•Investigates the layout planning and item assignment in U-shaped storage areas.•Considers the objectives of minimizing walking distances and ergonomic strains during order picking.•Develops a ...mixed-integer program formulation for the problem.•Proposes and tests a polynomial-runtime solution procedure.•Derives central problem characteristics and practical recommendations.
Order picking is considered one of the most labor- and cost-intensive warehouse operating processes. Regularly, order pickers are exposed to severe physical demands, which increase the likelihood for developing muscular-skeletal disorders, such as lower back pain. Muscular-skeletal disorders cause significant compensation, recovery and deficiency costs and are expected to gain further importance in the future due to an aging workforce. Both companies and workers may therefore benefit from decision support models that explicitly take ergonomics aspects into account. The work at hand investigates a warehouses where the storage area is divided into zones with shelves in each zone arranged in the shape of a U. For this warehouse, we determine an optimal configuration of the U-zone’s layout as well as an optimal assignment of products to storage locations. We depart from prior research by considering both the minimization of the total travel distance as well as the minimization of the total ergonomic strain workers are exposed to. Both optimization problems are formalized as mixed-integer programs. An exact polynomial-runtime solution procedure, suitable for both objectives, is developed. Using this solution procedure, we illustrate how the relevant ergonomic strains can be quantified to apply them to our optimization model. Computational studies illustrate the efficacy of our proposed solution procedure. Optimal layouts and storage assignments significantly reduce the walking distance and ergonomic strain during order picking. Additionally, both objectives are only marginally conflicting, such that, mutually, an optimal solution for one objective is also a close-to-optimal solution for the other. We finally derive insights on the optimal layout and storage assignment for future research and practical application.
Very large databases like data warehouse slow down over time. This is usually due to a large daily increase in the data in the individual tables, counted in millions of records per day. How do we ...make sure our queries do not slow down over time? Table partitioning comes in handy, and, when used correctly, can ensure the smooth operation of very large databases with billions of records, even after several years.
Purpose: Slotting is one of the main operations in warehouse management. It is based on the efficient allocation of slots for stock-keeping units (SKUs). Order picking and slotting represent a high ...percentage of total logistics costs; therefore, improving these activities leads to significant savings in the overall performance. This paper aims to develop an allocation model integrating SKUs physical variables, warehousing design, and operation (dimensions, layout, material handling equipment), and heterogeneous product demand.Design/methodology/approach: The modeling methodology considers two phases. First, an integer linear programming model for warehousing spaces assignment for SKUs considering priority and required orders is developed. Then, the total operation times using different strategies are calculated.Findings: The effectiveness of the model was verified through simulation using historical data. The results showed that the best performance in the total time of the slotting operation is achieved by using the ABC as a criterion for the classification of the SKUs and by sequentially assigning the row, level, column, and the section.Practical implications: This approach can be adapted to different industrial sectors and serves as a basis for more robust models regarding the number of constraints or the incorporation of additional warehouse operating parameters.Originality/value: The most important contribution of this work is the development of a flexible and adaptable methodology to changes in the operation to improve the efficiency of storage management through slotting. Future work includes other objective functions of sustainable operations and uncertainty treatment techniques.
In multi-automated guided vehicle (AGV) control, optimization and collision avoidance are two of the key issues. To deal with these problems of the AGV fleet, motion planning is a good solution. This ...method usually comprises two steps as follows: routing and scheduling that are always separately executed in conventional routine. This scheme still exists some drawbacks, such as limitation of candidate paths or lack of flexibility in handling collisions. Besides, with a specific layout, the algorithm needs to be modified to be proper with that application. The warehouse with grid-based layout employed popularly in logistics and supply chain is our concern. To overcome this theme, a time-frame-based routing and scheduling (TFRS) algorithm for motion planning of vehicles is proposed for this warehouse application. In detail, TFRS can also be called an enhanced Dijkstra's algorithm (EDA) with adaptive weights for every segment and node. It was designed to gain several benefits of time due to the shortest path, free collision, and proper for chessboard layout. The main idea is that while conducting path routing, certain circumstances of potential accidents are detected and dealt by scheduling in every loop. Due to simultaneous policies of routing and scheduling, the optimization and secure operation could be achieved in the AGV system. Numerous situations in danger of collision are experimented to verify the effectiveness, flexibility, and correctness of the proposed algorithm.
Choosing an order picking strategy is one of the most important decisions related to warehouse management. Making this decision properly can lead to high standards of efficiency, since order picking ...represents more than a half of a wholesale and retail organization’s operational costs and consumes a huge amount of the resources allocated to warehouse labor. Moreover, some productivity and service-oriented objectives related to order picking are sometimes conflicting, and require managers’ preferences to be considered, thus making the decision problem multi-objective and complex. We put forward a multicriteria decision model based on the ELECTRE III method that supports how to choose an order picking strategy. It takes managers’ preferences into consideration and integrates all the core elements for assessing how picking is being performed. Results showed that the model is able to identify the strategy that yields the best compromise between the objectives of productivity and the service-oriented ones, and that this strategy also represents the organization’s aims.
This paper proposes and experimentally assesses a
rewrite/merge approach for supporting real-time data warehousing via lightweight data integration
. Real-time data warehouses are becoming more and ...more relevant actually, due to emerging research challenges such as
Big Data
and
Cloud Computing
. Our contribution fulfills limitations of actual data warehousing architectures, which are no suitable to perform classical operations (e.g., loading, aggregation, indexing, OLAP query answering, and so forth) under
real-time constraints
. The proposed approach is based on
intelligent manipulation of SQL statements of input queries
, which are decomposed in suitable sub-queries (the rewrite phase) that are finally submitted as (final) input queries to an ad hoc component responsible for the cooperative query answering via a
parallel query processing
inspired method (the merge phase). This method induces in a novel data warehousing framework where
the static phase is separated by the dynamic phase, in order to achieve the real-time processing features
. We complete our analytical contributions by means of an extensive experimental campaign where we stress the performance of our proposed real-time data warehousing framework against a popular data warehouse benchmark, and in comparison with traditional architectures, which finally confirms the benefits deriving from our proposal.
This reprint focuses on applications of machine learning models in a diverse range of fields and problems. It reports substantive results on a wide range of learning methods; discusses the ...conceptualization of problems, data representation, feature engineering, machine learning models; undertakes critical comparisons with existing techniques; and presents an interpretation of the results. The topics within the chapters of the publication fall into six categories: computer vision, teaching and learning, social media, forecasting, basic problems of machine learning, and other topics.
Purpose
– The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from ...the literature and to assess which learning curves are most suitable to describe learning in order picking. Design/methodology/approach
Design/methodology/approach
– An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis. Findings
Findings
– It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar-El et al.
Practical implications
– The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules.
Originality/value
– The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects.
We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input ...the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.
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