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In this paper, we discuss an approximation method based on G/G/m queuing network modeling using Whitt’s (1983) queuing network analyzer to analyze pick-and-pass order picking systems. The objective of this approximation method is to provide an instrument for obtaining rapid performance estimates (such as order lead time and station utilization) o...
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... picking, the process of picking products to fill customer orders, is one of the most important activities in warehouses due to its high contribution (about 55%) to the total warehouse operating cost ( Tompkins et al., 2003). This paper considers a common type of pick- and-pass order picking system, which consists of a conveyor connecting all pick stations located along the conveyor line, as sketched in Figure 1. Storage shelves are used to store products at each pick station. ...
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... The types of order picking, which are tailored to different operational scales and efficiency goals, include single order picking, best suited for small businesses where individual orders are processed independently [8]; batch order picking, ideal for consolidating multiple orders to minimize picker travel [9][10][11]; zone order picking, designed for large businesses to divide picking tasks by warehouse zones [12,13]; pick-and-pass, where items are sequentially picked and passed along a predefined route [14]; cluster order picking, focused on grouping similar orders to optimize picking time [15]; and wave order picking, used to coordinate picking schedules with outbound shipping times [16]. Order-picking systems have also been categorized, including sorting systems for the automated organization of items [17]; pick-to-box systems, which streamline the packing process by picking items directly into order-specific boxes [18][19][20]; picker-to-part systems, where pickers move to retrieve items from storage locations (this study); and part-to-picker systems, where items are brought to pickers via automated solutions for increased efficiency [21,22]. ...
Storage operations, order-picking, and product-handling processes have become increasingly important in today’s industrial environment. These operations are a huge burden for businesses in terms of time and cost, but they often do not add direct value to products or services. Therefore, it has become essential to improve the storage operations to the highest quality, reduce the costs arising from storage, and increase customer satisfaction. This study compared genetic algorithm (GA) and simulated annealing (SA) methods with existing real results and operations in order to minimize the distance traveled by the picker in order-picking systems, optimize routes, and increase operational efficiency in the medical textile industry. In the analyses conducted on product-based, list-based, and order-based strategies, real data sets were used to examine the performance of both methods in detail. The study results revealed that GA reduced the total travel distance by 50% and reduced the total number of tours from 51 to 32. In addition, the SA method provided efficient results in certain scenarios, but GA showed superior performance in terms of minimizing the distance and number of tours. While the product-based strategy provided the best results regarding travel distance and number of tours, the list-based approach showed a balanced performance. The study offers significant improvement potential in logistics operations by reducing distances by up to 37% and increasing operational efficiency by up to 50% in order-picking processes.
... Gong and De Koster (2008) provide an estimate for the mean of order waiting time in a dynamic order-picking system. Yu and De Koster (2008) model the order picking system with stochastic order picking time, where the picker picks only one line item in a trip. They use Whitt's queuing network analyzer and develop the first and second moments of order picking time. ...
... In progressive zone picking, additional order consolidation is not required because the orders are gradually consolidated as the order tote visits each zone. De Koster (1994), Yu and de Koster (2009a), Yu and De Koster (2008), and Melacini et al. (2011) propose a queuing network model to estimate performance statistics of a conveyor-based zone picking system. In their analysis, they do not consider order tote blocking and congestion effects. ...
During the last decade, several retailers have started to combine traditional store deliveries with the fulfillment of online sales to consumers from omni‐channel warehouses, which are increasingly being automated. A popular option is to use autonomous mobile robots (AMRs) in collaboration with human pickers. In this approach, the pickers' unproductive walking time can be reduced even further by zoning the storage system, where the pickers stay within their zone periphery and robots transport order totes between the zones. However, the robotic systems' optimal zoning strategy is unclear: few zones are particularly good for large store orders, while many zones are particularly good for small online orders. We study the effect of no zoning (NZ) and progressive zoning strategies on throughput capacity for balanced zone configurations with both fixed and dynamic order profiles. We first develop queuing network models to estimate pick throughput capacity that correspond to a given number of AMRs and picking with a fixed number of zones. We demonstrate that the throughput capacity is dependent on the chosen zoning strategy. However, the magnitude of the gains achieved is influenced by the size of the orders being processed. We also show that using a dynamic switching strategy has little effect on throughput performance. In contrast, a fixed switching strategy benefiting from changes in the order profile has the potential to increase throughput performance by 17% compared to the NZ strategy, albeit at a higher robot cost.
... However, this measure is positively correlated with order completion time. Our result is also in line with the study of Yu and de Koster (2008), who also looked at the trade-off between handling time and station transport time, for pick and pass systems, with a variable number of stations (but with a fixed number of pickers). With a growing number of pick stations, the resulting mean order throughput time (which is correlated with makespan) decreases. ...
Many warehouses involved in e-commerce order fulfillment use robotic mobile fulfillment systems. Because demand and variability can be high, scheduling orders, robots, and storage pods in interaction with manual workstations are critical to obtaining high performance. Simultaneously, the scheduling problem is extremely complicated because of interactions between decisions, many of which must be taken timely because of short planning horizons and a constantly changing environment. This paper models all such scheduling decisions in combination to minimize order fulfillment time. We propose two decision methods for the above scheduling problem. The models batch the orders using different batching methods and assign orders and batches to pods and workstations in sequence and robots to jobs. Order picking and stock replenishment operations are included in the models. We conduct numerical experiments based on a real-world case to validate the efficacy and efficiency of the model and algorithm. Instances with 14 workstations, 400 orders, 300 stock-keeping units (SKUs), 160 pods, and 160 robots can be solved to near optimality within four minutes. Our methods can be applied to large instances, for example, using a rolling horizon. Because our model can be solved relatively fast, it can be used to take managerial decisions and obtain executive insights. Our results show that making integrated decisions, even when done heuristically, is more beneficial than sequential, isolated optimization. We also find that positioning pick stations close together along one of the system’s long sides is efficient. The replenishment stations can be grouped along another side. Another finding is that SKU diversity per pod and SKU dispersion over pods have strong and positive impacts on the total completion time of handling order batches.
Funding: This work was supported by National Natural Science Foundation of China [72025103, 72361137001, 71831008, 72071173] and the Research Grants Council of the Hong Kong Special Administrative Region, China [HKSAR RGC TRS T32-707/22-N].
Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0265 .
... The pick-and-pass concept usually assumes that the picking process runs without losing the integration of orders: each order is picked separately [23,34]. However, in a few papers, order-batching (during the order-picking process, orders lose their integrity, after which additional effort is needed for sorting) is considered for pick-and-pass systems [11,31,43,44]. ...
... The presented approach can be used for establishing, e.g., the required number of zones, the conveyor speed, the size of batches, or the number of batches processed per period. Yu and De Koster [43] use the G/G/m queuing network to analyse pick-and-pass systems. The methods for estimating the number of zones and the number of pickers in each zone in pick-and-pass systems are described by Melacini et al. [27]. ...
Pick-and-pass systems are a part of picker-to-parts order-picking systems and constitute a very common storage solution in cases where customer orders are usually small and need to be completed very quickly. As workers pick items in the zones connected by conveyor, their work needs to be coordinated. The paper presents MILP models that optimize the order-picking process. The first model uses information about expected demand for items to solve the storage location problem and balance the workload across zones. The task of the next model is order-batching and sequencing – two concepts are presented that meet different assumptions. The results of the exemplary tasks solved with the use of the proposed MILP models show that the total picking time of a set of orders can be reduced by about 35-45% in comparison with random policies. The paper presents an equation for the lower bound of a makespan. Recommendations about the number of zones that guarantee the required system efficiency are also introduced.
... In the literature, most studies on warehouse order picking have focused only on picker routing or order batching. Articles that only consider order picking can be classified into certain [4][5][6][7][8][9][10][11][12][13][14][15][16][17] and uncertain cases [18,19]. Picker routing can be considered a traditional traveling-salesman problem (TSP). ...
Because of time and cost constraints, item picking plays a major role in warehouse operations. Considering diversified orders and a constant warehouse design, deciding how to combine each batch and picker route effectively is a challenge in warehouse management. In this study, we focus on the evaluation of order-batching strategies for a single picker facing multiple orders with the objective of minimizing the total traveling distance. We propose two-stage simulated annealing and variable neighborhood search algorithms to solve the combined problem. The orders are first merged into batches, followed by determining the sequence in each batch. The computational analysis revealed that the best-fit-decreasing (BFD) batch ordering strategy in the two-stage algorithms, the variable neighborhood search algorithm, obtained superior solutions to those of the simulated annealing algorithm.
... Thus, only workstation-based order fulfillment systems where the picker remains stationary are treated in this paper. There are other warehousing systems that also aim to reduce the walking effort of pickers by confining them to a smaller pick-face, such as pick-andpass systems ( Melacini, Perotti, & Tumino, 2011;Yu & de Koster, 2008 ), crane-supplied pick faces ( Schwerdfeger & Boysen, 2017;Yu & de Koster, 2010 ), and trolley line systems ( Füßler, Boysen, & Stephan, 2019a;Füßler, Fedtke, & Boysen, 2019b ). A detailed overview on these systems is provided by the survey papers of Boysen, de Koster, & Weidinger (2019) , Boysen et al. (2020) . ...
Triggered by the great success of e-commerce, today’s warehouses more and more evolve to fully-automated fulfillment factories. Many of them follow the parts-to-picker paradigm and employ shelf-lifting mobile robots or conveyors to deliver stock keeping units (SKUs) to stationary pickers operating in picking workstations. This paper aims to structure and review the family of synchronization problems that arise in this environment: If multiple orders demanding the same SKU can be serviced jointly, then a more efficient picking process and a relief of the bin supply system can be achieved. This paper classifies the family of slightly varying synchronization problems arising with different workstation setups in alternative warehouses. This classification scheme is applied to analyze computational complexity, to systematically quantify the gains of alternative workstation setups, and to benchmark the performance gains of synchronization with those of other well-established decision tasks. Our results show that the right workstation setup can greatly improve throughput performance, so that the gains of synchronization can outreach those promised by other well-researched decision tasks.
... De Koster (1994) analyses such a system as a Jackson queuing network where the order inter-arrival times and the zone service times are exponentially distributed. Yu and de Koster (2008) generalise the distribution of order interarrival and zone service times, and Yu and de Koster (2009) consider the general queuing network to analyse the impact of batching and zone-picking on throughput times. Melacini, Perotti, and Tumino (2011) extend the above-mentioned queuing literature by considering the number of zones and the number of pickers for each zone as variables. ...
In both manual and automated warehouses, a combination of efficient zoning and picker routing plays an important role in improving travel time, congestion, and system throughput. This paper considers the order picker routing problem in a dynamic and synchronised zoning environment, where the items corresponding to each customer order are picked simultaneously in multiple zones, and zones may change between different orders. The objective is to minimise the maximum time of completing the picking activities in any zone. Using a min–max type of objective not only minimises the makespan of an order picking wave, but it also helps balance the workload of the order pickers more effectively. We present a mathematical model for the optimal solution of this problem, as well as a dynamic programming approach to find the optimal solution for the case where a zone is a set of adjacent aisles. Computational experiments on randomly generated instances show that the dynamic programming approach is able to find optimal solutions in negligible computational times.
... However, this measure is positively correlated with order completion time. Our result is also in line with the study of Yu and de Koster (2008), who also looked at the trade-off between handling time and station transport time, for pick and pass systems, with a variable number of stations (but with a fixed number of pickers). With a growing number of pick stations, the resulting mean order throughput time (which is correlated with makespan) decreases. ...
... no balancing measure is part of the objective function or constraints). Yu and De Koster (2008) develop an approximation model to analyse the performance of pick-and-pass order picking systems. The model can be used to rapidly evaluate the effects of storage methods, number of pickers and size of pick stations, the arrival process of customer orders and the impact of batching and splitting orders on system performance. ...
An intensified competition forces warehouses to handle more orders in shorter time windows. This complicates the timely retrieval of these customer orders. Planning order picking operations, thereby aiming to increase efficiency, inevitably results in balancing concerns, such as imbalances among pick areas, pickers or time periods. Reducing workload imbalances, therefore anticipating on workload peaks, results in a more stable order picking process. However, there exist several measures that can be used to evaluate and correct existing imbalances. This study contributes to academic literature by analysing, explaining and evaluating the effectiveness of various workload balancing approaches (e.g. Rawlsian's approach, range, mean-based) in order picking operations, more specifically in the context of balancing workload over time in case of restricted time windows for retrieving customer orders. Results show that the effect of warehouse layout characteristics and customer order parameters on the effectiveness of balancing measures is very limited. However, the underlying managerial reason (e.g. workforce allocation, transportation schedule or human well-being) for solving the operational workload balancing problem does significantly impact the effectiveness and choice of an appropriate balancing measure.
