Warehousing has been traditionally viewed as a non value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today's warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of picklists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms. Across the various operations in a warehouse, order-picking is the most time consuming operation in general and accounts for around 55-75% of total warehousing costs (Chiang et al., 2011). Therefore, order-picking has the highest priority for productivity improvement (De Koster et al., 2007). The order-picking operation is particularly important in manual picker-to-parts picking systems 1 , which are the most common ones (Gong and De Koster, 2008) and account for over 80% of all order-picking systems in Western Europe (De Koster et al., 2007). In a picker-to-parts system, orders are firstly batched to form a pick-list. The list then guides the order-picker to travel along the aisles with a picking device (e.g. picking cart or fork-lifter) and collect requested items from designated storage locations (storage racks or bins) (De Koster et al., 2007).Although many studies have been conducted on improving the order-picking operation, managing it efficiently remains complex (Gong and De Koster, 2008). On the demand side, the complexity arises from the introduction of new sales channels such as on-line shopping; and on the supply side, it arises from new operating programs, e.g. JIT, cycle-time reduction (Tompkins, 2010;Davarzani and Norrman, 2015). In such novel business models, customers can place an order by a click of the mouse in their computer, expecting inexpensive, rapid and accurate delivery (De Koster, 2003), i.e. they tend to order more frequently but in smaller quantities asking for more customised service. In response, more companies are inclined to accept late orders which leads to tighter windows for timely deliveries (Gong and De Koster, 2008). Moreover, many logistics companies are replacing small warehouses by fewer but larger warehouses to realise the economies of scale (De Koster et al., 2007). Consequently, the time available for order picking is increasingly shorter. Hence, a fast response is critical for warehouses to operate in such ...

As the role of the customer becomes more important in modern logistics, warehouses are required to improve their response to customer orders. To meet the responsiveness expected by customers, warehouses need to shorten completion times. In this paper, we introduce an interventionist order picking strategy that aims to improve the responsiveness of order picking systems. Unlike existing dynamic strategies, the proposed strategy allows a picker to be intervened during a pick cycle to consider new orders and operational disruptions. An interventionist strategy is compared against an existing dynamic picking strategy via a case study. We report benefits both in terms of order completion time and travel distance. This paper also introduces a set of system requirements for deploying an interventionist strategy based on a second case study.

The role of logistics in effective supply chain management is increasingly critical, and researchers and practitioners have recently focused their attention in designing more intelligent systems to address today's challenges. In this paper, we focus on one such challenge concerning improving the role of the customer in logistics operations. In particular, we identify specific developments in the systems governing core logistics operations, which will enhance the customer experience. This paper proposes a conceptual model for customer orientation in intelligent logistics and describes a number of specific developments the authors are involved in.

Developing suitable catalysts capable of receiving injected electrons and possessing active sites for hydrogen evolution reaction (HER) is the key to building an efficient dyesensitized system for hydrogen production. Fe 3 S 4 is generally regarded as an inferior HER catalyst among the metal sulfide family, mainly due to its weak surface adsorption toward H atoms. In this work, we demonstrate a facile metal−organic frameworkderived method to synthesize uniform Fe 3 S 4 nanorods and active them for HER by Ni doping. Our experimental results and theoretical calculations reveal that Ni doping can greatly modify the electronic structure of Fe 3 S 4 nanorods, improving their electron conductivity and optimizing their surface adsorption energy toward H atoms. Sensitized by a commercial organic dye (eosin-Y), 1%Ni-doped Fe 3 S 4 nanorods display a high H 2 production rate of 3240 μmol g cat −1 h −1 with an apparent quantum yield of 12% under 500 nm wavelength, which is significantly higher than that of pristine Fe 3 S 4 and even higher than that of 1% Pt-deposited Fe 3 S 4 . The working mechanism of this dye-sensitized system is explored, and the effect of Ni-doping concentration has been studied. This work presents a facile strategy to synthesize metal-doped sulfide nanocatalysts with greatly enhanced activity toward photocatalytic H 2 production.

The need for more flexible, adaptable and customer-oriented warehouse operations has been increasingly identified as an important issue by today's warehouse companies due to the rapidly changing preferences of the customers that use their services. Motivated by manufacturing and other logistics operations, in this paper we argue on the potential application of product intelligence in warehouse operations as an approach that can help warehouse companies address these issues. We discuss the opportunities of such an approach using a real example of a third-party-logistics warehouse company and we present the benefits it can bring in their warehouse management systems.