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

Evaluation of order picking systems using simulation

Sipari? toplama faaliyetleri, tedarik zinciri yönetiminde, hem üretim sistemleri açısından (montaj istasyonlarına alt parçaların tedarik edilmesi), hem de dağıtım i?lemleri açısından (mü?teri taleplerinin kar?ılanması) kritik rol oynamaktadır. Mü?teri sipari?lerindeki eğilimler, az sayıda ve yüksek miktarlarda sipari?lerin çok sayıda ve dü?ük miktarlarda sipari?lere dönü?tüğünü göstermektedir. Diğer yandan, talep edilen sipari? teslim süreleri ise her geçen gün kısalmaktadır. Bu deği?imler, i?letmelerin piyasada rekabet edebilmeleri için etkin ve esnek bir sipari? toplama sistemi benimsemelerini gerektirmektedir. Sipari? toplama süreci, tüm lojistik operasyonlarını ve mü?teriye sağlanan hizmet seviyesini büyük ölçüde etkilemektedir. Ayrıca, sipari? toplama süreci toplam depolama maliyetlerinin yarıdan fazlasını olu?turmaktadır. Bu nedenle, sipari? toplama faaliyetlerinin en etkin ?ekilde gerçekle?tirilmesi i?letmeler için büyük önem ta?ımaktadır. Bu çalı?manın amacı, sipari? toplama süresini kısaltarak, sipari? toplama etkinliğini arttırmaya yönelik deği?iklikler için sipari? toplama sistemini değerlendirmek ve geli?tirmektir. Sipari? toplama süresi, ürünlerin depolama alanlarından belirli bir mü?teri talebini kar?ılamak amacıyla toplanması süreci için geçen zamandır. Bu çalı?mada, ürünlerin depolama alanlarına atanma kararları ve rotalama metotları gibi kritik faktörlerin yanı sıra, daha önce gerçekle?tirilmi? çalı?malarda sıkça rastlanmayan depolama alanlarının ikmali problemi dikkate alınmı?tır. Bo?alan rafların yeniden doldurulması kararında, (S, s) envanter politikası uygulanmı?tır. Böylece, sipari? toplama sistemi dinamik olarak modellenmi?tir. Sipari? toplama performansını geli?tirmek için, bağlantı elemanları üreten bir firmanın ambarı temel alınarak olu?turulmu? hipotetik bir ambar üzerinde vii çalı?ılmı?tır. Ambara ait farklı benzetim modelleri olu?turulmu?, depolama ve rotalama politikalarının alternatif kombinasyonları geli?tirilerek bu benzetim modellerinde kullanılmı?tır. Elde edilen benzetim sonuçlarına göre, en küçük sipari? toplama süresini veren depolama ve rotalama politikası kombinasyonu belirlenmi?tir. Son olarak, benzetim sonuçları üzerinde bazı istatistiksel analiz metotları uygulanmı?tır Order picking activities play a critical role in supply chain management in terms of both production systems (supplying components to assembly operations) and distribution operations (meeting customer demands). Trends in customer orders reveal that orders are transformed from few-and-large orders to many-and-small ones. On the other hand, lead times of customer orders get consistently shorter. Because of these changes, companies need to adopt an effective and flexible order picking system in order to remain competitive in the market. Order picking affects both overall logistic operations and service level provided to customers. Additionally, order picking process constitutes more than half of the total warehousing cost. For these reasons, it is crucial for companies to design and perform an effective order picking process. The aim of this study is evaluating and improving of the order picking system so as to minimize the order retrieval time while increasing the picking efficiency. Order retrieval time can be defined as the time elapsed for the process of retrieving products from storage area to meet a specific customer demand. Besides the critical factors such as storage assignment decisions and routing methods, replenishment problem of the storage areas, which is rarely addressed in the previous studies, has been taken into consideration in this study. Replenishment of the empty storage locations has been conducted by using the (S, s) inventory policy. Thus, the order picking system was modeled as a dynamic system. A hypothetical distribution warehouse, based on the real life warehouse of a company specialized in production of fasteners, has been studied in order to improve the order picking performance. Alternative combinations of routing and storage policies have been developed. Moreover, different simulation models of the order picking process were constructed. In these models, proposed alternative storage and v routing policies were operated. According to the simulation results, the storage policy and routing policy combination which provides the shortest order retrieval time is determined. Finally, using simulation results, some statistical analysis methods have been implemented

Workforce Scheduling with Order-Picking Assignments in Distribution Facilities

Scheduling the availability of order pickers is crucial for effective operations in a distribution facility with manual order pickers. When order-picking activities can only be performed in specific time windows, it is essential to jointly solve the order picker shift scheduling problem and the order picker planning problem of assigning and sequencing individual orders to order pickers. This requires decisions regarding the number of order pickers to schedule, shift start and end times, break times, as well as the assignment and timing of order-picking activities. We call this the order picker scheduling problem and present two formulations. A branch-and-price algorithm and a metaheuristic are developed to solve the problem. Numerical experiments illustrate that the metaheuristic finds near-optimal solutions at 80% shorter computation times. A case study at the largest supermarket chain in The Netherlands shows the applicability of the solution approach in a real-life business application. In particular, different shift structures are analyzed, and it is concluded that the retailer can increase the minimum compensated duration for employed workers from six hours to seven or eight hours while reducing the average labor cost with up to 5% savings when a 15-minute flexibility is implemented in the scheduling of break times

Designing new models and algorithms to improve order picking operations

Order picking has been identified as a crucial factor for the competitiveness of a supply chain because inadequate order picking performance causes customer dissatisfaction and high costs. This dissertation aims at designing new models and algorithms to improve order picking operations and to support managerial decisions on facing current challenges in order picking. First, we study the standard order batching problem (OBP) to optimize the batching of customer orders with the objective of minimizing the total length of order picking tours. We present a mathematical model formulation of the problem and develop a hybrid solution approach of an adaptive large neighborhood search and a tabu search method. In numerical studies, we conduct an extensive comparison of our method to all previously published OBP methods that used standard benchmark sets to investigate their performance. Our hybrid outperforms all comparison methods with respect to average solution quality and runtime. Compared to the state-of-the-art, the hybrid shows the clearest advantages on the larger instances of the existing benchmark sets, which assume a larger number of customer orders and larger capacities of the picking device. Finally, our method is able to solve newly generated large-scale instances with up to 600 customer orders and six items per customer order with reasonable runtimes and convincing scaling behavior and robustness. Next, we address a problem based on a practical case, which is inspired by a warehouse of a German manufacturer of household products. In this warehouse, heavy items are not allowed to be placed on top of light items during picking to prevent damage to the light items. Currently, the case company determines the sequence for retrieving the items from their storage locations by applying a simple S-shape strategy that neglects this precedence constraint. As a result, order pickers place the collected items next to each other in plastic boxes and sort the items respecting the precedence constraint at the end of the order picking process. To avoid this sorting, we propose a picker routing strategy that incorporates the precedence constraint by picking heavy items before light items, and we develop an exact solution method to evaluate the strategy. We assess the performance of our strategy on a dataset provided to us by the manufacturer. We compare our strategy to the strategy used in the warehouse of the case company, and to an exact picker routing approach that does not consider the given precedence constraint. The results clearly demonstrate the convincing performance of our strategy even if we compare our strategy to the exact solution method that neglects the precedence constraint. Last, we investigate a new order picking problem, in which human order pickers of the traditional picker-to-parts setup are supported by automated guided vehicles (AGVs). We introduce two mathematical model formulations of the problem, and we develop a heuristic to solve the NP-hard problem. In numerical studies, we assess the solution quality of the heuristic in comparison to optimal solutions. The results demonstrate the ability of the heuristic in finding high-quality solutions within a negligible computation time. We conduct several computational experiments to investigate the effect of different numbers of AGVs and different traveling and walking speed ratios between AGVs and order pickers on the average total tardiness. The results of our experiments indicate that by adding (or removing) AGVs or by increasing (or decreasing) the AGV speed to adapt to different workloads, a large number of customer orders can be completed until the respective due date