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Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

In this paper, I explore what shapes the identities of digital nomads (DNs), a class of remote workers who travel and work concurrently. Through extensive fieldwork and interviews with 50 digital nomads conducted in seven coworking hostels in Mexico in 2022, I construct a theory of DN identity. I base this upon the frequent transformations they undergo in their Circumstances, which regularly change their worker identity.

DNs relinquish traditional social determinants of identity, such as nationality and religion. They define their personal identities by their passions and interests, which are influenced by the people they meet. DNs exist in inherently transitive social spaces and, without rigid social roles to fulfil, they represent themselves authentically. They form close relationships with other long-term travellers to combat loneliness and homesickness. Digital nomads define their worker identities around their location independence. This study shows that DNs value their nomadic lifestyle above promotions and financial gain. They define themselves by productivity and professionalism to ensure the sustainability of their lifestyle. Furthermore, digital nomad coworking hubs serve focused, individual work, leaving workplace politics and strict ‘office image’ norms behind. Without fixed social and professional roles to play, digital nomads define themselves personally according to their ever-evolving passions and the sustainability of their nomadic life. Based on these findings, I present a cyclical framework for DN identity evolution which demonstrates how relational, logistical, and socio-personal flux evolves DN’s worker identities.

A growing body of research finds that gig economy platforms use gamification to enhance managerial control. Focusing on technologically mediated forms of gamification, this literature reveals how platforms mobilize gig workers’ work effort by making the labour process resemble a game. This chapter contends that this tech-centric scholarship fails to fully capture the historical continuities between contemporary and much older occurrences of game-playing at work. Informed by interviews and participatory observations at two food delivery platforms in Amsterdam, I document how these platforms’ piece wage system gives rise to a workplace dynamic in which severely underpaid delivery couriers continuously employ game strategies to maximize their gig income. Reminiscent of observations from the early shop floor ethnographies of the manufacturing industry, I show that the game of gig income maximization operates as an indirect modality of control by (re)aligning the interests of couriers with the interests of capital and by individualizing and depoliticizing couriers’ overall low wage level. I argue that the new, algorithmic technologies expand and intensify the much older forms of gamified control by infusing the organizational activities of shift and task allocation with the logic of the piece wage game and by increasing the possibilities for interaction, direct feedback and immersion. My study contributes to the literature on gamification in the gig economy by interweaving it with the classic observations derived from the manufacturing industry and by developing a conceptualization of gamification in which both capital and labour exercise agency.

The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

As responsibilities of high school principals continue to expand, their workweeks become longer, and their attention is stretched in multiple directions. How principals from various school types use their time is influenced by their organizational structures and external policies. To gain deeper insights into the workload, priorities and constraints faced by high school principals, this study examines principal time use (PTU) patterns across different school types, including traditional public schools (TPSs), charter schools, Catholic schools and non-Catholic private schools.

Drawing on the national representative data from the High School Longitudinal Study of 2009 in the USA, this study examines and compares PTU in various leadership tasks across different school types, controlling for school and principal characteristics.

Among various high school types, principals in TPS had the longest workweeks. However, the extended workweek did not necessarily result in significantly more hours or a larger proportion of their time dedicated to instructional leadership. Instead, TPS principals allocated more time to administrative tasks and student affairs than principals in other school types.

By examining PTU of different school types, this study adds new evidence on the influence of contextual factors on leadership behavior. It also offers policy implications to enhance principals’ capacities, alleviate their workload and to prioritize time use in different leadership domains.

Women's travel pattern is different from those of men. Women who have both paid employment and unpaid care work have more complex travel patterns. However, land-use policies and urban mobility strategies in the Egyptian context do not consider these differences. This paper analyzes and discusses the travel patterns of the Egyptian working women with children. It examines the difference between men's and women's travel behavior in different income levels. The paper aims at determining the main factors that affect working women's travel patterns within the care economy framework and suggesting recommendations for enhancing women's travel patterns in Greater Cairo Region (GCR).

The methodology relies on conducting a quantitative and qualitative analysis using questionnaires and interviews with working women and men from different social/economic levels in two different workplaces in GCR.

The results demonstrate that income level, workplace locations, schools locations, and schools typologies greatly affect working women's travel patterns in GCR.

The study findings will help urban planners and decision-makers to improve working women's mobility to make their daily trips shorter and more accessible to achieve equitable cities through understanding the conducted affecting factors and considering the suggested recommendations.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.

This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.

The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.

The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.

To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.