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The use of wearables in the workplace allows for close monitoring of work processes and might also have consequences for work content and skill requirements. Past research has emphasized the detrimental effects of wearables, particularly those caused by the standardization of work and monitoring of workers. By contrast, this study asks under what conditions the implementation of wearables as part of digital assistance systems is beneficial for workers. Based on recent contributions in the field of labor process theory, this study analyzes the implementation of new technologies using the concepts of the regulatory regime, organizational first-order factors, and workplace second-order choices. The analysis is based on findings from 48 interviews with 83 interviewees in 16 German manufacturing workplaces along with making site visits. It examines the implementation of wearables and the impacts on work content, skills, working conditions, and employment. Besides showing how labor agency affects the implementation of new technologies, the particular contribution of this study lies in analyzing the differences in the implementation of wearables in capital- and labor-intensive organizations. While standardization of work and reduction of work content prevailed in labor-intensive processes, capital-intensive processes were most often characterized by the extension of skill requirements and the risk of work intensification. [ABSTRACT FROM AUTHOR]

This paper highlights the tight relationship between the picking and packing processes in warehouse management and the need to consider them as an integrated problem. The study describes and models this integrated problem as a mixed-integer programming model, to optimise overall labour costs by determining the assignment of the subsets of orders, i.e. batches, for picking and packing. To address the issue of model complexity, the paper presents a statistical-based framework for generating approximate models and selecting the optimal one through examination. Based on the examination results, a pair-swapping heuristic is additionally proposed to be combined as a hybrid algorithm. Numerical experiments based on a real-world case demonstrate the effectiveness of the framework-proposed and selected hybrid algorithm by comparison with other framework-proposed approximate models, a solver, and existing heuristics. Our findings indicate that the combined usage of integrated picking and packing processes planning and the hybrid algorithm proposed and selected within the statistical-based framework can effectively reduce the cost of warehouse management. [ABSTRACT FROM AUTHOR]

Freight transportation, including just-in-time (JIT) supplier networks, accounts for a substantial part of the global carbon dioxide (CO $ _2 $ 2 ) emissions. The JIT truck routing problem (TRP-JIT) presented in the recent literature consists of several suppliers serving a single original equipment manufacturer (OEM). A logistics provider organises the milk-run routes. The shipments are available after their release dates at the suppliers and should be delivered on their due dates at the OEM with minimal total earliness-tardiness penalties (first objective). Unlike previous research on the TRP-JIT, we focus on its environmental impact: (1) We include the weight-distance (second objective), depending on the truck's curb weight, the load, and the transportation distance. (2) We adapt a state-of-the-art large neighbourhood search (LNS) from the literature considering both objectives. (3) The LNS is embedded in bi-criterial frameworks, i.e. ε-constraint and weighted sum methods. Thereby, we estimate Pareto frontiers with at least 60 solutions in less than 25 min for instances with 99 shipments. From a managerial perspective, increasing the difference between the release and due dates for a better JIT performance may worsen the environmental impact. Lighter trucks can reduce the environmental costs without affecting the JIT performance, whereas a smaller fleet negatively affects both objectives. [ABSTRACT FROM AUTHOR]

Drones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the singlecapacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system. [ABSTRACT FROM AUTHOR]

Night deliveries are increasingly used by motor carriers to cut costs, but many carriers struggle to secure night truck drivers because many drivers dislike night driving. Monetary incentives are offered to drivers to accept night loads, but the incentive system currently used in practice is inefficient, as it not only pays more incentives than necessary but also does not incentivize the right drivers, both of which increase costs. This paper develops a new incentive system that allows carriers to secure night drivers at lower costs. Based on the interviews conducted with seven motor carriers, as well as the analysis of large night delivery data, we propose a bi-level programming approach that (1) generates a unique incentive for each driver based on his/her night driving performance, and (2) implicitly aligns the incentive paid to each driver with his/her reservation price. Simulation experiments conducted with real-world data showed that by using our approach carriers can not only avoid over-incentivizing drivers, but also (1) incentive only the right set of drivers who can bring cost savings and (2) encourage other drivers to improve night driving. Results also showed that our approach may give cost savings of up to 2.5% over the current system. [ABSTRACT FROM AUTHOR]

The article presents a response to a study entitled "Competitive Advantages Through Artificial Intelligence: Toward a Theory of Situated AI," published by Ayenda Kemp. The author argues that Kemp's theory regarding the means through which artificial intelligence can promote competitive advantage does not take into account that pursuing AI could likewise have the opposite effect on competitive advantage. The article argues that Kemp's theory does not take into account certain factors, such as the impact AI has on social values, the impact AI has on the nature of the work process itself, and the potential for “algorithmic inertia” whereby the AI may lack the ability to adapt to environmental changes in a dynamic way.

The purpose of this project is to apply computational tools from topological data analysis (TDA) to study logistical systems suchas freight networks. TDA is a relatively nascent research area that allows one to describe geometric properties of a data set, such as connectivity, existence of holes, or clustering, in a way that imposes minimal assumptions on parametric structures like coordinate systems or forms of probability distributions. In recent years, TDA has been successfully applied to many different scientific domains, such as aviation, path planning, and time series analysis. To the best of the author's knowledge, this project will be the first to apply TDA to the logistics domain. View the NCST Project Webpage

The concept of the Physical Internet (PI) has been introduced as a vision for globally efficient and sustainable logistics. Examining the activities of the PI since its foundation, enormous efforts can be recognised in the political and business spheres. Furthermore, the topic has received considerable attention from logistics and supply chain management scientists. In this context, a wide variety of dimensions of the PI concept has been investigated. In contrast to some existing qualitative literature reviews, this study focuses on a quantitative review methodology to provide a holistic overview of the PI concept and its underlying dimensions and interrelations. Based on bibliometric performance analysis and science mapping, this research maps and connects the extant PI literature landscape. In doing so, the most important journals, researchers, and institutions in the PI field are identified. Furthermore, eight research topics are grouped thematically, including PI benefits and the challenge of its operationalisation, carriers and shippers in the PI network, PI foundations, smart product-service systems and services, hyperconnected city logistics, information and communication technology requirements, modular PI containers, and organisation and control of PI hubs. Based on the clustering of existing research, the study concludes by providing directions for future research. [ABSTRACT FROM AUTHOR]

In an era when intricately woven global networks dictate the pulse of commerce, supply chain issues have ascended as a critical focal point--particularly within the medical device industry. The turn [...]

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average. [ABSTRACT FROM AUTHOR]

BACKGROUND: Navigating the logistics and emotional processing of a patients death is an inevitable part of many physicians roles. While research has primarily examined how inpatient clinicians cope with patient loss, little work has explored how primary care clinicians (PCCs) handle patient death in the outpatient setting, and what support resources could help PCCs process loss. OBJECTIVE: To explore PCCs experiences with the logistics and emotional processing of patient deaths and suggestions for supportive resources. DESIGN: Qualitative study using semi-structured interviews conducted between March and May 2023. PARTICIPANTS: Recruitment emails were sent to 136 PCCs (physicians and nurse practitioners) at three San Francisco academic primary care clinics. Twelve clinicians participated in the study. APPROACH: This study used a template analysis approach. Interview transcripts were analyzed in an iterative fashion to identify themes for how PCCs navigate patient death. RESULTS: Participants (n=12) described outpatient death notification as inconsistent, delayed, and rife with uncertainty regarding subsequent actions. They felt various emotions, notably sadness and guilt, especially with deaths of young, vulnerable patients or those from preventable illnesses. Participants identified strategies for emotional processing and recommended improvements including clear procedural guidance, peer debriefings, and formal acknowledgements of deceased patients. CONCLUSIONS: Interviewing PCCs about their experiences following a patient death revealed key themes in logistical and emotional processing, and clinic resource recommendations to better support PCCs. Given the distinct characteristics of primary care-such as enduring patient relationships, greater isolation in ambulatory settings compared to inpatient environments, and rising burnout rates-enhancing guidance and support for PCCs is crucial to mitigate administrative burdens and grief after patient loss.

Abstract: In concurrent work, we introduced a novel robotic package delivery system latching intelligent modular mobility system (LIMMS). Each LIMMS end effector requires a small, lightweight latching mechanism for pre-manufactured containers, such as cardboard boxes. In order to effectively process a high volume of packages, aligning the latching mechanism quickly and reliably is critical. Instead of depending on highly accurate controllers for alignment, we propose a novel self-aligning rotational mechanism to increase the system’s tolerance to misalignment. The radial latching design consists of evenly spaced blades that rotate into slots cut into the box. When misaligned, the blades contact the edges of the engagement slots, generating a self-correcting force that passively centers the blades with the slot pattern. This paper introduces a mathematical framework with closed form expressions to quantify error tolerance for these mechanisms. Through our mathematical and optimization analyses, it is shown that a two-blade design can tolerate a maximum misalignment of three times the radius to the blade tips, much larger than commonly used designs with three or more blade-like contacts. Our approach can be generalized for a class of rotational latching mechanisms with any number of blades. Utilizing this theory, a design process is laid out for developing an optimal self-aligning rotational latching mechanism given desired parameters and task constraints. With this methodology, we designed, manufactured, and verified the effectiveness of both two-blade and three-blade self-aligning in practical experiments.

Amazon seems to be creating a new hybrid model of capitalism combining some elements of classical Fordist vertical integration, or even the over hundred-year-old “Taylorism” of scientific management, with 21st century elements of labor “flexibility” and reliance on gig labor and subcontracting. This hybrid model offers opportunities for organized labor to gain a foothold within some of Amazon’s vertically integrated nodes as the firm lengthens its corporate commodity chain to grow increasingly close to consumers. Building on earlier work on opportunities for, and constraints on, labor in a variety of global commodity chains, our empirical cases examine how Amazon’s corporate strategies may open opportunities for labor in three illustrative cases ensconced in fulfillment centers—the Fordist vertical integration side of the model—in the Inland Empire and Otay Mesa (both in southern California) and Northern Kentucky.

Finishing and coating providers are in high demand in the medical device industry, especially for electropolishing and passivation services. Although they have plenty of work, there are still some supply [...]

Ebola vaccine development has been accelerated in response to large outbreaks in West and Central Africa; those outbreaks have caused >32,000 cases and >13,500 deaths (1). One vaccine emerging from [...]

In warehouses, routing decisions for vehicles are highly related to the schedule of docks and loading operations. To mind the gap that most existing studies consider the routing decisions solely, this study investigates the vehicle routing problem with time windows and loading scheduling (VRPTW-LS). In this problem, the loading schedules at the loading docks and the visiting sequences for vehicles are determined to minimise the total travelled distance. To solve this problem, an adaptive large neighbourhood search algorithm, embedded with a tailored solution representation and an efficient feasibility check mechanism, is developed. In addition, the results of extensive computational experiments verify the effectiveness and efficiency of the proposed algorithm, and some analyses are conducted to obtain managerial insights. [ABSTRACT FROM AUTHOR]

Supply chain visibility (SCV) has been gaining recognition in recent years as a key factor for achieving analytical capabilities and improving supply chain performance. However, levels of SCV implementation lag behind current technological advances. This research was motivated by the lack of visibility in inbound logistics, which limits the possibility of managing deviation, in particular concerning changes in arrival time of incoming goods, in large industrial firms. We addressed this problem by adopting a design science approach. In particular, we followed context–intervention–mechanism–outcome (CIMO) logic to map and analyse material and information flows. The problems areas were successively translated via business and functional requirements into technological solutions. We evaluated alternative technologies using controlled experiments that mimicked real-life situations. This study provides guidance for manufacturing companies aiming to enhance deviation management and predictive capabilities by improving visibility in their inbound logistics and potentially extending visibility to other areas, such as internal and outbound flows. [ABSTRACT FROM AUTHOR]

This paper studies a multiple-deep automated vehicles storage and retrieval system (AVS/RS) rack following a Depth-First storage and a Depth-First relocation strategy. We propose an analytical model based on a novel approach that utilises the Markov chain stochastic steady-state model. To verify the analytical model, a numerical simulation is developed. We also derive an empirical model using first- and second-order polynomial functions that are accurately fitted with regression equations and examined with MAPE and RMSE prediction accuracy measurements from a large-scale simulation study. The empirical model enables a straightforward calculation of the expected number of location movements of shuttle carriers and the attached satellite vehicles from which the AVS/RS throughput performance can be calculated. We present threefold and sixfold deep AVS/RS case study scenarios with an equal number of storage locations and estimate the cycle times. The evaluation of the case study results reveals that the analytical and empirical models achieve less than 2% error in the case of a dual command cycle time prediction compared to the simulation results. This proves that our approach allows an accurate estimation of multiple-depth AVS/RS throughput performance. [ABSTRACT FROM AUTHOR]

Inefficient practices in the logistics industry cause huge losses in social resources. Addressing the quality control issues is challenging in multi-stage transport. Due to the structural characteristics of the serial logistics service chains, the real delivery quality of each Third Party Logistics (3PL) is invisible to the Lead Logistics Provider (LLP), and the defects of a single 3PL's delivery quality will be covered up. Thus, unobservable delivery quality triggers the moral hazard action of 3PL, and strengthens the inequities in a serial logistics service chain. Blockchain technology can be an effective tool to resolve the moral hazard problem. We employ game theory-based models to investigate the blockchain adoption issue in serial logistics service chains. Adopting blockchain technology might bring Pareto improvement in delivery quality and total profit of the service chain. However, 3PL's profit may be hurt because of blockchain adoption. Therefore, successful blockchain implementation depends on whether the initiator of a blockchain programme can properly incentivise 3PLs. [ABSTRACT FROM AUTHOR]