commonly known as the Warehouse Location Allocation Problem (SLAP).Discussion and ResultsIn this research, 20 clusters were obtained based on 15 indicators, resulting in a total of 154 goods items. ANOVA analysis was conducted on the obtained clusters to examine the impact of each factor on warehouse arrangement. The F statistic value indicated a significant difference among all clusters and indicators The cluster analysis results revealed that the first cluster comprised various types of footings, with the "level of activity" index scoring higher than other indices. This cluster ranked second in terms of this index. The second cluster consisted of roof products, with a higher score in the "need for quick access" index compared to other indicators. Additionally, it obtained the highest scores in the indicators of quick access requirement, demand level, consumption similarity, product activity level, and average item references in the second step, the allocation problem was formulated with two dimensions: the item cluster and its storage location. The first dimension encompassed group A items, including five clusters of frequently used items and 20 clusters resulting from the clustering process, as well as groups B and C inventory items, represented by one cluster consisting of all items from these groups and virtual items. The second dimension consisted of storage locations, with the entire storage space divided into equal areas and a total of 360 storage locations considered in the model to validate the model, the problem was solved in smaller dimensions, and the warehouse manager manually arranged the clusters in the same dimensions. The objective function value was calculated in this case and compared to the value obtained from the mathematical allocation model. The results demonstrated that the researcher's mathematical model achieved over 70% improvement compared to manual arrangement. It should be noted that the actual warehouse conditions were less efficient than the manual arrangement provided by the warehouse manager, as the items had already been clustered, and the warehouse supervisor arranged the problem manually using the data from the warehouse clustering.ConclusionsThe grouping of goods, based on the results obtained from cluster analysis, ensures that items are placed together according to important criteria such as demand, access requirements, and employee safety, among others. This arrangement creates clusters of related products, forming families of goods. This approach minimizes the search time for requested products and enables timely order fulfillment in this research, the area of each cluster was determined by considering the maximum inventory of each product. Additionally, a confidence factor was applied to account for cluster area, allowing for sufficient space in case of additional inventory. This approach ensures efficient search and timely delivery of requested products. It is recommended that if a new product is introduced to the factory's product lineup, managers should conduct cluster analysis to determine its appropriate group. If the area of that category exceeds the area considered in the present research, the allocation issue should be revisited to accommodate the new addition

Rational Searching Procedure in Warehouse Design

Designing a warehouse layout is an important problem, which plays a role in a life cycle of a company. Generally, two main types of layout decision problems are mentioned in literature. The first problem is usually called facility layout problem while the second one is internal layout designing problem or aisle configuration problem. Merging them both, the procedure for rational searching of optimal warehouse layout is prepared. It was decided to prepare the procedure as an optimisation procedure of functional and spatial areas. The procedure is a part of the logistics facilities designing method because a sketch layout of logistics facility is an important part of the method of logistic facilities designing. The paper briefly describes the location of the procedure in the method. Regarding functional and spatial layouts designing, model was expanded with mathematical formulas of certain geometrical parameters.Particular emphasis on warehouse geometry is important due to the fact that many other issues depend on these parameters in warehouse designing process. The paper consists of a problem definition, mathematical model formulation and precise descriptions adequate to mathematic notations. What is more, the example of procedure application implemented into the software is given. The software is the implementation of the procedure and the whole method itsel

The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% c

Advanced Storage and Retrieval Policies in Automated Warehouses

Warehouses are key components in supply chain. They facilitate the product flow from production to distribution. The performance of supply chains relies on the performance of warehouses and distribution centers. Being able to realize short order delivery lead times, in retail and ecommerce particularly, is important for warehouses. Efficient and responsive storage and retrieval operations can help in realizing a short order delivery lead time. Additionally, space scarcity has brought some companies to use high-density storage systems that increase space usage in the warehouse. In such storage systems, most of the available space is used for storing products, as little space is needed for transporting loads. However, the throughput capacity of high-density storage systems is typically low. New robotic and automated technologies help warehouses to increase their throughput and responsiveness. Warehouses adapting such technologies require customized storage and retrieval policies fit for automated operations. This thesis studies storage and retrieval policies in warehouses using several common and emerging automated technologies