Dynamic warehouse optimization using predictive analytics.

The forward area is a small area of a warehouse with a low picking cost. Two approaches that are investigated for selecting the SKUs of this area and the allocated space are the static and the dynamic approaches. In the case that decisions about the forward area are made periodically (e.g. yearly) and the products\u27 demand patterns are completely ignored, the FRP is static. We developed two heuristics that solve the large discrete assignment, allocation, and sizing problem simultaneously. Replenishing the same product in the same place of the forward area brings about a ``Locked layout of the fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick operations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity arises. The primary objective of this dissertation is to formally define the dynamic FRP. One main mission of this research is to define a generic dynamic slotting problem while also demonstrating the strengths of this approach over the static model. Dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics. Applying different order data with different demand volatility, we show that the dynamic model always outperforms the static model. The benefits attained from the dynamic model over the static model are greater for more volatile warehouses

Progress in Material Handling Research: 2012

Table of Content

Determining a heuristic for pick location design in an end user warehouse

"May 2014."Thesis advisors: Dr. James Noble and Dr. Cerry Klein.Picking is the number one cost center for most warehouses, representing up to 65% of their total expenditures. Travel time is the largest component of the picking cost which makes travel distance an extremely important variable. The critical issue is to reduce a warehouses picking cost by reducing the overall distance traveled by pickers. While there have been many attempts to reduce travel distance by improving product assignment, pick routing and warehouse design, this research addresses pick location design. Existing methods to pick location design are very basic as the issue is barely addressed. Most research either assumes location sizes are not a constraint, or that a single location size will be sufficient. This research shows that an intelligent approach to pick location design can significantly increase a warehouses space utilization and decrease the distance traveled in its picking operation. The method developed utilizes product dimensions and volumes as well as system attributes and constraints. By using the method developed in this research, the cost incurred by traveled distance can be reduced by up to 48%. Finally, the method is shown to be successful in real world application through a comprehensive case study performed in an actual distribution warehouse.Includes bibliographical references (pages 81-83)

Autonomy In Mobile Fulfillment System: Goods-To-Man Picking System

Nowadays, issues regarding to e-commerce unpredictability become a problem in warehouse operations. This unpredictability is make difficult by fulfillment challenges. Designing a goods-to-man picking system and dispatching order strategy based on service in random order (SIRO) can be one of promising alternative to reduce AGV empty travel distance. The focus is on the warehouse operations, start from item classification on dynamic slots location, multi-attribute AGV dispatching rules and AGV battery management. The system aims to minimize total cost of AGV by assign the multi-attribute dispatching rules and bidding process to get on time delivery as many orders that can be completed, dealing with minimum battery-charging effects on the system operation. The planning system considers dynamic nature of customer order demand, and the simulation based development is used to model real time dynamic slots storage location and AGVs availability. The computational experiments showed this methodology most likely could reduce total cost by perform more than one AGV in operating system

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

Order picking performance improvement through storage location assignment: the case of a hardware wholesaler

Order picking is the most time-consuming, labour-intensive, and costly activity in picker-to-parts picking systems. The literature frequently minimises picking time but ignores the picking error, which affects picking efficiency and customer satisfaction. This paper to minimise picking time and picking error using multi-objective optimisation. Three storage location assignment policies, i.e., ABC-based, product-popularity-based (PPB) and product-relation-based (PRB), are deployed to minimise the picking time. PPB policy gave both the minimum picking time and picking error, with the trade-off between the two objectives presented in a Pareto frontier. Hence, the managers can determine a storage policy based on the optimisation results

A warehouse design framework for order processing and materials handling improvement - Case Etra Oy

Warehouses function as node points in the supply chain linking the material flows between the supplier and the customer As a result of the highly competitive market environment companies are continuously forced to improve their warehousing operations. Many companies have also customized their value proposition to better meet customer demands, which has led to changes in the role of warehouses. In such conditions improvement of order processing and materials handling can bring significant cost savings and at the same time increase customer value. The purpose of this study is to develop a warehouse design framework that supports systematic decision making, and show that this framework can be used to reduce order processing cycle times and improve the overall performance of a warehouse. The empirical part of this thesis was conducted as a case study in a Finnish technical wholesales company. Data for the study was collected from two primary sources. The first source was a review of the company’s order history. The material was profiled to determine ordering patterns of products and to understand order processing needs. The second source of research material came from a participant-observation of warehouse employees. This allowed assessing the overall distribution of time between different warehousing activities and identifying most critical bottlenecks in the warehousing process. The results of this study show that even simple planning methodologies can provide general guidelines for designing warehouse processes. The results also imply that companies with poor information infrastructure are unable to efficiently track operations that are performed within the warehouse. This emphasizes the fact that management of information flows is becoming an increasingly important criterion to successfully plan and allocate resources within the warehouse

The storage location assignment problem: A literature review

The storage of goods plays a crucial role in warehouse management systems. Although this operation is often considered simple, it is in fact complex, due to the volume of products, the uncertainty in demands, and the rapid customer service response that the market requires. Moreover, the optimization of logistical resources to maintain an efficient operational flow, such as the allocation of storage spaces, often requires complex decisions. In this paper, we analyze academic contributions on the storage location assignment problem published between 2005 and 2017. The literature is classified according to the solution methods, objectives, and related considerations. Suggestions are also provided for future research

Integrated Models and Tools for Design and Management of Global Supply Chain

In modern and global supply chain, the increasing trend toward product variety, level of service, short delivery delay and response time to consumers, highlight the importance to set and configure smooth and efficient logistic processes and operations. In order to comply such purposes the supply chain management (SCM) theory entails a wide set of models, algorithms, procedure, tools and best practices for the design, the management and control of articulated supply chain networks and logistics nodes. The purpose of this Ph.D. dissertation is going in detail on the principle aspects and concerns of supply chain network and warehousing systems, by proposing and illustrating useful methods, procedures and support-decision tools for the design and management of real instance applications, such those currently face by enterprises. In particular, after a comprehensive literature review of the principal warehousing issues and entities, the manuscript focuses on design top-down procedure for both less-than-unit-load OPS and unit-load storage systems. For both, decision-support software platforms are illustrated as useful tools to address the optimization of the warehousing performances and efficiency metrics. The development of such interfaces enables to test the effectiveness of the proposed hierarchical top-down procedure with huge real case studies, taken by industry applications. Whether the large part of the manuscript deals with micro concerns of warehousing nodes, also macro issues and aspects related to the planning, design, and management of the whole supply chain are enquired and discussed. The integration of macro criticalities, such as the design of the supply chain infrastructure and the placement of the logistic nodes, with micro concerns, such the design of warehousing nodes and the management of material handling, is addressed through the definition of integrated models and procedures, involving the overall supply chain and the whole product life cycle. A new integrated perspective should be applied in study and planning of global supply chains. Each aspect of the reality influences the others. Each product consumed by a customer tells a story, made by activities, transformations, handling, processes, traveling around the world. Each step of this story accounts costs, time, resources exploitation, labor, waste, pollution. The economical and environmental sustainability of the modern global supply chain is the challenge to face

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development