Multiple Pickers Research Articles

REliable PIcking by Consensus (REPIC): a consensus methodology for harnessing multiple cryo-EM particle pickers

Cryo-EM particle identification from micrographs (“picking”) is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. State-of-the-art computational algorithms (“pickers”) identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REliable PIcking by Consensus (REPIC), a computational approach to identifying particles common to the output of multiple pickers. We frame consensus particle picking as a graph problem, which REPIC solves using integer linear programming. REPIC picks high-quality particles even when the best picker is not known a priori or a protein is difficult-to-pick (e.g., NOMPC ion channel). Reconstructions using consensus particles without particle filtering achieve resolutions comparable to those from particles picked by experts. Our results show that REPIC requires minimal (often no) manual intervention, and considerably reduces the burden on cryo-EM users for picker selection and particle picking. Availability: https://github.com/ccameron/REPIC.

A new mathematical model and meta-heuristic algorithm for order batching, depot selection, and assignment problem with multiple depots and pickers

The ‘order picking problem’ involves the efficient and organized retrieval of items from shelves to fulfill customer orders. In this study, we consider a new configuration of warehouse system with multiple pickers and depots (m-pickers & n-depots) for manually operated ‘picker-to-parts’ warehouses. The efficiency of the process is measured based on two metrics: i- total order picking distance of each picker ii- total number of pickers assigned to each of depots. The handled problem is called as ‘Order Batching, Depot Selection and Assignment Problem with Multiple Depots and Multiple Pickers (OBDSAPMDMP)11OBDSAPMDMP = Order Batching, Depot Selection, and Assignment Problem with Multiple Depots and Multiple Pickers.’. To solve this complex problem, a new bi-objective Mixed-Integer Linear Programming (MILP) formulation for small-sized problems and a meta-heuristic called ‘Dependent Harmony Search (DHS)22DHS = Dependent Harmony Search.’ for large-sized problems are proposed. The performance of DHS algorithm is evaluated by comparing the optimal results attained by MILP model. For the problem size of 10 orders, the average gap (%) in distance between the solution of DHS and MILP is 4.22%, although in some experiments DHS can find the optimal solution in a very short time. Also, in related analysis, it is seen that constructing multiple depots instead of one left-most located depot decreases total order picking distance by 7.11% on average.

Human-Robot Cooperation: Coordinating Autonomous Mobile Robots and Human Order Pickers

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 .

Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing

With the growing demand for speed and flexibility in order fulfillment, it is crucial to employ an efficient picking system that allows faster delivery of items from warehouse storage to a depot. The use of autonomous mobile robots (AMRs) for intra-logistics can improve picking productivity, while alleviating the strain on human workers. In this research, a collaborative human–robot order-picking system (CHR-OPS) is considered, where humans perform item retrieval tasks and AMRs handle item transportation to the depot. The delivery performance of a CHR-OPS for a given set of orders and their due dates depend on three subproblems: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts system employing multiple pickers and AMRs. An optimization model is developed to jointly optimize the three problems with the objective of minimizing the total tardiness of all orders. A simulated annealing algorithm with adaptive neighborhood search and optimization-based restart strategy is proposed for handling large instances. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human–robot team composition, AMR speed, AMR capacity and warehouse layout.

Cost-neutral reduction of infection risk in picker-to-parts warehousing systems

The rapid and severe outbreak of COVID-19 caused by SARS-CoV-2 has heavily impacted warehouse operations around the world. In particular, picker-to-parts warehousing systems, in which human pickers collect requested items by moving from picking location to picking location, are very susceptible to the spread of infection among pickers because the latter generally work close to each other. This paper aims to mitigate the risk of infection in manual order picking. Given multiple pickers, each associated with a given sequence of picking tours for collecting the items specified by a picking order, we aim to execute the tours in a way that minimizes the time pickers simultaneously spend in the same picking aisles, but without changing the distance traveled by the pickers. To achieve this, we exploit the degrees of freedom induced by the fact that picking tours contain cycles which can be traversed in both directions, i.e., at the entry to each of these cycles, the decision makers can decide between the two possible directions. We formulate the resulting picking tour execution problem as a mixed integer program and propose an efficient iterated local search heuristic to solve it. In extensive numerical studies, we show that an average reduction of 50% of the total temporal overlap between pickers can be achieved compared to randomly executing the picking tours. Moreover, we compare our approach to a zone picking approach, in which infection risk between pickers can be almost eliminated. However, compared to our approach, the results show that the zone picking approach increases the makespan by up to 1066%.

The integrated orderline batching, batch scheduling, and picker routing problem with multiple pickers: the benefits of splitting customer orders

Fast delivery is one of the most popular services in e-commerce retail. It consists in shipping the items ordered on-line in short times. Customer orders in this segment come with deadlines, and respecting this latter is pivotal to ensure a high service quality. The most time-consuming process in the warehouse is order picking. It consists in regrouping orders into batches, assigning those batches to order pickers, sequencing the batches assigned to each order picker such that the orders deadlines are satisfied, and the picking time is minimized. To speed up the order picking operations, e-commerce warehouses implement new logistical practices. In this paper, we study the impact of splitting the orders (assigning the orderlines of an order to multiple pickers). We thus generalize the integrated orders batching, batch scheduling, and picker routing problem by allowing the orders splitting and propose a route first-schedule second heuristic to solve the problem. In the routing phase, the heuristic divides the orders into clusters and constructs the picking tours that retrieve the orderlines of each cluster using a split-based procedure. In the scheduling phase, the constructed tours are assigned to pickers such that the orders deadlines are satisfied using a constraint programming formulation. On a publicly available benchmark, we compare our results against a state-of-the-art iterated local search algorithm designed for the non-splitting version of the problem. Results show that splitting the customer orders using our algorithm reduces the picking time by 30% on average with a maximum reduction of 60%.

A heuristic approach for the online order batching problem with multiple pickers

The Online Order Batching Problem with Multiple Pickers (OOBPMP) consists of optimizing the operations related to the picking process of orders in a warehouse, when the picking policy follows an order batching strategy. In this case, this variant of the well-known Order Batching Problem considers the existence of multiple workers in the warehouse and an online arrival of the orders. We study three different objective functions for the problem: minimizing the completion time, minimizing the picking time, and minimizing the differences in the workload among the pickers. We have identified and classified all previous works in the literature for the OOBPMP. Finally, we propose a multistart procedure hybridized with a Variable Neighborhood Descent metaheuristic to handle the problem. We test our proposal over well-known instances previously reported in the literature by empirically comparing the performance of our proposal with previous methods in the state of the art. The statistical tests corroborated the significance of the results obtained.

Solving the order batching and sequencing problem with multiple pickers: A grouped genetic algorithm

This paper introduces a grouped genetic algorithm (GGA) to solve the order batching and sequencing problem with multiple pickers (OBSPMP) with the objective of minimizing total completion time. To the best of our knowledge, for the first time, an OBSPMP is solved by means of GGA considering picking devices with heterogeneous load capacity. For this, an encoding scheme is proposed to represent in a chromosome the orders assigned to batches, and batches assigned to picking devices. Likewise, the operators of the proposed algorithm are adapted to the specific requirements of the OBSPMP. Computational experiments show that the GGA performs much better than six order batching and sequencing heuristics, leading to function objective savings of 18.3% on average. As a conclusion, the proposed algorithm provides feasible solutions for the operations planning in warehouses and distribution centers, improving margins by reducing operating time for order pickers, and improving customer service by reducing picking service times.

Formulations for joint order picking problems in low-level picker-to-part systems

This article introduces several mathematical formulations for the joint order picking problem (JOPP) in low-level picker-to-part warehousing systems. In order to represent real warehousing environments, the proposed models minimize performance measures such as travel distance, travel time and tardiness, considering multi-block warehouses, due dates, and multiple pickers. The number of constraints and decision variables required for each proposed model is calculated, demonstrating the complexity of solving medium and long-sized problems in reasonable computing time using exact methods. The proposed models can be followed as a reference for new solution methods that yield efficient and fast solutions.

A rule-based heuristic algorithm for on-line order batching and scheduling in an order picking warehouse with multiple pickers

In manual order picking systems, orders received from internal or external customers are collected by pickers that walk or ride through the warehouse. Generally, orders are grouped into several sub sets, i.e. batches to reduce picking time and cost. This paper considers the on-line order batching problem (OOBP) in an order picking warehouse with multiple pickers in which the maximum completion time of all batches (customer orders) has to be minimized. At first, a mathematical model is introduced for the off-line version of the problem. Then the on-line version of the problem is considered in which customer orders become available dynamically over time. Since the proposed model is NP-hard, a rule-based heuristic algorithm was proposed to solve the on-line problem. The main contributions of the present work is to propose a mathematical model for the off-line order batching problem with multiple pickers considering makespan minimization and to present a novel heuristic algorithm for solving the on-line version of the problem. To validate the proposed algorithm, it is proved that its competitive rate is equal to 2. Finally, the solution algorithm is evaluated through a series of experiments and the most appropriate routing, batching and selection policies are introduced.

Mathematical programming modeling for joint order batching, sequencing and picker routing problems in manual order picking systems

This article aims to introduce mathematical programming models for the joint order batching and picker routing problem (JOBPRP) and the joint order batching, sequencing and routing problem (JOBSPRP). For this purpose, we present formulations for the traveled distance and travel time between picking positions in low-level and high-level picker-to-part systems (2D and 3D warehouses) and single-block and multiple-block warehouses. Likewise, we formulate Steiner traveling salesman problem (STSP) models considering multiple pickers, heterogeneous picking vehicles, multiple objectives and due windows. We calculate the number of binary variables, continuous variables and constraints for the proposed models, in order to show the complexity of solving these order picking problems using exact solution methods. As a result, we introduce several mathematical models for manual order picking systems, which could serve as references for researchers interested in finding optimal or high-quality solutions to joint order picking problems, considering realistic warehouse and distribution center environments.

Order Batching Optimization for Warehouses with Cluster-Picking

This paper focuses on optimizing the static order batching problem (OBP) with multiple pickers. This problem involves optimal assignment of a set of customer orders to batches, given capacity constraints, in order to minimize the total distance travelled. The order picking strategy considered is cluster picking. We assume that each picker is assigned one batch and all pickers begin the pick tour simultaneously. The aim is to minimize the total distance travelled by all pickers, which is calculated based on the S-shape routing strategy. A mathematical model is developed to solve the OBP for small-scale instances. For larger instances, a simplified version of an order batching heuristic proposed by DHL Supply Chain is utilized and experimented with for different-sized problems. Both approaches are compared to the conventional first come first serve (FCFS) method of order batching.

Minimizing order picking makespan with multiple pickers in a wave picking warehouse

In this paper, order assignment, order batching and picker routing problems with multiple pickers in a wave picking warehouse of a major US third party logistics company is studied and modeled mathematically. The proposed mathematical model is solved using an exact algorithm. Since the exact algorithm suffers from long CPU time, a Lagrangian decomposition heuristic combined with particle swarm optimization (LD-PSO) algorithm is proposed, which performs well for small size waves. To solve large-scale problems, a hybrid parallel simulated annealing and ant colony optimization (PSA-ACO) is presented. The proposed methods are tested using the warehouse data. The results are compared against the minimum makespan impact (MMI) heuristic that is currently being used in the warehouse and a state-of-the-art variable neighborhood descent (VND). While PSA-ACO slightly outperforms VND, for picking large waves, PSA-ACO and VND can improve the makespan by approximately 7.8% and 6.9% over MMI respectively. Numerical experiments show that increasing number of pickers and picking capacity has a greater impact on reducing makespan when the ratio of orders to number of pickers or picking capacity is high.

SKU arrangement on a unidirectional picking line

AbstractAn order picking system consisting of independent unidirectional picking lines has been investigated in this study. A picking line forms a type of unidirectional carousel as multiple pickers walk in a clockwise direction around a conveyor belt running down the centre of the picking line. There are three decision tiers in this order picking system, namely the assignment of stock keeping units (SKUs) to different waves, the arrangement of SKUs into locations on a picking line for a wave of picking and the sequencing of orders. Each tier has an objective to minimise the total travel distance of pickers. The focus of this paper is on the arrangement of SKUs on a picking line for a wave of picking. An exact mathematical formulation is introduced which is not solvable to optimality for large real‐life instances. Two heuristics known to be optimal for certain carousel systems as well as two further heuristics using SKU correlations are therefore tested. The heuristic approaches are compared against lower bounds generated by relaxing the exact formulation and a set of random solutions. Known algorithms for carousel systems are not optimal in this carousel system and all heuristic approaches perform approximately equally well. It is shown that the gains from addressing the order sequencing decision tier outweighs that of the SKU arrangement decision tier. Moreover, the complexity of addressing the entire system of decision can be reduced by arranging SKUs with a simple heuristic having a negligible impact on solution quality.

A Distributed Adaptive Coordination Protocol to Enhance Manual Order Picking Operations

This paper proposes a distributed adaptive coordination protocol for efficient management of manual order picking operations in the presence of unexpected delays and process disruptions. In manual order picking operations, multiple pickers work concurrently to retrieve inventory from aisles in response to customer orders. The lack of pickers coordination often leads to congestion incidents and high non-value added time as pickers attempt to locally optimize their routing decisions. In this research, pickers are modeled as a collaborative network in which they share their work and traffic information, and accordingly adjust their routes to avoid congestion and manage aisle competition. An adaptive coordination protocol is developed to enable pickers efficiently reach consensus (i.e., agreement) state on route adjustments. The proposed protocol is compared using simulation against the baseline case, and the experimental results indicate that it minimizes total order picking time and pickers congestion by 10.4% and 62.6%, respectively.

Order picking with multiple pickers and due dates – Simultaneous solution of Order Batching, Batch Assignment and Sequencing, and Picker Routing Problems

In manual picker-to-part order picking systems, human operators (order pickers) walk or ride through the warehouse, retrieving items from their storage locations in order to satisfy a given demand specified by customer orders. Each customer order is characterized by a certain due date until which all items included in the order are to be retrieved. For the actual picking process, customer orders may be grouped (batched) into more substantial picking orders (batches). The items of a batch are then collected on a picker tour. Thus, the picking process of each customer order in the batch is completed when the picker returns to the depot after the last item of the batch has been picked. Whether and to what extent due dates are violated depends on how the customer orders are batched, how the batches are assigned to order pickers, how the assigned batches are sequenced and how the pickers are routed. Existing literature has only dealt with specific aspects of this problem so far. In this paper, for the first time, an approach is proposed which considers all subproblems simultaneously. A mathematical model of the problem is introduced that allows for solving small problem instances. For larger instances, a variable neighborhood descent algorithm is presented. By means of numerical experiments, it is demonstrated that the algorithm provides solutions of excellent quality. Furthermore, it is shown that a simultaneous solution approach to the above-mentioned subproblems can be considered as a significant source for improving the efficiency of operations in distribution warehouses.

On-line order batching and sequencing problem with multiple pickers: A hybrid rule-based algorithm

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.

A storage assignment heuristic method based on genetic algorithm for a pick-and-pass warehousing system

An order storage assignment problem (SAP) is to find an effective way to locate products in a warehouse in order to improve the operational efficiency of order picking. Since SAP is an NP-hard problem, many heuristic algorithms have been proposed. Most of previous researches focused on picker-to-parts warehousing systems or automated storage and retrieval systems. However, pick-and-pass systems play an important role for the faster delivery of small and frequent orders of inventory with the rise of e-commerce and e-business in the global supply chain. Two factors lead to idle time of pickers in a pick-and-pass system: picking line imbalance and shortage replenishment of products. This paper develops a genetic based heuristic method to solve SAP for a pick-and-pass system with multiple pickers to determine the appropriate storage space for each product and balance the workload of each picking zone so that the performance of the system can be improved. A simulation model based on FlexSim is used to implement the proposed heuristic algorithm and compare the throughput for different storage assignment methods as well. The results indicate that the proposed heuristic policy outperforms existing assignment methods in a pick-and-pass system.

An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse

One of the challenging problems in order picking is how to deal with the congestion happens in warehouse with multiple pickers. In this paper, we consider an ant colony optimization (ACO)-based online routing method to find picking routes for multiple order pickers under nondeterministic picking time. Here, a default route is formed by ACO for each single picker. Then, we coordinate these routes to alleviate congestion by dedicated rules based on indoor positioning and information sharing technologies, during order pickers serve the picking task. Our results indicate that the proposed method can achieve a reduction in the order service time primarily by coping with the congestion. We conclude that the new method is particularly effective in multiple-block picker-to-parts warehouses.

Order sequencing on a unidirectional cyclical picking line

A real life order-picking configuration that requires multiple pickers to cyclically move around fixed locations in a single direction is considered. This configuration is not the same, but shows similarities to, unidirectional carousel systems described in literature. The problem of minimising the pickers’ travel distance to pick all orders on this system is a variant of the clustered travelling salesman problem. An integer programming (IP) formulation of this problem cannot be solved in a realistic time frame for real life instances of the problem. A relaxation of this IP formulation is proposed that can be used to determine a lower bound on an optimal solution. It is shown that the solution obtained from this relaxation can always be transformed to a feasible solution for the IP formulation that is, at most, within one pick cycle of the lower bound. The computational results and performance of the proposed methods as well as adapted order sequencing approaches for bidirectional carousel systems from literature are compared to one another by means of real life historical data instances obtained from a retail distribution centre.