Algorithms for On-line Order Batching in an Order-Picking Warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these – indivisible – customer orders into picking orders. With respect to order batching, two problem types can be distinguished: In off-line (static) batching all customer orders are known in advance. In on-line (dynamic) batching customer orders become available dynamically over time. This report considers an on-line order batching problem in which the total completion time of all customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for the off-line order batching can be modified in order to deal with the on-line situation. A competitive analysis shows that every on-line algorithm for this problem is at least 2-competitive. Moreover, this bound is tight if an optimal batching algorithm is used. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the completion time of a set of customer orders.Warehouse Management, Order Picking, Order Batching, On-line Optimization

Algorithms for On-line Order Batching in an Order-Picking Warehouse

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items required by (internal or external) customers. Order batching consists of combining these - indivisible - customer orders into picking orders. With respect to order batching, two problem types can be distinguished: In off-line (static) batching all customer orders are known in advance. In on-line (dynamic) batching customer orders become available dynamically over time. This report considers an on-line order batching problem in which the total completion time of all customer orders arriving within a certain time period has to be minimized. The author shows how heuristic approaches for the off-line order batching can be modified in order to deal with the on-line situation. A competitive analysis shows that every on-line algorithm for this problem is at least 2-competitive. Moreover, this bound is tight if an optimal batching algorithm is used. The proposed algorithms are evaluated in a series of extensive numerical experiments. It is demonstrated that the choice of an appropriate batching method can lead to a substantial reduction of the completion time of a set of customer orders

Evolution of regional clusters in nanotechnology. Empirical findings from Germany

This article aims at establishing a wider understanding of the evolution of spatial clusters. It will be argued that the potential to generate regional growth is dependent on the way a cluster emerges. Two models of cluster formation will be distinguished in detail – start-up clusters and unrelated spatial concentrations. In its sectoral orientation the study is focused on nanotechnology, a key(technology)-industry said to contribute to new growth spurts in the industrialised world. By analysing the evolution of regional clusters in the Saarland and in Berlin-Brandenburg it will be shown that both types of cluster formation can be found in nanotechnology, demanding different modes of policy intervention

Variable Neighborhood Search for the Order Batching and Sequencing Problem with Multiple Pickers

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. The transformation and consolidation of customer orders into picking orders (batches) is pivotal for the performance of order picking systems. Typically, customer orders have to be completed by certain due dates in order to avoid delays in production or in the shipment to customers. The composition of the batches, their processing times, their assignment to order pickers and the sequence according to which they are scheduled determine whether and the extent to which the due dates are missed. This article shows how Variable Neighborhood Descent and Variable Neighborhood Search can be applied in order to minimize the total tardiness of a given set of customer orders. In a series of extensive numerical experiments, the performance of the two approaches is analyzed for different problem classes. It is shown that the proposed methods provide solutions which may allow order picking systems to operate more efficiently

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

Bridging Ruptures: The Re-Emergence of the Antwerp Diamond District after WW II

“Diamonds love Antwerp” – these three words constitute the present slogan of the Antwerp World Diamond Centre, the organisation concerned with the promotion of the diamond trade and industry in the Scheldt city and keeping the latter an important diamond hub in the age of globalisation with new diamond centres evolving especially in low-cost countries like China and Thailand (The New York Times, May 31 2005; EVEN-ZOHAR 2006, 371ff.). In fact, diamonds seem to have loved Antwerp even in the past as the city has been a major centre for trading and polishing the precious stones since the 15th century (WALGRAVE 1993, 37). Despite some ups and downs there had not been any interruption of the commercial activities in this sector until World War II reached Belgium and trading as well as processing diamonds gradually were discontinued (LAUREYS 2005, chapter 5f). Surprisingly, however, the Belgian diamond sector experienced a long-lasting boom after 1945, contributing significantly to the country’s economic power even though the former infrastructure had partly been taken away or destroyed, many workers had fled, been deported or killed and other diamond centres had evolved during the years of the German occupation (VAN DYCK 1989). Given these aspects, the paper on hand addresses the question whether the window of locational opportunity arising after the abrupt break of the trajectory was restricted by past structures that actually have favoured the re-emergence of the cluster at its former location. By doing so, two aspects of conceptual interest are concerned as well: On the one hand, evolutionary literature up to now has emphasised chance as the major determinant of the emergence of regional trajectories thereby neglecting the underlying social processes. Furthermore, due to the concentration on concepts like path-dependency and lock-in potential ruptures in the development of clusters were only seldom taken into account yet (BATHELT/BOGGS 2005; 2003)

Tabu Search Heuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or ride through a distribution warehouse in order to collect items requested by (internal or external) customers. In order to perform these operations efficiently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours necessary to collect all items is minimized. For the solution of this problem the authors suggest two approaches based on the tabu search principle. The first one is a straightforward classic Tabu Search algorithm (TS), the second one is the Attribute-Based Hill Climber (ABHC). In a series of extensive numerical experiments, the newly developed approaches are benchmarked against different solution methods from literature. It is demonstrated that the proposed methods are superior to existing methods and provide solutions which may allow for operating distribution warehouses significantly more efficiently

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

Metaheuristics for the Order Batching Problem in Manual Order Picking Systems

In manual order picking systems, order pickers walk or drive through a distribution warehouse in order to collect items which are requested by (internal or external) customers. In order to perform these operations effciently, it is usually required that customer orders are combined into (more substantial) picking orders of limited size. The Order Batching Problem considered in this paper deals with the question of how a given set of customer orders should be combined such that the total length of all tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic approaches for the solution of this problem; the rst one is based on Iterated Local Search, the second one on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed approaches are benchmarked against classic solution methods. It is demonstrated that the proposed methods are not only superior to existing methods, but provide solutions which may allow for operating distribution warehouses signicantly more effcient.Warehouse Management, Order Picking, Order Batching, Iterated Local Search, Ant Colony Optimization

Order Batching in Order Picking Warehouses: A Survey of Solution Approaches

Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders. Of all warehouse operations, order picking is considered to include the most cost-intensive ones. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. This article will focus on order batching, one of the main planning issues in order picking systems. Order Batching has been proven to be pivotal for the efficiency of order picking operations. With respect to the availability of information about the customer orders, order batching can be distinguished into static batching and dynamic batching. Improved order batching reduces the total picking time required to collect the requested articles. According to experience from practice, this can result in significant savings of labor cost and into a reduction of the customer order\u27s delivery lead time.The aim of this contribution is to provide comprehensive insights into order batching by giving a detailed state-of-the-art overview of the different solution approaches which have been suggested in the literature. Corresponding to the available publications, the emphasis will be on static order batching.In addition to this, the paper will also review the existing literature for variants and extensions of static order batching (e.g. due dates, alternative objective functions). Furthermore, solution approaches for dynamic order batching problems (like time window batching) will be presented