Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

Quick Response Practices at the Warehouse of Ankor

In the warehouse of Ankor, a wholesaler of tools and garden equipment, various problems concerning the storage and retrieval of products arise. For example, heavy products have to be retrieved prior to light products to prevent damage. Furthermore, the layout of the warehouse differs from the layout generally assumed in literature. The goal of this research was to determine storage locations for the products and a routing method to obtain sequences in which products are to be retrieved from their locations. It is shown that despite deviations from the "normal" case, similar savings in route length can be obtained by adapting existing solution techniques. Total labor savings are far less than expected on basis of assumptions made in literature. With a minimum of adaptations to the current situation the average route length can be decreased by 30 %. There is no need for complex techniques.storage;warehousing;optimization;case study;routing

Job Sequencing in a Miniload System

More and more warehouses shift towards the use of automated systems. One such system is a miniload system. In this system, a crane retrieves bins from their storage locations and brings the bins to the picking station, where a worker takes the requested products from the retrieved bins. After a product has been picked, the bin is put back by the crane in its original location. A buffer is present at the picking station to absorb fluctuations in speed between the picker and the crane. The problem of scheduling jobs in this system has received little attention in the literature. We describe system properties and give insight in the performance of a number of heuristics

A compact arc-based ILP formulation for the pickup and delivery problem with divisible pickups and deliveries

We consider the capacitated single vehicle one-to-one pickup and delivery problem with divisible pickups and deliveries (PDPDPD). In this problem, we do not make the standard assumption of one-to-one pickup and delivery problems that each location has only one transportation request. Instead we assume there are multiple requests per location that may be performed individually. This may result in multiple visits to a location. We provide a new compact arc-based ILP formulation for the PDPDPD by deriving time-consistency constraints that identify the order in which selected outgoing arcs from a node are actually traversed. The formulation can also easily be applied to the one-to-one PDP by restricting the number of times that a node can be visited. Numerical results on standard one-to-one PDP test instances from the literature show that our compact formulation is almost competitive with tailor-made solution methods for the one-to-one PDP. Moreover, we observe that significant cost savings up to 15% on average may be obtained by allowing divisible pickups and deliveries in one-to-one PDPs. It turns out that divisible pickups and deliveries are not only beneficial when the vehicle capacity is small, but also when this capacity is unrestrictive

Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies.<br/

Controlling and enabling practices to manage supply in online service triads

Purpose: The purpose of this paper is to understand which controlling and enabling practices are used, how the numerous supplying partners are managed and how positive network effects are generated in online service triads (multi-sided platform – supplying partners – consumers). Design/methodology/approach: A single representative in-depth case study was conducted to refine theory on managing service triads. The main data source consists of field notes collected by one author, who held a temporary position within the organization. Additional data were collected from observations, internal documents, informal talks and 20 interviews. Findings: The authors found controlling and enabling organizational practices in four main categories on two levels as follows: managing network composition (system level), managing order fulfillment and returns (operations level), category management (both levels) and capability enhancement (both levels). Research limitations/implications: The authors show that both controlling and enabling practices are present in online service triads. This enables platform owners and supplying partners to share responsibilities for creating positive network effects, i.e. to increase scale, which increases value, which again attracts more suppliers and consumers, which creates more value, etc. Practical implications: The authors present a range of and controlling and enabling practices that describe how multi-sided platforms can manage numerous supplying partners in an online context. Originality/value: This study is the first to show that contractual and relational governance is insufficient in service triads in online settings with numerous supplying partners. Further, the authors provide empirical evidence that supply networks continuously adapt over time