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This paper proposes a new metaheuristic algorithm called Particle Swarm-based picking time minimization (Pkt_PSO), ideated for picking time minimization in manual warehouses. As the name suggests, Pkt_PSO is inspired by Particle Swarm Optimization (PSO), and it is specifically designed to minimize the picking time in order case picking contexts. To assess the quality and the robustness of Pkt_PSO, it is compared to five alternative algorithms used as benchmarks. The comparisons are made in nine different scenarios obtained by changing the layout of the warehouse and the length of the picking list. The results of the analysis show that Pkt_PSO has a slower convergence rate and suffers less of early stagnation in local minima; this ensures a more extensive and accurate exploration of the solution space. In fact, the solutions provided by Pkt_PSO are always better (or at least comparable) to the ones found by the benchmarks, both in terms of quality (closeness to the overall best) and reliability (frequency with which the best solution is found). Clearly, as more solutions are explored, the computational time of Pkt_PSO is longer, but it remains compatible with the operational needs of most practical applications. [ABSTRACT FROM AUTHOR]

In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207. [ABSTRACT FROM AUTHOR]

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

This study concentrates on the storage location assignment problem which is effective in the operation of order picking systems in warehouse management. The problem of storage location assignment occurs simultaneously with production planning activities in real life and requires multiple criteria to be considered. The effective implementation of the problem solution can only be achieved by taking into account these features of the problem together. For this purpose, a mixed-integer nonlinear programming (MINLP) mathematical model is proposed by regarding the class-based storage policy for the integrated problem involving the assignment of products to storage locations and production quantity of each product, which is faced in the operation of order picking systems within warehouse management. In order to test the effectiveness of the proposed model, the solution is made using the data set available in the literature and the solution results are compared with the solution results of the dedicated policy model. As a result of the solution, it is determined that the proposed model saves space cost compared to the dedicated policy model. [ABSTRACT FROM AUTHOR]

To reduce unproductive picker walking in traditional picker-to-parts warehousing systems, automated guided vehicles (AGVs) are used to support human order pickers. In an AGV-assisted order-picking system, each human order picker is accompanied by an AGV during the order-picking process. AGVs receive the picked items and, once a picking order is complete, autonomously bring the collected items to the shipping area. Meanwhile, a new AGV is requested to meet the picker at the first storage position of the next picking order. Thus, the picker does not have to return to a central depot and continuously picks order after order. This paper addresses both the routing of an AGV-assisted picker through a single-block, parallel-aisle warehouse and the sequencing of incoming orders. We present an exact polynomial time routing algorithm for the case of a given order sequence, which is an extension of the algorithm of Ratliff and Rosenthal [Ratliff HD, Rosenthal AS (1983) Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Oper. Res. 1(3):507–521], and a heuristic for the case in which order sequencing is part of the problem. In addition, we investigate the use of highly effective traveling salesman problem (TSP) solvers that can be applied after a transformation of both problem types into a standard TSP. The numerical studies address the performance of these methods and study the impact of AGV usage on picker travel: by using AGVs to avoid returns to the depot and by sequencing in (near-) optimal fashion, picker walking can be reduced by about 20% compared with a traditional setting. Sharing AGVs among the picker workforce enables a pooling effect so that, in larger warehouses, only about 1.5 AGVs per picker are required to avoid picker waiting. Summary of Contribution: New technologies, such as automatic guided vehicles (AGVs) are currently considered as options to increase the efficiency of the order-picking process in warehouses, which is responsible for a large part of operational warehousing costs. In addition, picker-routing decisions are more and more often based on algorithmic decision support because of their relevance for decreasing unproductive picker walking time. This paper addresses both aspects and investigates routing algorithms for AGV-assisted order picking in parallel-aisle warehouses. We present a dynamic programming routine with polynomial runtime to solve the problem variant in which the sequence of picking orders is fixed. For the variant in which this sequence is a decision, we show that the problem becomes NP-hard, and we propose a greedy heuristic and investigate the use of state-of-the-art exact and heuristic traveling salesman problem solution methods to address the problem. The numerical studies demonstrate the effectiveness of the algorithms and indicate that AGV assistance promises strong improvements in the order-fulfillment process. Because of the practical relevance of AGV-assisted order picking and the presented algorithmic contributions, we believe that the paper is relevant for practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Because order picking is the most demanding, the most labor intensive and, accordingly, the costliest activity in a warehouse it has been potential for the optimization from different points of view and at different decision-making levels. In this research our goal was to examine a possible improvement of the order picking process by reducing the potential of the aisle congestion which is quite frequent in order picking systems with lots of pickers passing through same isles simultaneously. For that purpose, we give a quadratic mixed integer programming solution approach, as well as a Variable Neighborhood Search based heuristic algorithm solution approach. Beside that we employ a reduction-based strategy for reducing running times of the heuristic algorithms. Eventually, we tested the efficiency of developed models on an imaginary order picking system. [ABSTRACT FROM AUTHOR]

This study focuses on streamlining the order-picking process in a warehouse. We consider determining the picking sequence of items in a pick-list to minimize the total traveled distance in a multiblock warehouse, where a low-level picker-to-parts manual picking system is employed. We assume that the items are stored randomly in the warehouse. First, we construct a distance matrix of the shortest path between any pair of items. Next, using the distance matrix, we implement two meta-heuristics--the tabu search algorithm and the iterated greedy algorithm--to determine the picking sequence with the minimum total traveled distance. Through a numerical study, the performances of the meta-heuristic algorithms are compared with those of popular rule-based heuristics (S-shape, largest gap, and Combined+) and the bestknown solutions. We conducted the numerical study in two stages. In the first stage, we considered a two-block rectangular warehouse, and in the second stage, we considered a three-block rectangular warehouse. The performance of the heuristics was calculated based on the optimal solution when available or the best calculated bound when the optimal solution is not available. We observed that the iterated greedy algorithm significantly outperforms the other heuristics for both stages. [ABSTRACT FROM AUTHOR]

The material handling industry in order to increase the productivity and quality of the order picking process has developed various technical or technological equipment. Therefore, to establish the right technology for every specific business context is a decision that need to be evaluated in a right way. The purpose of this paper is to create an intelligent decision model to select the most appropriate order picking technology. The present study shows an artificial neural network (ANN) trained with the results of an analytic hierarchy process (AHP). The weighting of the determining criteria and the prioritization of the different technologies from several experts are obtained through the AHP, while the artificial neural network is used to automate the decision process. The designed ANN can synthesize expert judgments and then predict the prioritization of order picking technologies. [ABSTRACT FROM AUTHOR]

Customer order response time and system throughput capacity are key performance measures in warehouses. They depend strongly on the storage strategies deployed. One popular strategy is to split inventory into a bulk storage and a pick stock, or Forward-Reserve (FR) storage. Managers often use a rule of thumb: when the ratio m of average picks per replenishment is larger than a certain factor, it is beneficial to split inventory. However, research that systematically quantifies the benefits is lacking. We quantify the benefits analytically by developing response travel time models for FR storage in an Automated Storage/Retrieval system combined with order picking. We compare performance of FR storage with turnover class-based storage, and find when it pays off. Our findings illustrate that, in FR storage systems where forward and reserve stocks are stored in the same rack, FR storage usually pays off, as long as m is sufficiently larger than 1. The response time savings can go up to 50% when m is larger than 10. We validate these results using real data from a wholesale distributor. [ABSTRACT FROM AUTHOR]

In manual order picking systems, a paper list specifying the name, location, and amount of each item is often given, and this would exert mental pressure on pickers while finding target shelf bins in high-density picking environments. Augmented reality (AR) can provide a friendly alternative to improve the manual order picking performance by conveying picking information into visual instruction. Nevertheless, there is still no systematic consensus to deploy the pick-by-AR associated with actual warehouse workplaces. To establish a spatial correspondence between the visual guidance with the actual workplace, the multi-marker-based global map about the warehouse floor is established in advance. Instead of traditional single marker-based pick-by-AR methods, the warehouse floor-related map can provide an accurate and continuous navigation performance for intuitive AR guidance, allowing the picker to move freely on the warehouse floor without limiting to some certain locations. Besides, a systematic pick-by-AR solution is available by integrating the proposed method to a lightweight wearable AR device, and this easy-to-deploy pick-by-AR solution can alleviate the worker's mental effort while doing picking action. Finally, the pick-by-AR system described in the paper is deployed in the automobile assembly line, and experimental results illustrate that the proposed method can improve the picking efficiency while reducing the picking errors compared with the current paper-based order picking. [ABSTRACT FROM AUTHOR]

This paper presents a mixed-reality (MR) application called order picking with mixed reality (OP-MR) for the order-picking activities in a smart warehouse. OP-MR is a set of applications operated by an administrator through a computer server and by the staff using the HoloLens MR device. OP-MR is built to reduce the operational time of an order-picking activity by providing the shortest route to the staff. The HoloLens device displays the order-picking instructions through the MR window, renders virtual navigation, and virtually marks the positions of items. For determining the shortest distance for an order picking, the proposed OP-MR method combines two different algorithms, namely the Held–Karp algorithm in the server and A* algorithm in the client. The Held–Karp algorithm sorts the items in the pick-up list based on the nearest position. Next, the A* algorithm determines the shortest route to ensure that a user travels the shortest distance to pick all the items. To show the effectiveness of the proposed OP-MR method, OP-MR is implemented and experiments are performed. The experimental results show that OP-MR outperforms paper-based order-picking from the viewpoint of completing all the order picking. [ABSTRACT FROM AUTHOR]

Order picking has often been considered as one of the most labor- and time-intensive tasks in warehouse operations. Among the various planning problems that have to be solved in manual picker-to-parts systems, the routing of the order picker usually accounts for the highest share of the total warehouse operating cost. To minimize the cost of order picking, researchers have developed various routing procedures that guide the order picker through the warehouse to complete given customer orders. For some warehouse layouts such as the chevron warehouse, an optimal routing algorithm has not yet been proposed. This article, therefore, contributes to filling this research gap by developing an optimal order picker routing policy for the chevron warehouse. The optimal routing algorithm proposed in this article is based on the concept of graph theory and utilizes a dynamic programming procedure. In addition, we propose various simple routing heuristics for the chevron warehouse. In computational experiments, the average order picking tour lengths resulting from optimal routing and from the simple heuristics are compared. Moreover, we compare the performance of the chevron warehouse to the conventional two-block warehouse under various conditions using the tour lengths obtained by the optimal algorithms. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of electronic commerce via the Internet, there is a need to streamline the supply chain in order to reduce product distribution costs and shorten delivery lead times. Among these, the role of distribution centers that connect physical and commercial distribution has become more important. Improvement in work efficiency in the distribution centers has also become an important theme. Aware of such problems, we focus on order picking operations where products are collected based on the customer order since it is one of the most labor-intensive activities in the distribution center. We consider methods to shorten the average traveling distance of order-picking operators from two perspectives: 1) Improving the floor layout of the distribution center, and 2) introducing better management rules to determine the storage position of products. In the first half of the paper, the mathematical model to evaluate the average traveling distance of the picker is proposed. The model proposed assumes that the width of the aisle and the size of the shelf are sufficiently small, and obtains the value of the traveling distance analytically. In addition, improvement measures are considered from the two perspectives stated above, and are evaluated by comparing with the case in conventional Traditional Layout with horizontal and vertical aisles. In the second half of the paper, two improvement measures combining the two perspectives are proposed based on the results of the first half of the paper. Both measures move the accumulation point of products to the center of the lower side of the floor. In addition, the first measure adopts the addition of a diagonal aisle on the floor and classbased management to determine the storage position of products. The second measure executes class-based storage in an area where the work floor is divided using diagonal straight lines, which means being equidistant from the accumulation point of products. As a result, this paper clarifies that the two improvement measures proposed in the second half of the paper shorten the average traveling distance by 25% or more compared to case in conventional Traditional Layout. [ABSTRACT FROM AUTHOR]

This study focused on the influencing factors for a wide-aisle order picking system and found that the blocking time ratio is influenced by the picking density and number of picking faces. Under the condition of a one-to-one ratio between the picking and walking speeds, we construct a discrete-time Markov state transition probability matrix. We studied the steady state of the matrix, therein analysing the relationships among the blocking time ratio, pick density and number of picking faces, and we determined the extreme point of the blocking time ratio. The research results can provide reference for picking strategy selection and represent the theoretical basis of random process application research on logistics operation systems. [ABSTRACT FROM AUTHOR]

The article addressed the problem of the allocation of tasks in one of the warehouse processes (order picking). The objective was to obtain the optimum solution, which guarantees the minimum execution time of particular tasks by warehouse personnel. To optimise the issue mentioned above Excel spreadsheet with the Solver module was applied. [ABSTRACT FROM AUTHOR]

Scattered storage is a storage assignment strategy where single items are isolated and distributed all around the shelves of a warehouse. This way, the probability of always having some items per stock-keeping units close-by is increased, which is intended to reduce the unproductive walking time during order picking. Scattered storage is especially suited if each order line demands just a few items, so that it is mainly applied by business-to-consumer online retailers. This paper formulates a storage assignment problem supporting the scattered storage strategy. We provide and test suited solution procedures and investigate important managerial aspects, such as the frequency with which refilling the shelves should be executed. The online appendix is available at https://doi.org/10.1287/trsc.2017.0779. [ABSTRACT FROM AUTHOR]

Paper presents analysis of the order picking system efficiency under congestion situations. Analysed picking system is manual and organized as picker-to-part system with very narrow aisles. About 150 different variants of the system was taken into consideration. Each of them is characterized by different storage policies (random, class-based and volume-based), number of order pickers and picking strategies (single picking and batch picking). Main attention was paid to picker blocking phenomenon and its impact on order picking efficiency. Authors explain where, when and why such situations may happen. Additionally, they point out how the number of pickers, storage policy and picking strategy impact pickers blocking and picking process efficiency. Due to complexity of presented issue, dynamics and volatility of picking processes, simulation method was implemented. [ABSTRACT FROM AUTHOR]

This paper examines a single-direction, high density, deep-lane warehouse and develops a truck based, one-cycle order picking automated storage and retrieval system (AS/RS) that minimizes either travel time or energy consumption. The warehouse is considered completely full at the beginning of the picking task, and the alignment of the trucks at the output station is known before they arrive. The time and energy based algorithm assigns the best cell address of the product pallet and optimum shuttle robot according to the truck order picking instructions. Unlike previous studies, this study focuses on a reserved storage part in the warehouse for orders of trucks and defines overall aisle and shuttle robot components. Different from most algorithms, the algorithm used in this study identifies not only the minimum travel time route, but also the least energy consuming route as two different cases. The order picking task with the reserved storage area as the destination is formulated as an analytical problem. Energy and time based order picking period simulation results are compared, and real applications in the warehouse of the project partner firm are discussed. [ABSTRACT FROM AUTHOR]

The aging society in many developed countries has made an ergonomic workplace design to an important topic among researchers and practitioners alike. We investigate the workplace design for order pickers that manually collect items from the shelves of a warehouse. Specifically, we treat the storage assignment, i.e., the placement of products in shelves of different height, and zoning, i.e., the partitioning of the storage space into areas assigned to separate pickers, in the fast pick area of a warehouse. A fast pick area unifies the most fast-moving items in a compact area, so that workers are relieved from unproductive travel, but face extraordinary ergonomic risks due to the frequent repetition of picking operations. Concerning the health of (aged) workers, it is crucial to reduce such risks. Thus, we define a combined ergonomic storage assignment and zoning problem with the objective of minimizing the maximum ergonomic burden among all workers. This problem is formalized, and two construction heuristics and a tabu search procedure are proposed. Our results show that neglecting ergonomic aspects and only focusing on picking performance leads to much higher ergonomic risks of the workforce. [ABSTRACT FROM AUTHOR]

Existing on-line order batching rules, namely fixed time window batching (FTWB) and variable time window batching (VTWB), try to choose the fixed time window in the case of FTWB or the fixed number of orders in the case of VTWB. However, these solutions are not appropriate for the fluctuating order environment. The reasonable assignment of batches to order pickers is also an important issue in order picking systems. Motivated by these issues, we study the problem of integrating on-line order batching and the assignment of the batches, which is called the on-line order batching and sequencing problem with multiple pickers (OOBSPMP). The objective is to minimize the turnover time. To solve the problem, a hybrid rule-based algorithm, referred to FTWB, is proposed in order to form batches and assign them to appropriate pickers under a fluctuating order environment. Three batching situations (off-peak, normal and peak arrival time) and two assigning situations (assigning to one busy picker and assigning to one idle picker) are distinguished. Through a series of experiments, we discover several enlightening findings: (i) the rule-based algorithm demonstrates high effectiveness and efficiency in turnover time with multiple pickers; (ii) the rule-based algorithm leads to an impressive improvement in both saving time and wage costs under different arrival rates, picking devices and time intervals compared with VTWB; (iii) to obtain both good warehouse performance and a reasonable workload distribution, the factors, such as the fixed time window, the average workload per picker and the average idle time per picker are also important issues in analysing the efficiency of order picking systems. [ABSTRACT FROM AUTHOR]

Even small improvements in storage operations can bring significant benefits in customer response time, increasing the level of service offered. However, a company cannot offer high level of service for all products. In this sense, one of the ways used to optimize the activities for order picking is the study of storage location assignment problem (SLAP). Thus, this paper proposed a multi-criteria decision model to perform the products classification and to solve the SLAP in a multi-layer warehouse. The ELECTRE TRI method is used to define the shelf level for each product, and the ELECTRE III method is used to establish a fixed location for each product on the shelf it belongs to. The great advantage of this model is that there is not trade-off among the criteria, and it is able to consider several criteria simultaneously. However, the main goal is not optimization, but the search for a storage operations performance balance, considering multiple criteria, allowing for a better inventory management when considering profitability criteria, for example, and improvement in the order picking operations, through the demand and space required criteria. In addition, it considers the preferences of the decision maker. [ABSTRACT FROM AUTHOR]

The warehouse operation planning in the scholar and office supplies industry encompasses the activities of receiving, storing, order picking, and shipping products. The order picking operation is a laborious and capital intensive procedure. Despite the extensive literature to solve practical problems of warehouse routing as a step of the order picking process, the application of general approaches to deal with the routing of the warehouse pickers is not straightforward given the specific conditions and configurations and the singular design and operational policies commonly found in practice. In this paper, we present an optimization approach to the stacker routing problem found in the order picking of a Brazilian scholar and office supplies company. The approach is based on a routing algorithm that solves different integer linear problems to define the optimal routes of the stacker inside the facility. The solutions of several numerical experiments show the potential and effectiveness of the approach, for instance, that only one fifth of the routes actually performed by the company are optimal and that the algorithm application to a real set of batches reduced by more than one fourth the total distance monthly traveled by the stacker. [ABSTRACT FROM AUTHOR]

This paper addresses an order picking problem in a multi-aisle automated warehouse, in which a single storage/retrieval (S/R) machine performs storage and retrieval operations. When retrieval requests consist of multiple items and the items are in multiple stock locations, the S/R machine must travel to several storage locations to complete a customer order. The objective is to minimize the total time traveled by the S/R machine to complete the retrieval process of customer orders at the shortest time. First, we formulate the problem as a nonlinear programming model. Then, we propose a heuristic to solve the problem. Finally, we provide numerical experiments to evaluate the performance of the proposed heuristic. The results show that as the number of items in customer order increases, the heuristic shows a better performance by obtaining solutions close to optimal but in very small amount of times. [ABSTRACT FROM AUTHOR]

We investigate two routing problems that arise when order pickers traverse an aisle in a warehouse. The routing problems can be viewed as Euclidean travelling salesman problems with points on two parallel lines. We show that if the order picker traverses only a section of the aisle and then returns, then an optimal solution can be found in linear time, and if the order picker traverses the entire aisle, then an optimal solution can be found in quadratic time. Moreover, we show how to approximate the routing cost in linear time by computing a minimum spanning tree for the points on the parallel lines. [ABSTRACT FROM AUTHOR]

A real life order picking system consisting of a set of unidirectional picking lines is investigated. Batches of stock keeping units (SKUs) are processed in waves defined as a set of SKUs and their corresponding store requirements. Each wave is processed independently on one of the parallel picking lines as pickers walk in a clockwise direction picking stock. Once all the orders for a wave are completed a new mutually exclusive set of SKUs are brought to the picking line for a new wave. SKUs which differ only in size classification, for example small, medium and large shirts, are grouped together into distributions (DBNs) and must be picked in the same wave. The assignment of DBNs to available picking lines for a single day of picking is considered in this paper. Different assignments of DBNs to picking lines are evaluated using three measures, namely total walking distance, the number of resulting small cartons and work balance. Several approaches to assign DBNs to picking lines have been investigated in literature. All of these approaches seek to minimise walking distance only and include mathematical formulations and greedy heuristics. Four different correlation measure are introduced in this paper to reduce the number of small cartons produced and reduce walking distance simultaneously. These correlation measures are used in a greedy insertion algorithm. The correlation measures were compared to historical assignments as well as a greedy approach which is known to address walking distances effectively. Using correlation measures to assign DBNs to picking lines reduces the total walking distance of pickers by 20% compared to the historical assignments. This is similar to the greedy approach which only considers walking distance as an objective, however, using correlations reduced the number of small cartons produced by the greedy approach. [ABSTRACT FROM AUTHOR]

Introduced research work relates to the possibility of material flow enhancement in production systems, with the apostrophe on material order picking in production assembly lines. The paper presents basic rules and the results related to formed computer models of zoned order picking systems under the application of developed bound cavities method. [ABSTRACT FROM AUTHOR]

The performance of a warehouse is impacted by how it is configured, yet there is no optimization model in the literature to answer the question of how to best configure the warehouse in terms of warehouse shape and the configuration of the dock doors. Moreover, the building blocks for such a model (put-away, replenishment, and order picking models that can be combined in an optimization model) are either not available (in the case of replenishment) or built on a set of inconsistent assumptions (in the case of put-away and order picking). Therefore, this article lays the foundation for more sophisticated warehouse configuration optimization models by developing the first analytical model for replenishment operation performance and extending put-away and order picking performance models. These new models are used to address a question motivated by industry: the optimal configuration of a case-picking warehouse in terms of the shape of the facility and whether the facility is configured with dock doors on one or both sides. An example is presented to demonstrate the use of the proposed models in answering such a question, quantifying the benefit of using an integrated approach to warehouse configuration. [ABSTRACT FROM PUBLISHER]

Nowadays, customer orders with high product variety in small quantities are often received and requested for timely delivery. However, the order-picking process is a labour-intensive and costly activity to handle those small orders separately. In such cases, small orders are often grouped into batches so that two or more orders can be served at once to increase the picking efficiency and thus reduce the travel distance. In this paper, an order-picking operations system (OPOS) is proposed to assist the formulation of an order-picking plan and batch-handling sequence. The study integrates a mathematical model and fuzzy logic technique to divide the receiving orders into batches and prioritise the batch-handling sequence for picking, respectively. Through the proposed system, the order-picking process can be managed as batches with common picking locations to minimise the travel distance, and the batch-picking sequence can be determined as well. To demonstrate the use of the system, a case study in a third-party logistics warehouse is presented, and the result shows that both the order-picking activity and labour utilisation can be better organised. [ABSTRACT FROM PUBLISHER]

A recent trend in the layout design of unit-load warehouses is the application of layouts without conventional parallel pick aisles and straight middle aisles. Two examples for such designs are flying-V and fishbone designs for single- and dual-command operations. In this study, it is shown that the multi-item order picking problem can be solved in polynomial time for both fishbone and flying-V layouts. These two designs are compared with the traditional parallel-aisle design under the case of multi-item pick lists. Simple heuristics are proposed for fishbone layouts that are inspired by those put forward for parallel-aisle warehouses and it is experimentally shown that a modification of the aisle-by-aisle heuristic produces good results compared with other modified S-shape and largest gap heuristics when items have uniform demand. Computational experiments are performed in order to compare the performances of fishbone and traditional layouts under optimal routing and it is shown that a fishbone design can obtain improvements of around 20% over parallel-aisle design in single-command operations but can perform as high as around 30% worse than an equivalent parallel-aisle layout as the size of the pick list increases. The sensitivity of the results to varying demand skewness levels when volume-based storage is applied is tested and it is shown that unlike the single- and dual-command cases, a fishbone design performs better compared to a traditional design under highly skewed demand as opposed to uniform demand. [ABSTRACT FROM AUTHOR]

In this paper the sequencing of orders on a unidirectional picking line is considered. The aim of the order sequencing is to minimise the number of cycles travelled by a picker within the picking line to complete all orders. A tabu search, simulated annealing, genetic algorithm, generalised extremal optimisation and a random local search are presented as possible solution approaches. Computational results based on real life data instances are presented for these metaheuristics and compared to the performance of a lower bound and the solutions used in practise. The random local search exhibits the best overall solution quality, however, the generalised extremal optimisation approach delivers comparable results in considerably shorter computational times. [ABSTRACT FROM AUTHOR]

The objective of this study is to design a correlated storage assignment system by storing items with demand dependences together to minimize zone visits when picking materials/parts in production line. The demand dependences can be obtained from the products' bill of materials (BOMs) and production rates. Due to the large number of materials in a BOM, picking capacity constraint is explicitly considered. A mathematical model is formulated and heuristic algorithms are proposed, including two direct heuristics and two hybrid genetic algorithms with different crossover mechanisms. Numerical experimentation shows that the hybrid genetic algorithm with 2-point crossover outperforms others and can be practical in warehouse design. [ABSTRACT FROM AUTHOR]

Abstract: We develop discrete time models for the throughput time distribution of orders arriving to a one-block warehouse. The models accommodate single- or multi-line orders, and we show how to use them to determine the optimal batch size, given a desired probability of on-time order fulfillment. Experiments suggest that the optimal batch size is slightly higher than one would choose if minimizing average throughput time. [Copyright &y& Elsevier]

Given a picker-to-part warehouse having a simple rectilinear aisle arrangement with north–south storage aisles and east–west travel aisles (or “cross-aisles”), this article investigates the optimal placement of the cross-aisles as a consequence of the probability mass function of the order pick points, as determined by the storage policy. That is, what placement of the cross-aisles will result in a minimal expected path length for the picker? An analytical solution procedure is developed for the optimal placement of a single middle cross-aisle for a given storage policy. A simplifying assumption is made as regards picker routing, but arbitrary non-random storage policies are considered. The solution procedure is generalized to a method for multiple cross-aisles. Some example problems are solved and a simulation study is used to measure the impact of the assumptions made to generate the method. [ABSTRACT FROM PUBLISHER]

In today's competitive scenario of increasingly faster deliveries and smaller order sizes, a solution that is being used more frequently is the pick-and-pass Order Picking System (OPS). The purpose of this study is to define a framework for the pick-and-pass system design, by expanding previous literature on this topic. The framework aims at minimising the overall picking costs while respecting the required service level (i.e. order throughput time), and can be easily applied to the selection stage of OPS design. The number of zones and the number of pickers per zone are among the main project data for the framework application. Analytical models are used to estimate the travel distance, and network queuing theory to analyse the mean order throughput. On the basis of the proposed framework, pick-and-pass system performance is examined as a function of two typical design conditions: order size and number of items. [ABSTRACT FROM AUTHOR]

Abstract: Determining the proper storage locations for potentially thousands of products is one major task that faces the warehouse manager during the design of a new warehouse or the refurbishment of existing facility. There are many factors affects the storage assignment. For example, order picking method, size and layout of the storage system, material handling system, product characteristics, demand trends, turnover rates and space requirements. Selecting appropriate storage assignment policies (i.e. random, dedicated or class-based) and routing methods (i.e. transversal, return or combined) with regards to above factors is a possible solution to tackle this problem. However, the efficiency of these policies is interdependent. This paper aims to present a simulation study of a real case regarding storage assignment problem of a manual-pick and multi-level rack warehouse. Performance of the case is measured in terms of travel distance and order retrieval time. The results of the study indicate that the key to effective implementation of a storage assignment system is to match the types of warehouse storage system and the variety of items in the customer order. Also, the use of key performance indicator should clearly reflect the needs of the warehouse. [ABSTRACT FROM AUTHOR]

This paper gives an overview of recent research on the performance evaluation and design of carousel systems. We discuss picking strategies for problems involving one carousel, consider the throughput of the system for problems involving two carousels, give an overview of related problems in this area and present an extensive literature review. Emphasis has been given on future research directions in this area. [ABSTRACT FROM AUTHOR]

Carousel conveyors (also known simply as "carousels") are an important and well-known type of material handling system used in warehousing and manufacturing to store/retrieve small-to-medium-sized parts. A traditional multi-tier (or multi-level) carousel, which is powered by a single drive, rotates as a whole unit, and brings the items to the input/output point one at a time, since each level cannot be rotated independently. A special type of carousel (also known as a "rotary rack") uses a dedicated drive for each level of the carousel. This allows each level of the carousel to rotate independently of and concurrently with the other levels. It also allows a rotary rack to support multiple pickers (or robots) while a traditional carousel can support typically only one picker. Although rotary racks have been available for quite some time, questions about their performance and efficient use remain largely unanswered, especially in comparison with traditional carousels, which have been the subject of numerous studies. Assuming one or more picking robots, in this paper we use a simulation model to develop and analyze four retrieval strategies for a rotary rack. We compare the performance of the four strategies as a function of the input data and the number of robots in the system. We also address the question of the appropriate number of robots. As one would expect, the simulation results indicate that the throughput of the system increases with the number of robots. However, more robots also lead to more idle time per robot. We show the tradeoff between the expected time required to process an order and the expected idle time per robot. [ABSTRACT FROM AUTHOR]

Order picking is one of the most important process steps in logistics. Because of their flexibility, human beings cannot be replaced by machines. But if workers, in order, picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization. In this paper, the development of such a system—called Pick-by-Vision—is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as the subjective strain were measured. The results show that a Pick-by-Vision system can considerably improve industrial order picking processes. [ABSTRACT FROM AUTHOR]

Increasingly important issues for every organization in the warehousing business are to achieve a high efficiency of all processes, minimize operational costs and maximize flexibility of the labor force. Among all processes, order picking is recognized to be the most significant and most costly activity and therefore tends to get the most attention. Key objectives in designing an order picking operation include increases in productivity and accuracy, and reduction of cycle time and replenishment costs. The aim of this article is to present a proposal of improved wave planning processes and modifications to the wave planning technique. With those modifications it is possible to reduce cost and time to process a wave by reducing the distance traveled by an order picker, the retrieval time per item and the replenishment costs. [ABSTRACT FROM AUTHOR]

Numerous design and cost parameters, combined with an endless variety of equipment types, make it difficult to choose the right order picking system (OPS). The purpose of this study is to develop a methodology to support warehouse designers in choosing the most suitable OPS. By developing a new OPS classification, we carried out an in-depth survey on over 68 distribution centres that have been recently built in Italy. The results of the critical analysis allowed developing a design methodology to choose the most suitable OPS. This methodology has been integrated in the structured procedure for OPS design, developed by Yoon and Sharp ( IIE Trans 28:379–389, ). Finally, a numerical case study is presented to illustrate the application of the proposed design methodology. [ABSTRACT FROM AUTHOR]

An approximation method based on G/G/m queuing network modeling using Whitt's queuing network analyzer to analyze pick-and-pass order picking systems is presented. The objective of this approximation method is to provide an instrument for obtaining rapid performance estimates (such as order lead time and station utilization) of an order picking system. The pick-and-pass system is decomposed into conveyor segments and pick stations. Conveyor segments have a constant processing time, whereas the service times at a pick station depend on the number of order lines in the order to be picked at the station, the storage policy at the station and the work practices. The proposed approximation method appears to be sufficiently accurate for practical purposes. It can be used to rapidly evaluate the effects of the storage methods in pick stations, the number of order pickers at stations, the size of pick stations, the arrival process of customer orders and the impact of batching and splitting orders on system performance. [ABSTRACT FROM AUTHOR]

The forward pick area of a distribution center is a cache of conveniently located products from which order pickers can quickly draw, but which must be replenished from bulk or reserve storage. The quantities stored forward determine the amount of work required to sustain the forward pick area. Two stocking strategies that are commonly used in industry are analyzed and compared with the optimal stocking strategy for small parts. [ABSTRACT FROM AUTHOR]

Order picking is the warehousing process by which products are retrieved from their storage locations in response to customers' orders. Its efficiency can be influenced through the layout of the area and the operating policies. We present a model that minimizes travel distances in the picking area by identifying an appropriate layout structure consisting of one or more blocks of parallel aisles. The model has been developed for one commonly used routing policy, but it is shown to be fairly accurate for some other routing policies as well. [ABSTRACT FROM AUTHOR]

Nowadays, warehouses are not only used for protecting and keeping materials, but also serves customers as quick distribution centers. Products and raw materials have to be picked from warehouses more effectively and efficiently to transform materials in time to customers and accelerate the production. So, while providing optimization of a warehousing system, it is aimed to reduce traveling time and traveling cost. At this study, for a chosen firm, it is tried to optimize order picking strategy. Firstly, materials' location are observed, all optimal rotation is determined to provide optimal picking. Routes which are provided to reduce the traveling cost and time are determined, a new warehousing system is proposed and order picking time is improved. [ABSTRACT FROM AUTHOR]

• A soft computing-based column generation heuristic for order picking is proposed. • The proposed algorithm is compared against PSA-ACO and an exact method. • Based on numerical experiments some managerial insights are proposed. At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented. [ABSTRACT FROM AUTHOR]

Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets. [ABSTRACT FROM AUTHOR]