Many e-commerce retailers are adding “bricks to clicks” - that is, opening an offline channel in addition to their digital sales channel(s). Taking the perspective of such an online pure player, this research assesses the effects of offline channel additions on the financial performance (e.g., sales, profits) and customer behavior (e.g., basket size, return rate) in the extended channel network as well as the initial online channel of the retailer. Across two studies, one at the zip code level and the other at the customer level, we find that the channel addition of a fashion and lifestyle retailer is synergistic in terms of increasing not only overall sales but also profits. At the same time, the new offline channel does not significantly cannibalize the existing online shop, as new customers are attracted through the channel addition. The effects of channel additions, however, are influenced by characteristics of customers gained before the channel addition and of the trade area around the newly opened stores: among existing customers, those who bought more in the online channel do not react as positively to the addition of an offline channel, and trade areas with socioeconomic characteristics that are often viewed as disadvantageous for digital retailing (e.g., an older population, lower average income) show a stronger positive sales effect of a brick-and-mortar addition. The attractiveness of the offline channel for these customer segments highlights that adding bricks to clicks might be most attractive for those customers who were previously unwilling to purchase from an online-only retailer.

This paper addresses the order- and rack-sequencing problem at a single picking station in the context of robotic mobile fulfillment systems, a warehouse technology typically applied in large distribution centers. Following the parts-to-picker concept, items are stored on movable racks that are lifted and transported by automated guided vehicles from the storage area to picking stations for order-processing. The order-picking process involves two linked decisions: How to sequence the processing of orders and how to sequence the rack visits to supply the picking station with the requested items. We present a novel mixed-integer linear programming formulation achieving stronger linear programming bounds than a previous formulation. Including preprocessing techniques it quickly solves instances of medium-size to proven optimality for the first time in literature. For large real-world instances, we provide a three-stage heuristic solution procedure suitable in a dynamic environment, while providing competitive solutions within a short run time. Computational experiments on a broad set of benchmark instances and a comparative study with approaches from literature verify our results.

A single machine scheduling problem with assignable job due dates to minimize total late work has recently been introduced by Mosheiov, Oron, and Shabtay (2021). The problem was proved NP-hard in the ordinary sense, and no solution algorithm was proposed. In this note, we present two pseudo-polynomial dynamic programming algorithms and an FPTAS for this problem. Besides, we introduce a new single machine scheduling problem to minimize maximum late work of jobs with assignable due dates. We develop an O(n log n) time algorithm for it, where is the number of jobs. An optimal solution value of this new problem is a lower bound for the optimal value of the total late work minimization problem, and it is used in the FPTAS.

Organizational purpose has recently gained great popularity in research and practice. However, the development of this nascent research field has struggled with definitional ambiguity, the lack of a measurement instrument and little empirical testing of potential outcomes. In our paper, we first introduce and define the multidimensional construct of perceived organizational purpose, which sheds light on the individual and subjective experiences of organizational purpose. Second, building on our construct definition, we develop and validate a four-dimensional Perceived Organizational Purpose Scale. Third, we disentangle the related yet differentiated concepts of perceived organizational purpose and meaningful work and theorize how substantial knowledge in the field of meaningful work can be transferred to the relatively new and untested field of perceived organizational purpose. Fourth, we critically elaborate and empirically test the relationship of perceived organizational purpose with employee job satisfaction, subjective wellbeing and work-life conflict.

We introduce the concepts of the players’ second-order productivities in cooperative games with transferable utility (TU games) and of the players’ second-order payoffs for one-point solutions for TU games. Second-order productivities are conceptualized as second-order marginal contributions, that is, how one player affects another player’s marginal contributions to coalitions containing neither of them by entering these coalitions. Second-order payoffs are conceptualized as the effect of one player leaving the game on the payoff of another player. We show that the Shapley value is the unique efficient one-point solution for TU games that reflects the players’ second-order productivities in terms of their second-order payoffs.

The train-to-yard assignment problem (TYAP) pertains to freight consolidation in a large rail transshipment yard—also called a multiple yard—that consists of two sub-yards. Inbound and outbound trains need to be assigned to one or the other sub-yard in a way that minimizes the total railcar switching costs. Each inbound and outbound train is processed in one of the two sub-yards, and time-consuming maneuvers may be necessary for railcars that are supposed to be part of an outbound train leaving from the other sub-yard. A lower number of railcar reassignments between the sub-yards reduce train dwell times and avoid train delays that affect the whole rail network. We develop a matheuristic algorithm with a learning mechanism, which we call MuSt, as well as a branch-and-bound procedure that incorporates elements of constraint propagation. We examine the performance of the developed algorithms through extensive computational experiments. Effective optimization approaches for the TYAP have high practical significance since they may reduce the number of avoidable railcar reassignments, which are resource-blocking, traffic-generating, and expensive, by about 20% compared to current practice, as we illustrate in our computational experiments. Our branch-and-bound algorithm solves problem instances for small or medium railyards in less than a minute or within several hours run time, respectively. The heuristic procedure MuSt finds optimal or nearly optimal solutions within just a couple of minutes, even for large railyards.

This paper treats the Piggyback Transportation Problem: A large vehicle moves successive batches of small vehicles from a depot to a single launching point. Here, the small vehicles depart toward assigned customers, supply shipments, and return to the depot. Once the large vehicle has returned and another batch of small vehicles has been loaded at the depot, the process repeats until all customers are serviced. With autonomous driving on the verge of practical application, this general setting occurs whenever small autonomous delivery vehicles with limited operating range, e.g., unmanned aerial vehicles (drones) or delivery robots, need to be brought in the proximity of the customers by a larger vehicle, e.g., a truck. We aim at the most elementary decision problem in this context, which is inspired by Amazon’s novel last-mile concept, the flying warehouse. According to this concept, drones are launched from a flying warehouse and – after their return to an earthbound depot – are resupplied to the flying warehouse by an air shuttle. We formulate the Piggyback Transportation Problem, investigate its computational complexity, and derive suited solution procedures. From a theoretical perspective, we prove different important structural problem properties. From a practical point of view, we explore the impact of the two main cost drivers, the capacity of the large vehicle and the fleet size of small vehicles, on service quality.

Advertisers have to pay publishers for “viewable” ads, irrespective of whether the users paid active attention. In this paper, we suggest that a granular analysis of users’ viewing patterns can help us to progress beyond mere “viewability” and toward actual differentiation of whether a user has paid attention to an ad or not. To this end, we use individual viewport trajectories, which measures the sequence of locations and times an object (e.g., an ad) is visible on the display of a device (desktop or mobile). To validate our model and benchmark it against the extant models, such as the “viewability” policy (50% threshold) model, we use data from an eye-tracking experiment. Findings confirm the improved model fit, highlight distinct viewing patterns in the data, and inform information processing on mobile phones. Consequently, implications are relevant to publishers, advertisers, and consumer researchers.

We consider single-crane scheduling at rail transshipment yards, in which gantry cranes move containers between trains, trucks and a storage area. The single-crane scheduling problem arises at single-crane transshipment terminals and as a subproblem of the multiple-crane scheduling problem. We consider a makespan objective function, which is equivalent to minimizing the train dwell time in the yard, and introduce time windows for container moves, for example, as a customer service promise. Our proposed decomposition algorithm with integrated dynamic branch-and-cut or dynamic programming solves practically relevant instances within short time limits.

Machine scheduling traditionally is the study of the sequencing of tasks on a single or several parallel or dedicated machines, with possibly different characteristics, subject to a set of constraints. Constraints commonly capture the limited availability of resources, reflect precedence relations between tasks or generally express some restrictions of processing tasks over time. Beyond satisfaction of the constraints, there is generally the goal to optimize an objective as a criterion of the quality of the solution delivered by some search method. There is a vast literature on this area of study, and it has led to numerous sophisticated heuristics, approximate and exact algorithms. Many of these problems are computationally intractable, and problems have been classified by their varying degrees of complexity. The motivation of many problems has come from applications in staff rostering and personnel planning, scheduling in parallel and distributed systems and production planning (see Blazewicz et al. (<a aria-controls="popup-references" aria-expanded="false" role="button" title="View reference" href="https://link.springer.com/article/10.1007/s10951-018-0571-3#CR1">2018</a>)). Over recent years, researchers have started to study scheduling problems that are derived from new applications and settings. These applications include scheduling in decentralized systems and selfish organizations, in sea ports and automotive production plants. Energy-efficient processing, fast data processing and online scheduling are challenging applications. Some of these scheduling problems reflect real-life situations by including results on real industrial datasets. This listing is only a small sample of the many new applications and scheduling problems that researchers are studying. The selected papers not only offer valuable insights on different facets of this current trend, but they also pave the way for future developments. In addition to the challenges related to changing industrial requirements and market conditions, future research studies will need to address challenges posed by emerging technologies. Industry 4.0, also called the Internet of Things, is closely tied with the digitalization of industrial processes and equipment, cyber-physical systems and the capability of real-time big-data processing.