Order Picking Research Articles

The study of joint order batching and picker routing problem with food and nonfood category constraint in online‐to‐offline grocery store

AbstractOrder picking is the process of retrieving products from the storage locations to meet customer orders, which accounts for more than 55% of the total warehouse cost. The joint order batching and picker routing problem (JOBPRP) is an effective way to improve picking efficiency. Although many warehouses face the physical constraints of products that have impact on the picking sequence, such as weight, size, shape, and fragility, JOBPRP with such physical constraints has not been widely studied in the literature. This paper is inspired by a practical case observed in an online‐to‐offline grocery store in China, where food products should not be carried under nonfood products in the picking container to maintain food safety, called category constraint. Therefore, JOBPRP with category constraint is studied. The JOBPRP optimization models with and without category constraint are formulated to minimize the total processing time, and the modified seed algorithms, with new seed addition rules and modified near‐optimal routing methods are proposed to solve the models. The performance of the proposed algorithms is evaluated in different seed addition rules, routing methods, sort time scenarios, and storage assignment strategies (SASs) in a case study. We found that considering category constraint in JOBPRP can reduce the total processing time, and the modified seed algorithms perform better than the traditional first‐come‐first‐serve benchmark algorithms and the seed algorithms with traditional seed addition rules and S‐shape routing method. The SASs where nonfood and food products are separately in fewer number of zones are recommended.

Expected distances and alternative design configurations for automated guided vehicle-based order picking systems

Automated Guided Vehicle (AGV)-based order picking (OP) systems, also known as Robotic Mobile Fulfilment Systems, continues to receive attention in industry and academia since their introduction as Kiva systems. A key component of AGV-based OP systems is the ‘robots’ (or AGVs) that pick up the ‘pods’ and transport them to the appropriate pick station (PS), where a picker picks the items ordered by customers. The performance of such systems depends on the shape of the forward area (FA) and the number of AGVs, which in turn depends on the time it takes an AGV to retrieve a pod. To aid system designers, we explore alternative shapes for the FA and we derive closed-form expressions for the expected AGV travel distances under two possible order assignment rules. Under the random assignment rule, an order is assigned to any PS with equal probability. Under the closest assignment rule, the order is assigned to the closest PS. We also examine the impact of alternative PS configurations for different shapes of the FA. The results offer valuable insights concerning expected travel distances under alternative design configurations. The results would also be useful when building design and performance evaluation models for AGV-based OP systems.

The joint order batching and picker routing problem: Modelled and solved as a clustered vehicle routing problem

The joint order batching and picker routing problem (JOBPRP) is a promising approach to minimize the order picking travel distance in a picker-to-parts warehouse environment. In this paper, we show that the JOBPRP can be modelled as a clustered vehicle routing problem (CluVRP), a variant of the capacitated VRP in which customers are grouped into clusters. To solve this cluster-based model of the JOBPRP, we apply a two-level variable neighborhood search (2level-VNS) metaheuristic, previously developed for the CluVRP, and study which adaptations are required to perform efficiently in a warehouse environment. Additionally, we evaluate if the Hausdorff distance used as an approximation for the clusters’ proximity in the CluVRP, performs equally well when determining closeness between pick orders in a warehouse. We compare the performance of the Hausdorff-based batching criterion to the cumulative minimal aisles visited-criterion, known as a well-performing batching metric in rectangular warehouses with parallel aisles.The 2level-VNS performs well compared to state-of-the-art algorithms specifically developed for the order batching problem (OBP) in a single-block warehouse. A multi-start VNS remains slightly superior to our approach. Concerning the Hausdorff distance, we conclude that in most experiments, the minimum-aisles criterion retains a better fit in the warehouse context.

A novel approach for modeling order picking paths

AbstractWe introduce the visibility graph as an alternative way to estimate the length of a route traveled by order pickers in a warehouse. Heretofore it has been assumed that workers travel along a network of travel paths corresponding to centers of aisles, including along the right angles formed where picking aisles join cross aisles. A visibility graph forms travel paths that correspond to more direct and, we believe, more appropriate “travel by sight.” We compare distance estimations of the visibility graph and the aisle‐centers method analytically for a common traditional warehouse design. We conduct a range of computational experiments for both traditional and fishbone warehouse layouts to assess the impact of this change in distance metric. Distance estimations using aisle‐centers calculates a length of a picking tour on average 10–20% longer compared to distance estimations based on the visibility graph. The visibility graph metric also has implications for warehouse design: when comparing three traditional layouts, the distance model using a visibility graph resulted in choosing a different best layout in 13.3% of the cases.

Routing order pickers in warehouses considering congestion and aisle width

Due to the importance of routing order pickers, there has been extensive research in the area of routing in warehouses. Still, there are some prominent factors that should receive more attention, as they may provide unsatisfactory services and incur considerable operational costs if ignored. In real-world applications, warehouse configuration, width of aisles, and controlling the vehicle congestion in the aisles greatly influence the efficiency of the routing process. Therefore, this paper proposes a mixed-integer programming model. The model aims to minimize maximum delivery time by finding the shortest pickup and delivery routes of all goods for all vehicles. Since the problem is NP-hard, a Simulated Annealing metaheuristic approach is designed to solve the model in large-size problems. This research contributes to picker routing literature by considering dynamic congestion, narrow and wide aisles, and pickup times and proposing a metaheuristic algorithm. The validation and efficiency of our proposed model are proved by solving some various generated benchmark problems. In summary, the developed route planning mathematical model works effectively for any two-dimensional rectangular layout, and the collision prevention constraints are incorporated in the mathematical model.

A rule-based heuristic algorithm for joint order batching and delivery planning of online retailers with multiple order pickers

Today with the rapid improvement of new technologies, people tend to buy various products from online retailers which facilitate the purchasing process and save their valuable limited time. Two important and interconnected operations of each online retailing system are order picking and delivery planning. In an online system, lots of small orders including different products arrive dynamically and must be delivered on time, so there is a limited time to retrieve products from their storage locations, pack them, load onto trucks, and deliver to the destinations. In this study, we deal with these two problems of an online retailer that stores a variety of products in a warehouse and sells them online through their website. A rule-based heuristic algorithm is proposed which integrates decisions of order batching, picking schedule of batches, and assigning orders to trucks as well as, scheduling and routing of trucks. Three different batching methods including two well- known heuristics and a genetic algorithm have been used. An extensive numerical experiment is carried out to show the efficiency of the rule-based algorithm and investigate the results of using each batching method for different problem sizes. It is demonstrated that while the algorithm has efficient performance with three used batching methods, the genetic algorithm can lead to less system cost and more order pickers productivity.

Impact of the Method of Criteria Normalisation on the Order Picking Route and Time

Purpose: The purpose of the article is the selection of the best criteria for the normalisation method in order to achieve the minimal order picking route and time. Design/Methodology/Approach: When a company utilizes warehouse with the shared storage system, every product can be stored in many, sometimes very distant from each other, locations. Locations were selected by the multiple-criteria decision-making technique – TOPSIS. The research was conducted by means of the simulation methods. Every location was described by three criteria (distance from the I/O point, degree of demand satisfaction and the number of other picked products in the proximity of the analyzed location), for which 37 combinations of weights and 18 normalization formulas were applied. For every combination of weights and normalization method 1000 orders were generated. Findings: The best results were obtained when high weight was assigned to the degree of demand satisfaction. It was hard to indicate unequivocally the best normalization method. However, quotient inversions generally yielded slightly worse results than standard scores and feature scaling. Practical Implications: Obtained results indicate that the presented approach can be useful in real warehouse management. It is a quite versatile method that can be adopted to various situations. Originality/value: Although the routing problem in order picking has already been widely discussed, the literature about the problem of selection of locations in shared storage is quite scarce. Therefore, the presented approach can serve as one method of selection of locations in the shared storage system.

Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving the order batching problem becomes more important when the size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. The content of the batch and picking route affect the retrieval-time of the orders. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is to develop fast and effective metaheuristic approaches to solve the order batching problem. For this purpose, two genetic algorithm based metaheuristic approaches are proposed. The numerical test of the proposed algorithms is performed with generated data sets. The proposed methods are thought to be useful to solve real-life problems in different warehouse configurations. Accordingly, a real case study is conducted in the distribution center of a well-known retailer in Turkey. The case study includes the storage assignment process of incoming products. The results demonstrate that developed algorithms are practical and useful in real-life problems.

An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system

Recently, growth in e-commerce has resulted in substantially increased investment in robotic technologies in warehouses, especially for storage and retrieval devices. A shuttle-based storage and retrieval system (SBS/RS) is one of the robotic automated storage and retrieval technologies mostly used for a miniload material handling system in e-commerce warehouses. This new technology has been developed as an alternative to the traditional miniload crane-based automated storage and retrieval system. Because it has an autonomous order picker robot in each tier of the racking system, the process rate for this new technology is very high. To evaluate several design options for an SBS/RS warehouse quickly, we have developed analytical models for estimating important performance outputs of these systems. Specifically, this study presents an open queuing network-based software tool that can quickly estimate some important performance metrics for a predefined SBS/RS warehouse design. Once the user enters the required input data values in the tool (e.g., the arrival rate of transactions in the system, warehouse metrics, number of tiers, aisles, and bays, and velocity profiles of devices), the performance metric estimates can be obtained in terms of the mean cycle time of a transaction, the mean energy consumption and energy regeneration per transaction, the mean waiting time of a transaction, the mean number of transactions waiting in a server (lift/shuttle) queue, and the mean utilization of servers. The software tool developed can be downloaded for free from the Web site http//:www.sbsrscalculator.com.

A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses

Short and reliable delivery lead times are crucial for many buying decisions, making efficiently operated order picking systems a critical contributor to a company’s competitiveness. To pick orders fast and with minimal effort, three planning problems need to be solved, namely the assignment of items to storage locations, the consolidation of orders in batches and the routing of the order pickers through the warehouse. Even though the problems are strongly interdependent, they have so far largely been solved separately, leading to losses in efficiency. Recent research has shown that a joint solution can lead to significant performance improvements as compared to individual solutions. Up to now, no method is available that solves all three problems jointly. This work contributes to closing this research gap by proposing an iterative heuristic method that solves the problems jointly and that takes account of future dynamics in customer demand and their influence on the three planning problems. The performance of the method is illustrated in numerical experiments. The results of our studies indicate that the method may lead to significant savings in travel distance.

A flow picking system for order fulfillment in e-commerce warehouses

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse operation management. In this study, we first develop analytic models to estimate the critical performance indicators of a flow picking system, including picking density and turnover time of an order. Second, we leverage the proposed models and real warehouse data to compare the performance of batch picking and flow picking systems through simulation. Our results show that a flow picking system requires fewer order pickers and shorter walking distances than a batch picking system in most scenarios, especially those with a higher order arrival rate to achieve the same service level. Our study can provide valuable guidelines to warehouse managers and decision-makers for choosing an order fulfillment solution by comparing a batch picking system and a flow picking system.

Robotic mobile fulfilment systems considering customer classes

This paper studies a Robotic Mobile Fulfilment System (RMFS), featured by a number of robots lifting and transporting movables storage shelves from storage grids to order pickers. In such systems, online retailers often classify their customers by two major classes ‘expedited shipping’ and ‘standard shipping’. We build high-dimension Markov models to describe this system with customer classes, calculate the throughput of this system given the number of robots and provide design rules to determine the optimal number of robots and their capacities considering the trade-off between capacities of picker stations and robots. We verify the analytic results of Markov models with simulations. We further consider multiple-picker RMFS and study its optimal design.

Integrated Order Picking and Vehicle Routing Operations – Literature Review and Further Research Opportunities

In modern supply chains, customers expect a high level of service, which results in short delivery times and improved service offerings through retailers. The resulting pressure on supply chains requires a precisely coordinated scheduling of successive processes. Even though a multitude of publications on integrated production and delivery problems already exist, the research field of integrated order picking and vehicle routing is relatively young. Due to its practical relevance and theoretical challenge several studies have been published in recent years that focus on the field of integrated order picking and vehicle routing. This paper provides an overview of existing papers, their main findings, and further research opportunities to integrated order picking and vehicle routing problems.

Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times.

A Monte Carlo simulation to estimate fatigue allowance for female order pickers in high traffic manual picking systems

The paper presents a Monte Carlo simulation model to estimate the average rate of energy expenditure (Kcal/min) and, consequently, fatigue allowance in manual order picking systems with high demand rates. The study is limited to picking a low number of low weight items per order in a compact warehouse system with traditional layout configuration of parallel racks with equal size storage cubicles. Replicates of 10,000 orders with random order sizes and random locations of items per order are used to estimate the average rate of energy expenditure for female pickers with various combinations of body mass and walking speeds. Design for the average and design for percentile are investigated to estimate a maintainable work standard to maximise productivity while maintaining energy expenditure within acceptable limits. Results illustrate the significant impacts of body mass and walking speed on the productivity and rate of energy expenditure, especially for the above average weight picker. Moreover, results reveal the difficulty of adopting recommendations on limitations on energy input and degrade in aerobic capacity with age and health conditions when setting productivity limits for pickers with a wide range of weights.

Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders.

Sensitivity analysis of selected parameters in simulation models of logistics facilities is one of the key aspects in functioning of self-conscious and efficient management. In order to develop simulation models adequate of real logistics facilities’ processes, it is important to input actual data connected to material flows on entry to models, whereas most models assume unified load units as default. To provide such data, pseudorandom number generators (PRNGs) are used. The original generator described in the paper was employed in order to generate picking lists for order picking process (OPP). This ensures building a hypothetical, yet close to reality process in terms of unpredictable customers’ orders. Models with applied PRNGs ensure more detailed and more understandable representation of OPPs in comparison to analytical models. Therefore, the author’s motivation was to present the original model as a tool for enterprises’ managers who might control OPP, devices and means of transport employed therein. The outcomes and implications of the contribution are connected to presentation of selected possibilities in OPP analyses, which might be developed and solved within the model. The presented model has some limitations. One of them is assumption that one mean of transport per one aisle is taken into consideration. Another limitation is the indirectly randomization of certain model’s parameters.

Comparing the Efficiency of Intelligent Hybrid Operator Assistance Software with Intuitive Set-up (OASIS) for Assembly Production Line

Pick-To-Light Order Picking System is the operational process when an operator begins to pick the parts in a sequential manner by which the quantity of the parts is recorded. The design of an effective hybrid order picking process in an assembly line is assisted by an intelligent sensing system to improve pick efficiency, accuracy and increase productivity. This research compares between intelligent hybrid order picking versus order picking with pick confirmation system at parts assembly line. The results show that by our proposed system with the elimination of certain steps within the picking process, the better efficiency, accuracy, fewer miss-picks will occur in the system and the operator can perform more intelligently with required picking quantities. The development of this system can provide a low-cost solution with an intelligent order picking system for small and medium-sized enterprises (SMEs) and a fast-moving production assembly line in manufacturing.

Application of Simulation Methods for Study on Availability of One-Aisle Machine Order Picking Process

The main aim of the paper is the analysis of simulation model reflecting selected in-warehouse logistics processes in the aspect of their availability. For this purpose, one-aisle machine picking problem with use of a stochastic random simulation is studied, with a special focus on reliability of the system to disturbances and maintenance scheduling. The methodology in the research consists of classic measures of reliability. The model is designed in order to analyze availability of selected parameters of randomly generated order picking process. One of key-results of the paper is answer for question if a mean time to failure can be treated as a value of time when the first failure in the system occurs. A summary of the contribution includes discussion and the perspectives for further research in the subject matter.

The impact of the applied technology on health and productivity in manual "picker-to-part" systems.

Manual "picker-to-part" order picking takes place in a labour-intensive and time-consuming working environment where humans are the central actors and co-determine the effectiveness and efficiency of the process. Throughout Europe, work-related musculoskeletal disorders affect millions of workers, especially in the logistics sector, and cost employers billions of euros. This paper studies how order pickers relate the use of technology as well as their relationship with the logistics company to their well-being, health and productivity. To obtain data, a survey consisting of questions regarding work characteristics, health problems and the logistics company's relationship with employees was conducted in Poland, Slovenia and Croatia. Workers who carry most items manually experience more health problems than cart and forklift users. The most common complaint is lower back pain - only 6% of order pickers (n = 221) never experienced it. The use of barcode or RFID scanner/terminal/smart phone correlates with more health problems than the use of other technologies. Participation in the selection of transport means or in training on health preservation can reduce the perceived health problems. Workers' perception of the impact of the applied technology on health and productivity can differ from the impact that is calculated or measured. Through their relationship with employees, logistics companies can influence employees' perception of their health problems.

Sensitivity Analysis in Finding Shortest Path for Order Picker with Limited Picking Capacity

The success of any warehouse management is normally contributed by the success of order picking process. In any warehouse operation, order picking contributes more than half percentage of the total warehouse operating costs. Layout of warehouse, storage allocation, number of order pickers and various routing methods contribute to significant impact on the order picking process. All of these variables need to be considered in ensuring smooth and fast order picking process. We test the variation of each variable in finding the optimal route for each order picker, using a modified Dynamic Programming method. Real data set of a warehouse operation is used for the study. The results show a reduced of total distance by 11.6% by choosing the shortest path based on the number of order pickers and an increase by 48% using batch picking method. The shortest distances for each OP with limited capacity are also discussed thoroughly in this paper.