Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems concerns of the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid delay in the shipment to customers or in production. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent given due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second one is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow for operating order picking systems more efficiently. Solutions can be improved by 46% on average, compared to the ones obtained by standard constructive heuristics such as an application of the Earliest Due Date rule

Metaheuristics for Order Batching and Sequencing in Manual Order Picking Systems

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. A major issue in manual order picking systems concerns of the transformation and consolidation of customer orders into picking orders (order batching). In practice, customer orders have to be completed by certain due dates in order to avoid delay in the shipment to customers or in production. The composition of the picking orders, their processing times and the sequence according to which they are released have a significant impact on whether and to which extent given due dates are violated. This paper presents how metaheuristics can be used in order to minimize the total tardiness for a given set of customer orders. The first heuristic is based on Iterated Local Search, the second one is inspired by the Attribute-Based Hill Climber, a heuristic based on a simple tabu search principle. In a series of extensive numerical experiments, the performance of these metaheuristics is analyzed for different classes of instances. We will show that the proposed methods provide solutions which may allow for operating order picking systems more efficiently. Solutions can be improved by 46% on average, compared to the ones obtained by standard constructive heuristics such as an application of the Earliest Due Date rule.Warehouse Management, Order Batching, Batch Sequencing, Due Dates, Iterated Local Search, Attribute-Based Hill Climber

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

Order Picking Problem: A Model for the Joint Optimisation of Order Batching, Batch Assignment Sequencing, and Picking Routing

Background: Order picking is a critical activity in end-product warehouses, particularly using the picker-to-part system, entail substantial manual labor, representing approximately 60% of warehouse work. Methods: This study develops a new linear model to perform batching, which allows for defining, assigning, and sequencing batches and determining the best routing strategy. Its goal is to minimise the completion time and the weighted sum of tardiness and earliness of orders. We developed a second linear model without the constraints related to the picking routing to reduce complexity. This model searches for the best routing using the closest neighbour approach. As both models were too complex to test, the earliest due date constructive heuristic algorithm was developed. To improve the solution, we implemented various algorithms, from multi-start with random ordering to more complex like iterated local search. Results: The proposed models were tested on a real case study where the picking time was reduced by 57% compared to single-order strategy. Conclusions: The results showed that the iterated local search multiple perturbation algorithms could successfully identify the minimum solution and significantly improve the solution initially obtained with the heuristic earliest due date algorithm

Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning

In e-commerce markets, on time delivery is of great importance to customer satisfaction. In this paper, we present a Deep Reinforcement Learning (DRL) approach for deciding how and when orders should be batched and picked in a warehouse to minimize the number of tardy orders. In particular, the technique facilitates making decisions on whether an order should be picked individually (pick-by-order) or picked in a batch with other orders (pick-by-batch), and if so with which other orders. We approach the problem by formulating it as a semi-Markov decision process and develop a vector-based state representation that includes the characteristics of the warehouse system. This allows us to create a deep reinforcement learning solution that learns a strategy by interacting with the environment and solve the problem with a proximal policy optimization algorithm. We evaluate the performance of the proposed DRL approach by comparing it with several batching and sequencing heuristics in different problem settings. The results show that the DRL approach is able to develop a strategy that produces consistent, good solutions and performs better than the proposed heuristics.Comment: Preprin

Order picking in parallel-aisle warehouses with multiple blocks::complexity and a graph theory-based heuristic

In this paper, we consider the order picking problem (OPP), which constitutes one of the special cases of the Steiner travelling salesperson problem and addresses the costliest operation in a warehouse. Given a list of items to be picked and their locations in the warehouse layout, the OPP aims to find the shortest route that starts from a depot point, picks all the items in the list, and returns to the depot. This paper fills two important gaps regarding the OPP. First, to the best of our knowledge, we present the first complexity results on the problem. Second, we propose a heuristic approach that makes use of its graph-theoretic properties. Computational experiments on randomly generated instances show that the heuristic not only outperforms its state-of-the-art counterparts in the literature, but it is also robust in terms of changing problem parameters

Estimation des distances lors de la préparation de commande

RÉSUMÉ : Le système de préparation de commande est l’un des processus les plus importants dans le fonctionnement d’un entrepôt. Dans un contexte où les entreprises cherchent à optimiser chaque fonctionnement, le processus de préparation de commande ne fait pas l’exception et les entreprises doivent constamment réévaluer la performance des systèmes établis. Plusieurs axes d’amélioration se posent. Les quatre plus pertinents ont été listés dans ce travail : (1) l’implantation de la zone d’entreposage, (2) la méthode de préparation de commande, (3) le modèle de circulation et (4) la politique de localisation des stocks. Le concepteur se trouvera finalement en face d’un système de préparation de commande dont il doit mesurer la performance. La revue de littérature présente plusieurs indicateurs qui ont été regroupés en six classes : indicateurs du temps, de distance, de rendement, de coût, techniques et orientés-clients. Le choix entre ces indicateurs se fait de plusieurs façons qui ne se basent pas sur une méthodologie précise. En plus, il n’y a pas de relations claires entre le choix des indicateurs et l’axe d’amélioration retenu. Par conséquent, le suivi de plusieurs indicateurs à la fois semble nécessaire. À travers ce mémoire, nous présentons un outil analytique pour estimer la distance parcourue et suivre l’un des plus importants indicateurs de performance. À cette fin, un exemple d’entrepôt, que nous jugeons représentatif de plusieurs entrepôts, a été choisi avec un jeu de données générées aléatoirement. Finalement, les résultats donnés par l’outil analytique ont été comparés avec des résultats de simulation.----------ABSTRACT : The order picking system is one of the most important processes in the operation of a warehouse. In a context where companies seek to optimize each operation, the order picking process is no exception and companies must constantly re-evaluate the performance of established systems. There are several areas for improvement. The four most relevant ones are listed in this work: (1) warehouse layout, (2) order preparation method, (3) circulation model and (4) location policy stocks. The designer is finally faced with a command preparation system whose performance must be evaluated. The literature review presents several indicators that have been grouped into six classes: time, distance, performance, cost, technical and customer-oriented indicators. The choice between these indicators is made in several ways that are not based on a specific methodology. In addition, there is no clear relationship between the choice of indicators and the improvement axis. Therefore, the monitoring of several indicators at a time seems necessary. Through this paper, we present an analytical tool to estimate the distance traveled and to follow one of the most important performance indicators. To this end, an example of a warehouse, which we consider representative of warehouses found in the industry, was selected with a randomly generated data set. Finally, the results given by the analytical tool were compared with simulation results